librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

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mci29/sn29_y0m4_duve	mci29	2025-06-19T15:44:22Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-19T15:40:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
tomaarsen/csr-mxbai-embed-large-v1-nq-updated-reconstruction-4	tomaarsen	2025-06-19T15:40:26Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "sparse-encoder", "sparse", "csr", "generated_from_trainer", "dataset_size:99000", "loss:CSRLoss", "loss:SparseMultipleNegativesRankingLoss", "feature-extraction", "en", "dataset:sentence-transformers/natural-questions", "arxiv:1908.10084", "arxiv:2503.01776", "arxiv:1705.00652", "base_model:mixedbread-ai/mxbai-embed-large-v1", "base_model:finetune:mixedbread-ai/mxbai-embed-large-v1", "license:apache-2.0", "model-index", "co2_eq_emissions", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2025-06-19T15:40:18Z	--- language: - en license: apache-2.0 tags: - sentence-transformers - sparse-encoder - sparse - csr - generated_from_trainer - dataset_size:99000 - loss:CSRLoss - loss:SparseMultipleNegativesRankingLoss base_model: mixedbread-ai/mxbai-embed-large-v1 widget: - text: Saudi Arabia–United Arab Emirates relations However, the UAE and Saudi Arabia continue to take somewhat differing stances on regional conflicts such the Yemeni Civil War, where the UAE opposes Al-Islah, and supports the Southern Movement, which has fought against Saudi-backed forces, and the Syrian Civil War, where the UAE has disagreed with Saudi support for Islamist movements.[4] - text: Economy of New Zealand New Zealand's diverse market economy has a sizable service sector, accounting for 63% of all GDP activity in 2013.[17] Large scale manufacturing industries include aluminium production, food processing, metal fabrication, wood and paper products. Mining, manufacturing, electricity, gas, water, and waste services accounted for 16.5% of GDP in 2013.[17] The primary sector continues to dominate New Zealand's exports, despite accounting for 6.5% of GDP in 2013.[17] - text: who was the first president of indian science congress meeting held in kolkata in 1914 - text: Get Over It (Eagles song) "Get Over It" is a song by the Eagles released as a single after a fourteen-year breakup. It was also the first song written by bandmates Don Henley and Glenn Frey when the band reunited. "Get Over It" was played live for the first time during their Hell Freezes Over tour in 1994. It returned the band to the U.S. Top 40 after a fourteen-year absence, peaking at No. 31 on the Billboard Hot 100 chart. It also hit No. 4 on the Billboard Mainstream Rock Tracks chart. The song was not played live by the Eagles after the "Hell Freezes Over" tour in 1994. It remains the group's last Top 40 hit in the U.S. - text: 'Cornelius the Centurion Cornelius (Greek: Κορνήλιος) was a Roman centurion who is considered by Christians to be one of the first Gentiles to convert to the faith, as related in Acts of the Apostles.' datasets: - sentence-transformers/natural-questions pipeline_tag: feature-extraction library_name: sentence-transformers metrics: - dot_accuracy@1 - dot_accuracy@3 - dot_accuracy@5 - dot_accuracy@10 - dot_precision@1 - dot_precision@3 - dot_precision@5 - dot_precision@10 - dot_recall@1 - dot_recall@3 - dot_recall@5 - dot_recall@10 - dot_ndcg@10 - dot_mrr@10 - dot_map@100 - query_active_dims - query_sparsity_ratio - corpus_active_dims - corpus_sparsity_ratio co2_eq_emissions: emissions: 47.46504952064221 energy_consumed: 0.12211166786032028 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K ram_total_size: 31.777088165283203 hours_used: 0.373 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: Sparse CSR model trained on Natural Questions results: - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO 8 type: NanoMSMARCO_8 metrics: - type: dot_accuracy@1 value: 0.16 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.2 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.28 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.4 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.16 name: Dot Precision@1 - type: dot_precision@3 value: 0.06666666666666667 name: Dot Precision@3 - type: dot_precision@5 value: 0.056000000000000015 name: Dot Precision@5 - type: dot_precision@10 value: 0.04 name: Dot Precision@10 - type: dot_recall@1 value: 0.16 name: Dot Recall@1 - type: dot_recall@3 value: 0.2 name: Dot Recall@3 - type: dot_recall@5 value: 0.28 name: Dot Recall@5 - type: dot_recall@10 value: 0.4 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.2553207334684595 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.2125238095238095 name: Dot Mrr@10 - type: dot_map@100 value: 0.2276491742120407 name: Dot Map@100 - type: query_active_dims value: 8.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.998046875 name: Query Sparsity Ratio - type: corpus_active_dims value: 8.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.998046875 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean 8 type: NanoBEIR_mean_8 metrics: - type: dot_accuracy@1 value: 0.16 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.2 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.28 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.4 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.16 name: Dot Precision@1 - type: dot_precision@3 value: 0.06666666666666667 name: Dot Precision@3 - type: dot_precision@5 value: 0.056000000000000015 name: Dot Precision@5 - type: dot_precision@10 value: 0.04 name: Dot Precision@10 - type: dot_recall@1 value: 0.16 name: Dot Recall@1 - type: dot_recall@3 value: 0.2 name: Dot Recall@3 - type: dot_recall@5 value: 0.28 name: Dot Recall@5 - type: dot_recall@10 value: 0.4 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.2553207334684595 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.2125238095238095 name: Dot Mrr@10 - type: dot_map@100 value: 0.2276491742120407 name: Dot Map@100 - type: query_active_dims value: 8.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.998046875 name: Query Sparsity Ratio - type: corpus_active_dims value: 8.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.998046875 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO 16 type: NanoMSMARCO_16 metrics: - type: dot_accuracy@1 value: 0.24 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.38 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.5 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.58 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.24 name: Dot Precision@1 - type: dot_precision@3 value: 0.12666666666666665 name: Dot Precision@3 - type: dot_precision@5 value: 0.1 name: Dot Precision@5 - type: dot_precision@10 value: 0.05800000000000001 name: Dot Precision@10 - type: dot_recall@1 value: 0.24 name: Dot Recall@1 - type: dot_recall@3 value: 0.38 name: Dot Recall@3 - type: dot_recall@5 value: 0.5 name: Dot Recall@5 - type: dot_recall@10 value: 0.58 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.3970913773706993 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.34011111111111114 name: Dot Mrr@10 - type: dot_map@100 value: 0.3530097721306681 name: Dot Map@100 - type: query_active_dims value: 16.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.99609375 name: Query Sparsity Ratio - type: corpus_active_dims value: 16.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.99609375 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean 16 type: NanoBEIR_mean_16 metrics: - type: dot_accuracy@1 value: 0.24 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.38 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.5 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.58 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.24 name: Dot Precision@1 - type: dot_precision@3 value: 0.12666666666666665 name: Dot Precision@3 - type: dot_precision@5 value: 0.1 name: Dot Precision@5 - type: dot_precision@10 value: 0.05800000000000001 name: Dot Precision@10 - type: dot_recall@1 value: 0.24 name: Dot Recall@1 - type: dot_recall@3 value: 0.38 name: Dot Recall@3 - type: dot_recall@5 value: 0.5 name: Dot Recall@5 - type: dot_recall@10 value: 0.58 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.3970913773706993 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.34011111111111114 name: Dot Mrr@10 - type: dot_map@100 value: 0.3530097721306681 name: Dot Map@100 - type: query_active_dims value: 16.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.99609375 name: Query Sparsity Ratio - type: corpus_active_dims value: 16.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.99609375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO 32 type: NanoMSMARCO_32 metrics: - type: dot_accuracy@1 value: 0.3 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.46 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.62 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.7 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.3 name: Dot Precision@1 - type: dot_precision@3 value: 0.15333333333333332 name: Dot Precision@3 - type: dot_precision@5 value: 0.12400000000000003 name: Dot Precision@5 - type: dot_precision@10 value: 0.07 name: Dot Precision@10 - type: dot_recall@1 value: 0.3 name: Dot Recall@1 - type: dot_recall@3 value: 0.46 name: Dot Recall@3 - type: dot_recall@5 value: 0.62 name: Dot Recall@5 - type: dot_recall@10 value: 0.7 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.4872873611978302 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.4205555555555555 name: Dot Mrr@10 - type: dot_map@100 value: 0.43261790702081204 name: Dot Map@100 - type: query_active_dims value: 32.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9921875 name: Query Sparsity Ratio - type: corpus_active_dims value: 32.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9921875 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean 32 type: NanoBEIR_mean_32 metrics: - type: dot_accuracy@1 value: 0.3 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.46 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.62 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.7 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.3 name: Dot Precision@1 - type: dot_precision@3 value: 0.15333333333333332 name: Dot Precision@3 - type: dot_precision@5 value: 0.12400000000000003 name: Dot Precision@5 - type: dot_precision@10 value: 0.07 name: Dot Precision@10 - type: dot_recall@1 value: 0.3 name: Dot Recall@1 - type: dot_recall@3 value: 0.46 name: Dot Recall@3 - type: dot_recall@5 value: 0.62 name: Dot Recall@5 - type: dot_recall@10 value: 0.7 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.4872873611978302 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.4205555555555555 name: Dot Mrr@10 - type: dot_map@100 value: 0.43261790702081204 name: Dot Map@100 - type: query_active_dims value: 32.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9921875 name: Query Sparsity Ratio - type: corpus_active_dims value: 32.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9921875 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO 64 type: NanoMSMARCO_64 metrics: - type: dot_accuracy@1 value: 0.42 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.6 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.68 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.78 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.42 name: Dot Precision@1 - type: dot_precision@3 value: 0.2 name: Dot Precision@3 - type: dot_precision@5 value: 0.136 name: Dot Precision@5 - type: dot_precision@10 value: 0.07800000000000001 name: Dot Precision@10 - type: dot_recall@1 value: 0.42 name: Dot Recall@1 - type: dot_recall@3 value: 0.6 name: Dot Recall@3 - type: dot_recall@5 value: 0.68 name: Dot Recall@5 - type: dot_recall@10 value: 0.78 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.591060924123 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5316666666666666 name: Dot Mrr@10 - type: dot_map@100 value: 0.5405635822735777 name: Dot Map@100 - type: query_active_dims value: 64.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.984375 name: Query Sparsity Ratio - type: corpus_active_dims value: 64.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.984375 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean 64 type: NanoBEIR_mean_64 metrics: - type: dot_accuracy@1 value: 0.42 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.6 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.68 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.78 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.42 name: Dot Precision@1 - type: dot_precision@3 value: 0.2 name: Dot Precision@3 - type: dot_precision@5 value: 0.136 name: Dot Precision@5 - type: dot_precision@10 value: 0.07800000000000001 name: Dot Precision@10 - type: dot_recall@1 value: 0.42 name: Dot Recall@1 - type: dot_recall@3 value: 0.6 name: Dot Recall@3 - type: dot_recall@5 value: 0.68 name: Dot Recall@5 - type: dot_recall@10 value: 0.78 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.591060924123 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5316666666666666 name: Dot Mrr@10 - type: dot_map@100 value: 0.5405635822735777 name: Dot Map@100 - type: query_active_dims value: 64.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.984375 name: Query Sparsity Ratio - type: corpus_active_dims value: 64.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.984375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO 128 type: NanoMSMARCO_128 metrics: - type: dot_accuracy@1 value: 0.36 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.64 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.72 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.82 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.36 name: Dot Precision@1 - type: dot_precision@3 value: 0.21333333333333332 name: Dot Precision@3 - type: dot_precision@5 value: 0.14400000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.08199999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.36 name: Dot Recall@1 - type: dot_recall@3 value: 0.64 name: Dot Recall@3 - type: dot_recall@5 value: 0.72 name: Dot Recall@5 - type: dot_recall@10 value: 0.82 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5877041624403332 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5139126984126984 name: Dot Mrr@10 - type: dot_map@100 value: 0.5216553078498245 name: Dot Map@100 - type: query_active_dims value: 128.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.96875 name: Query Sparsity Ratio - type: corpus_active_dims value: 128.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.96875 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean 128 type: NanoBEIR_mean_128 metrics: - type: dot_accuracy@1 value: 0.36 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.64 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.72 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.82 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.36 name: Dot Precision@1 - type: dot_precision@3 value: 0.21333333333333332 name: Dot Precision@3 - type: dot_precision@5 value: 0.14400000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.08199999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.36 name: Dot Recall@1 - type: dot_recall@3 value: 0.64 name: Dot Recall@3 - type: dot_recall@5 value: 0.72 name: Dot Recall@5 - type: dot_recall@10 value: 0.82 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5877041624403332 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5139126984126984 name: Dot Mrr@10 - type: dot_map@100 value: 0.5216553078498245 name: Dot Map@100 - type: query_active_dims value: 128.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.96875 name: Query Sparsity Ratio - type: corpus_active_dims value: 128.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.96875 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO 256 type: NanoMSMARCO_256 metrics: - type: dot_accuracy@1 value: 0.42 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.64 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.74 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.82 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.42 name: Dot Precision@1 - type: dot_precision@3 value: 0.21333333333333332 name: Dot Precision@3 - type: dot_precision@5 value: 0.14800000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.08199999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.42 name: Dot Recall@1 - type: dot_recall@3 value: 0.64 name: Dot Recall@3 - type: dot_recall@5 value: 0.74 name: Dot Recall@5 - type: dot_recall@10 value: 0.82 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6246741093433497 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5611904761904761 name: Dot Mrr@10 - type: dot_map@100 value: 0.5700740174857822 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean 256 type: NanoBEIR_mean_256 metrics: - type: dot_accuracy@1 value: 0.42 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.64 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.74 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.82 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.42 name: Dot Precision@1 - type: dot_precision@3 value: 0.21333333333333332 name: Dot Precision@3 - type: dot_precision@5 value: 0.14800000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.08199999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.42 name: Dot Recall@1 - type: dot_recall@3 value: 0.64 name: Dot Recall@3 - type: dot_recall@5 value: 0.74 name: Dot Recall@5 - type: dot_recall@10 value: 0.82 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6246741093433497 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5611904761904761 name: Dot Mrr@10 - type: dot_map@100 value: 0.5700740174857822 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoClimateFEVER type: NanoClimateFEVER metrics: - type: dot_accuracy@1 value: 0.36 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.52 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.66 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.8 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.36 name: Dot Precision@1 - type: dot_precision@3 value: 0.2 name: Dot Precision@3 - type: dot_precision@5 value: 0.15600000000000003 name: Dot Precision@5 - type: dot_precision@10 value: 0.11399999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.1573333333333333 name: Dot Recall@1 - type: dot_recall@3 value: 0.24733333333333335 name: Dot Recall@3 - type: dot_recall@5 value: 0.313 name: Dot Recall@5 - type: dot_recall@10 value: 0.43799999999999994 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.35656565827441056 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.479611111111111 name: Dot Mrr@10 - type: dot_map@100 value: 0.27824724841197973 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoDBPedia type: NanoDBPedia metrics: - type: dot_accuracy@1 value: 0.8 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.88 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.92 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.94 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.8 name: Dot Precision@1 - type: dot_precision@3 value: 0.6 name: Dot Precision@3 - type: dot_precision@5 value: 0.5800000000000001 name: Dot Precision@5 - type: dot_precision@10 value: 0.484 name: Dot Precision@10 - type: dot_recall@1 value: 0.09363124545761783 name: Dot Recall@1 - type: dot_recall@3 value: 0.1617934849974966 name: Dot Recall@3 - type: dot_recall@5 value: 0.2269008951554618 name: Dot Recall@5 - type: dot_recall@10 value: 0.33039847394029737 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.607206208169174 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.852 name: Dot Mrr@10 - type: dot_map@100 value: 0.4541106866963296 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoFEVER type: NanoFEVER metrics: - type: dot_accuracy@1 value: 0.9 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.92 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.96 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.96 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.9 name: Dot Precision@1 - type: dot_precision@3 value: 0.32666666666666666 name: Dot Precision@3 - type: dot_precision@5 value: 0.204 name: Dot Precision@5 - type: dot_precision@10 value: 0.102 name: Dot Precision@10 - type: dot_recall@1 value: 0.8466666666666667 name: Dot Recall@1 - type: dot_recall@3 value: 0.8933333333333333 name: Dot Recall@3 - type: dot_recall@5 value: 0.9333333333333332 name: Dot Recall@5 - type: dot_recall@10 value: 0.9333333333333332 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.9080731736277194 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.92 name: Dot Mrr@10 - type: dot_map@100 value: 0.8921016869970377 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoFiQA2018 type: NanoFiQA2018 metrics: - type: dot_accuracy@1 value: 0.56 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.7 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.72 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.72 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.56 name: Dot Precision@1 - type: dot_precision@3 value: 0.31999999999999995 name: Dot Precision@3 - type: dot_precision@5 value: 0.236 name: Dot Precision@5 - type: dot_precision@10 value: 0.13 name: Dot Precision@10 - type: dot_recall@1 value: 0.29924603174603176 name: Dot Recall@1 - type: dot_recall@3 value: 0.46729365079365076 name: Dot Recall@3 - type: dot_recall@5 value: 0.5337301587301587 name: Dot Recall@5 - type: dot_recall@10 value: 0.5473412698412699 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5253203704684166 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6316666666666666 name: Dot Mrr@10 - type: dot_map@100 value: 0.48003870359394873 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoHotpotQA type: NanoHotpotQA metrics: - type: dot_accuracy@1 value: 0.76 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.9 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.94 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.94 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.76 name: Dot Precision@1 - type: dot_precision@3 value: 0.5 name: Dot Precision@3 - type: dot_precision@5 value: 0.316 name: Dot Precision@5 - type: dot_precision@10 value: 0.17199999999999996 name: Dot Precision@10 - type: dot_recall@1 value: 0.38 name: Dot Recall@1 - type: dot_recall@3 value: 0.75 name: Dot Recall@3 - type: dot_recall@5 value: 0.79 name: Dot Recall@5 - type: dot_recall@10 value: 0.86 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.7910580229553633 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.8333333333333333 name: Dot Mrr@10 - type: dot_map@100 value: 0.7410767962182596 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO type: NanoMSMARCO metrics: - type: dot_accuracy@1 value: 0.42 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.64 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.76 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.82 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.42 name: Dot Precision@1 - type: dot_precision@3 value: 0.21333333333333332 name: Dot Precision@3 - type: dot_precision@5 value: 0.15200000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.08199999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.42 name: Dot Recall@1 - type: dot_recall@3 value: 0.64 name: Dot Recall@3 - type: dot_recall@5 value: 0.76 name: Dot Recall@5 - type: dot_recall@10 value: 0.82 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6248295446703863 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5613809523809523 name: Dot Mrr@10 - type: dot_map@100 value: 0.5703445525063172 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNFCorpus type: NanoNFCorpus metrics: - type: dot_accuracy@1 value: 0.44 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.56 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.6 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.74 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.44 name: Dot Precision@1 - type: dot_precision@3 value: 0.3533333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.32 name: Dot Precision@5 - type: dot_precision@10 value: 0.272 name: Dot Precision@10 - type: dot_recall@1 value: 0.03517605061787946 name: Dot Recall@1 - type: dot_recall@3 value: 0.07646787868408336 name: Dot Recall@3 - type: dot_recall@5 value: 0.11598401724221898 name: Dot Recall@5 - type: dot_recall@10 value: 0.15931797747485815 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.33447068554509884 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5147698412698413 name: Dot Mrr@10 - type: dot_map@100 value: 0.15438429278142912 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNQ type: NanoNQ metrics: - type: dot_accuracy@1 value: 0.5 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.72 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.76 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.84 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.5 name: Dot Precision@1 - type: dot_precision@3 value: 0.24666666666666667 name: Dot Precision@3 - type: dot_precision@5 value: 0.16 name: Dot Precision@5 - type: dot_precision@10 value: 0.088 name: Dot Precision@10 - type: dot_recall@1 value: 0.48 name: Dot Recall@1 - type: dot_recall@3 value: 0.67 name: Dot Recall@3 - type: dot_recall@5 value: 0.72 name: Dot Recall@5 - type: dot_recall@10 value: 0.79 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6479593376479322 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6163333333333333 name: Dot Mrr@10 - type: dot_map@100 value: 0.6035174820443362 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoQuoraRetrieval type: NanoQuoraRetrieval metrics: - type: dot_accuracy@1 value: 0.92 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.96 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 1.0 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 1.0 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.92 name: Dot Precision@1 - type: dot_precision@3 value: 0.3999999999999999 name: Dot Precision@3 - type: dot_precision@5 value: 0.26799999999999996 name: Dot Precision@5 - type: dot_precision@10 value: 0.13799999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.7973333333333332 name: Dot Recall@1 - type: dot_recall@3 value: 0.922 name: Dot Recall@3 - type: dot_recall@5 value: 0.9893333333333334 name: Dot Recall@5 - type: dot_recall@10 value: 0.996 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.9493554410777213 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.9456666666666667 name: Dot Mrr@10 - type: dot_map@100 value: 0.9286237373737373 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoSCIDOCS type: NanoSCIDOCS metrics: - type: dot_accuracy@1 value: 0.56 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.76 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.78 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.88 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.56 name: Dot Precision@1 - type: dot_precision@3 value: 0.4 name: Dot Precision@3 - type: dot_precision@5 value: 0.29200000000000004 name: Dot Precision@5 - type: dot_precision@10 value: 0.20999999999999996 name: Dot Precision@10 - type: dot_recall@1 value: 0.11866666666666668 name: Dot Recall@1 - type: dot_recall@3 value: 0.24966666666666665 name: Dot Recall@3 - type: dot_recall@5 value: 0.30266666666666675 name: Dot Recall@5 - type: dot_recall@10 value: 0.4316666666666666 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.4265505670611979 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6682142857142856 name: Dot Mrr@10 - type: dot_map@100 value: 0.3385559757581844 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoArguAna type: NanoArguAna metrics: - type: dot_accuracy@1 value: 0.36 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.78 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.84 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.94 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.36 name: Dot Precision@1 - type: dot_precision@3 value: 0.26 name: Dot Precision@3 - type: dot_precision@5 value: 0.16799999999999998 name: Dot Precision@5 - type: dot_precision@10 value: 0.09399999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.36 name: Dot Recall@1 - type: dot_recall@3 value: 0.78 name: Dot Recall@3 - type: dot_recall@5 value: 0.84 name: Dot Recall@5 - type: dot_recall@10 value: 0.94 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6674878961390456 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5782460317460317 name: Dot Mrr@10 - type: dot_map@100 value: 0.5802628384687207 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoSciFact type: NanoSciFact metrics: - type: dot_accuracy@1 value: 0.7 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.8 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.82 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.88 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.7 name: Dot Precision@1 - type: dot_precision@3 value: 0.2866666666666667 name: Dot Precision@3 - type: dot_precision@5 value: 0.184 name: Dot Precision@5 - type: dot_precision@10 value: 0.1 name: Dot Precision@10 - type: dot_recall@1 value: 0.665 name: Dot Recall@1 - type: dot_recall@3 value: 0.79 name: Dot Recall@3 - type: dot_recall@5 value: 0.81 name: Dot Recall@5 - type: dot_recall@10 value: 0.88 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.7776207541845983 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.7519444444444445 name: Dot Mrr@10 - type: dot_map@100 value: 0.742050969601677 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoTouche2020 type: NanoTouche2020 metrics: - type: dot_accuracy@1 value: 0.5306122448979592 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.8367346938775511 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.8979591836734694 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.9795918367346939 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.5306122448979592 name: Dot Precision@1 - type: dot_precision@3 value: 0.5306122448979591 name: Dot Precision@3 - type: dot_precision@5 value: 0.5142857142857142 name: Dot Precision@5 - type: dot_precision@10 value: 0.43469387755102035 name: Dot Precision@10 - type: dot_recall@1 value: 0.03672756127909814 name: Dot Recall@1 - type: dot_recall@3 value: 0.11122615754561782 name: Dot Recall@3 - type: dot_recall@5 value: 0.17495428374251296 name: Dot Recall@5 - type: dot_recall@10 value: 0.28731694149491666 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.47801832046439025 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.7052073210236476 name: Dot Mrr@10 - type: dot_map@100 value: 0.3658602219028105 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean type: NanoBEIR_mean metrics: - type: dot_accuracy@1 value: 0.6008163265306123 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.7674411302982732 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.8198430141287284 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.8799686028257457 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.6008163265306123 name: Dot Precision@1 - type: dot_precision@3 value: 0.3567137624280482 name: Dot Precision@3 - type: dot_precision@5 value: 0.2730989010989011 name: Dot Precision@5 - type: dot_precision@10 value: 0.18620722135007847 name: Dot Precision@10 - type: dot_recall@1 value: 0.3607523760846636 name: Dot Recall@1 - type: dot_recall@3 value: 0.5199318850272447 name: Dot Recall@3 - type: dot_recall@5 value: 0.5776848221695142 name: Dot Recall@5 - type: dot_recall@10 value: 0.6471826663654878 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6226550754065734 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6967979990531011 name: Dot Mrr@10 - type: dot_map@100 value: 0.5483980917195976 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio --- # Sparse CSR model trained on Natural Questions This is a [CSR Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) on the [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) dataset using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 4096-dimensional sparse vector space with 256 maximum active dimensions and can be used for semantic search and sparse retrieval. ## Model Details ### Model Description - Model Type: CSR Sparse Encoder - Base model: [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) <!-- at revision db9d1fe0f31addb4978201b2bf3e577f3f8900d2 --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 4096 dimensions (trained with 256 maximum active dimensions) - Similarity Function: Dot Product - Training Dataset: - [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) - Language: en - License: apache-2.0 ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Documentation: [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder) ### Full Model Architecture ``` SparseEncoder( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): CSRSparsity({'input_dim': 1024, 'hidden_dim': 4096, 'k': 256, 'k_aux': 512, 'normalize': False, 'dead_threshold': 30}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SparseEncoder # Download from the 🤗 Hub model = SparseEncoder("tomaarsen/csr-mxbai-embed-large-v1-nq-updated-reconstruction-4") # Run inference queries = [ "who is cornelius in the book of acts", ] documents = [ 'Cornelius the Centurion Cornelius (Greek: Κορνήλιος) was a Roman centurion who is considered by Christians to be one of the first Gentiles to convert to the faith, as related in Acts of the Apostles.', "Joe Ranft Ranft reunited with Lasseter when he was hired by Pixar in 1991 as their head of story.[1] There he worked on all of their films produced up to 2006; this included Toy Story (for which he received an Academy Award nomination) and A Bug's Life, as the co-story writer and others as story supervisor. His final film was Cars. He also voiced characters in many of the films, including Heimlich the caterpillar in A Bug's Life, Wheezy the penguin in Toy Story 2, and Jacques the shrimp in Finding Nemo.[1]", 'Wonderful Tonight "Wonderful Tonight" is a ballad written by Eric Clapton. It was included on Clapton\'s 1977 album Slowhand. Clapton wrote the song about Pattie Boyd.[1] The female vocal harmonies on the song are provided by Marcella Detroit (then Marcy Levy) and Yvonne Elliman.', ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) print(query_embeddings.shape, document_embeddings.shape) # [1, 4096] [3, 4096] # Get the similarity scores for the embeddings similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) # tensor([[111.0676, 23.1031, 22.6751]]) ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Sparse Information Retrieval * Dataset: `NanoMSMARCO_8` * Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters: ```json { "max_active_dims": 8 } ``` \| Metric \| Value \| \|:----------------------\|:-----------\| \| dot_accuracy@1 \| 0.16 \| \| dot_accuracy@3 \| 0.2 \| \| dot_accuracy@5 \| 0.28 \| \| dot_accuracy@10 \| 0.4 \| \| dot_precision@1 \| 0.16 \| \| dot_precision@3 \| 0.0667 \| \| dot_precision@5 \| 0.056 \| \| dot_precision@10 \| 0.04 \| \| dot_recall@1 \| 0.16 \| \| dot_recall@3 \| 0.2 \| \| dot_recall@5 \| 0.28 \| \| dot_recall@10 \| 0.4 \| \| dot_ndcg@10 \| 0.2553 \| \| dot_mrr@10 \| 0.2125 \| \| dot_map@100 \| 0.2276 \| \| query_active_dims \| 8.0 \| \| query_sparsity_ratio \| 0.998 \| \| corpus_active_dims \| 8.0 \| \| corpus_sparsity_ratio \| 0.998 \| #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean_8` * Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco" ], "max_active_dims": 8 } ``` \| Metric \| Value \| \|:----------------------\|:-----------\| \| dot_accuracy@1 \| 0.16 \| \| dot_accuracy@3 \| 0.2 \| \| dot_accuracy@5 \| 0.28 \| \| dot_accuracy@10 \| 0.4 \| \| dot_precision@1 \| 0.16 \| \| dot_precision@3 \| 0.0667 \| \| dot_precision@5 \| 0.056 \| \| dot_precision@10 \| 0.04 \| \| dot_recall@1 \| 0.16 \| \| dot_recall@3 \| 0.2 \| \| dot_recall@5 \| 0.28 \| \| dot_recall@10 \| 0.4 \| \| dot_ndcg@10 \| 0.2553 \| \| dot_mrr@10 \| 0.2125 \| \| dot_map@100 \| 0.2276 \| \| query_active_dims \| 8.0 \| \| query_sparsity_ratio \| 0.998 \| \| corpus_active_dims \| 8.0 \| \| corpus_sparsity_ratio \| 0.998 \| #### Sparse Information Retrieval * Dataset: `NanoMSMARCO_16` * Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters: ```json { "max_active_dims": 16 } ``` \| Metric \| Value \| \|:----------------------\|:-----------\| \| dot_accuracy@1 \| 0.24 \| \| dot_accuracy@3 \| 0.38 \| \| dot_accuracy@5 \| 0.5 \| \| dot_accuracy@10 \| 0.58 \| \| dot_precision@1 \| 0.24 \| \| dot_precision@3 \| 0.1267 \| \| dot_precision@5 \| 0.1 \| \| dot_precision@10 \| 0.058 \| \| dot_recall@1 \| 0.24 \| \| dot_recall@3 \| 0.38 \| \| dot_recall@5 \| 0.5 \| \| dot_recall@10 \| 0.58 \| \| dot_ndcg@10 \| 0.3971 \| \| dot_mrr@10 \| 0.3401 \| \| dot_map@100 \| 0.353 \| \| query_active_dims \| 16.0 \| \| query_sparsity_ratio \| 0.9961 \| \| corpus_active_dims \| 16.0 \| \| corpus_sparsity_ratio \| 0.9961 \| #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean_16` * Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco" ], "max_active_dims": 16 } ``` \| Metric \| Value \| \|:----------------------\|:-----------\| \| dot_accuracy@1 \| 0.24 \| \| dot_accuracy@3 \| 0.38 \| \| dot_accuracy@5 \| 0.5 \| \| dot_accuracy@10 \| 0.58 \| \| dot_precision@1 \| 0.24 \| \| dot_precision@3 \| 0.1267 \| \| dot_precision@5 \| 0.1 \| \| dot_precision@10 \| 0.058 \| \| dot_recall@1 \| 0.24 \| \| dot_recall@3 \| 0.38 \| \| dot_recall@5 \| 0.5 \| \| dot_recall@10 \| 0.58 \| \| dot_ndcg@10 \| 0.3971 \| \| dot_mrr@10 \| 0.3401 \| \| dot_map@100 \| 0.353 \| \| query_active_dims \| 16.0 \| \| query_sparsity_ratio \| 0.9961 \| \| corpus_active_dims \| 16.0 \| \| corpus_sparsity_ratio \| 0.9961 \| #### Sparse Information Retrieval * Dataset: `NanoMSMARCO_32` * Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters: ```json { "max_active_dims": 32 } ``` \| Metric \| Value \| \|:----------------------\|:-----------\| \| dot_accuracy@1 \| 0.3 \| \| dot_accuracy@3 \| 0.46 \| \| dot_accuracy@5 \| 0.62 \| \| dot_accuracy@10 \| 0.7 \| \| dot_precision@1 \| 0.3 \| \| dot_precision@3 \| 0.1533 \| \| dot_precision@5 \| 0.124 \| \| dot_precision@10 \| 0.07 \| \| dot_recall@1 \| 0.3 \| \| dot_recall@3 \| 0.46 \| \| dot_recall@5 \| 0.62 \| \| dot_recall@10 \| 0.7 \| \| dot_ndcg@10 \| 0.4873 \| \| dot_mrr@10 \| 0.4206 \| \| dot_map@100 \| 0.4326 \| \| query_active_dims \| 32.0 \| \| query_sparsity_ratio \| 0.9922 \| \| corpus_active_dims \| 32.0 \| \| corpus_sparsity_ratio \| 0.9922 \| #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean_32` * Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco" ], "max_active_dims": 32 } ``` \| Metric \| Value \| \|:----------------------\|:-----------\| \| dot_accuracy@1 \| 0.3 \| \| dot_accuracy@3 \| 0.46 \| \| dot_accuracy@5 \| 0.62 \| \| dot_accuracy@10 \| 0.7 \| \| dot_precision@1 \| 0.3 \| \| dot_precision@3 \| 0.1533 \| \| dot_precision@5 \| 0.124 \| \| dot_precision@10 \| 0.07 \| \| dot_recall@1 \| 0.3 \| \| dot_recall@3 \| 0.46 \| \| dot_recall@5 \| 0.62 \| \| dot_recall@10 \| 0.7 \| \| dot_ndcg@10 \| 0.4873 \| \| dot_mrr@10 \| 0.4206 \| \| dot_map@100 \| 0.4326 \| \| query_active_dims \| 32.0 \| \| query_sparsity_ratio \| 0.9922 \| \| corpus_active_dims \| 32.0 \| \| corpus_sparsity_ratio \| 0.9922 \| #### Sparse Information Retrieval * Dataset: `NanoMSMARCO_64` * Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters: ```json { "max_active_dims": 64 } ``` \| Metric \| Value \| \|:----------------------\|:-----------\| \| dot_accuracy@1 \| 0.42 \| \| dot_accuracy@3 \| 0.6 \| \| dot_accuracy@5 \| 0.68 \| \| dot_accuracy@10 \| 0.78 \| \| dot_precision@1 \| 0.42 \| \| dot_precision@3 \| 0.2 \| \| dot_precision@5 \| 0.136 \| \| dot_precision@10 \| 0.078 \| \| dot_recall@1 \| 0.42 \| \| dot_recall@3 \| 0.6 \| \| dot_recall@5 \| 0.68 \| \| dot_recall@10 \| 0.78 \| \| dot_ndcg@10 \| 0.5911 \| \| dot_mrr@10 \| 0.5317 \| \| dot_map@100 \| 0.5406 \| \| query_active_dims \| 64.0 \| \| query_sparsity_ratio \| 0.9844 \| \| corpus_active_dims \| 64.0 \| \| corpus_sparsity_ratio \| 0.9844 \| #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean_64` * Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco" ], "max_active_dims": 64 } ``` \| Metric \| Value \| \|:----------------------\|:-----------\| \| dot_accuracy@1 \| 0.42 \| \| dot_accuracy@3 \| 0.6 \| \| dot_accuracy@5 \| 0.68 \| \| dot_accuracy@10 \| 0.78 \| \| dot_precision@1 \| 0.42 \| \| dot_precision@3 \| 0.2 \| \| dot_precision@5 \| 0.136 \| \| dot_precision@10 \| 0.078 \| \| dot_recall@1 \| 0.42 \| \| dot_recall@3 \| 0.6 \| \| dot_recall@5 \| 0.68 \| \| dot_recall@10 \| 0.78 \| \| dot_ndcg@10 \| 0.5911 \| \| dot_mrr@10 \| 0.5317 \| \| dot_map@100 \| 0.5406 \| \| query_active_dims \| 64.0 \| \| query_sparsity_ratio \| 0.9844 \| \| corpus_active_dims \| 64.0 \| \| corpus_sparsity_ratio \| 0.9844 \| #### Sparse Information Retrieval * Dataset: `NanoMSMARCO_128` * Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters: ```json { "max_active_dims": 128 } ``` \| Metric \| Value \| \|:----------------------\|:-----------\| \| dot_accuracy@1 \| 0.36 \| \| dot_accuracy@3 \| 0.64 \| \| dot_accuracy@5 \| 0.72 \| \| dot_accuracy@10 \| 0.82 \| \| dot_precision@1 \| 0.36 \| \| dot_precision@3 \| 0.2133 \| \| dot_precision@5 \| 0.144 \| \| dot_precision@10 \| 0.082 \| \| dot_recall@1 \| 0.36 \| \| dot_recall@3 \| 0.64 \| \| dot_recall@5 \| 0.72 \| \| dot_recall@10 \| 0.82 \| \| dot_ndcg@10 \| 0.5877 \| \| dot_mrr@10 \| 0.5139 \| \| dot_map@100 \| 0.5217 \| \| query_active_dims \| 128.0 \| \| query_sparsity_ratio \| 0.9688 \| \| corpus_active_dims \| 128.0 \| \| corpus_sparsity_ratio \| 0.9688 \| #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean_128` * Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco" ], "max_active_dims": 128 } ``` \| Metric \| Value \| \|:----------------------\|:-----------\| \| dot_accuracy@1 \| 0.36 \| \| dot_accuracy@3 \| 0.64 \| \| dot_accuracy@5 \| 0.72 \| \| dot_accuracy@10 \| 0.82 \| \| dot_precision@1 \| 0.36 \| \| dot_precision@3 \| 0.2133 \| \| dot_precision@5 \| 0.144 \| \| dot_precision@10 \| 0.082 \| \| dot_recall@1 \| 0.36 \| \| dot_recall@3 \| 0.64 \| \| dot_recall@5 \| 0.72 \| \| dot_recall@10 \| 0.82 \| \| dot_ndcg@10 \| 0.5877 \| \| dot_mrr@10 \| 0.5139 \| \| dot_map@100 \| 0.5217 \| \| query_active_dims \| 128.0 \| \| query_sparsity_ratio \| 0.9688 \| \| corpus_active_dims \| 128.0 \| \| corpus_sparsity_ratio \| 0.9688 \| #### Sparse Information Retrieval * Dataset: `NanoMSMARCO_256` * Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters: ```json { "max_active_dims": 256 } ``` \| Metric \| Value \| \|:----------------------\|:-----------\| \| dot_accuracy@1 \| 0.42 \| \| dot_accuracy@3 \| 0.64 \| \| dot_accuracy@5 \| 0.74 \| \| dot_accuracy@10 \| 0.82 \| \| dot_precision@1 \| 0.42 \| \| dot_precision@3 \| 0.2133 \| \| dot_precision@5 \| 0.148 \| \| dot_precision@10 \| 0.082 \| \| dot_recall@1 \| 0.42 \| \| dot_recall@3 \| 0.64 \| \| dot_recall@5 \| 0.74 \| \| dot_recall@10 \| 0.82 \| \| dot_ndcg@10 \| 0.6247 \| \| dot_mrr@10 \| 0.5612 \| \| dot_map@100 \| 0.5701 \| \| query_active_dims \| 256.0 \| \| query_sparsity_ratio \| 0.9375 \| \| corpus_active_dims \| 256.0 \| \| corpus_sparsity_ratio \| 0.9375 \| #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean_256` * Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco" ], "max_active_dims": 256 } ``` \| Metric \| Value \| \|:----------------------\|:-----------\| \| dot_accuracy@1 \| 0.42 \| \| dot_accuracy@3 \| 0.64 \| \| dot_accuracy@5 \| 0.74 \| \| dot_accuracy@10 \| 0.82 \| \| dot_precision@1 \| 0.42 \| \| dot_precision@3 \| 0.2133 \| \| dot_precision@5 \| 0.148 \| \| dot_precision@10 \| 0.082 \| \| dot_recall@1 \| 0.42 \| \| dot_recall@3 \| 0.64 \| \| dot_recall@5 \| 0.74 \| \| dot_recall@10 \| 0.82 \| \| dot_ndcg@10 \| 0.6247 \| \| dot_mrr@10 \| 0.5612 \| \| dot_map@100 \| 0.5701 \| \| query_active_dims \| 256.0 \| \| query_sparsity_ratio \| 0.9375 \| \| corpus_active_dims \| 256.0 \| \| corpus_sparsity_ratio \| 0.9375 \| #### Sparse Information Retrieval * Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020` * Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) \| Metric \| NanoClimateFEVER \| NanoDBPedia \| NanoFEVER \| NanoFiQA2018 \| NanoHotpotQA \| NanoMSMARCO \| NanoNFCorpus \| NanoNQ \| NanoQuoraRetrieval \| NanoSCIDOCS \| NanoArguAna \| NanoSciFact \| NanoTouche2020 \| \|:----------------------\|:-----------------\|:------------\|:-----------\|:-------------\|:-------------\|:------------\|:-------------\|:----------\|:-------------------\|:------------\|:------------\|:------------\|:---------------\| \| dot_accuracy@1 \| 0.36 \| 0.8 \| 0.9 \| 0.56 \| 0.76 \| 0.42 \| 0.44 \| 0.5 \| 0.92 \| 0.56 \| 0.36 \| 0.7 \| 0.5306 \| \| dot_accuracy@3 \| 0.52 \| 0.88 \| 0.92 \| 0.7 \| 0.9 \| 0.64 \| 0.56 \| 0.72 \| 0.96 \| 0.76 \| 0.78 \| 0.8 \| 0.8367 \| \| dot_accuracy@5 \| 0.66 \| 0.92 \| 0.96 \| 0.72 \| 0.94 \| 0.76 \| 0.6 \| 0.76 \| 1.0 \| 0.78 \| 0.84 \| 0.82 \| 0.898 \| \| dot_accuracy@10 \| 0.8 \| 0.94 \| 0.96 \| 0.72 \| 0.94 \| 0.82 \| 0.74 \| 0.84 \| 1.0 \| 0.88 \| 0.94 \| 0.88 \| 0.9796 \| \| dot_precision@1 \| 0.36 \| 0.8 \| 0.9 \| 0.56 \| 0.76 \| 0.42 \| 0.44 \| 0.5 \| 0.92 \| 0.56 \| 0.36 \| 0.7 \| 0.5306 \| \| dot_precision@3 \| 0.2 \| 0.6 \| 0.3267 \| 0.32 \| 0.5 \| 0.2133 \| 0.3533 \| 0.2467 \| 0.4 \| 0.4 \| 0.26 \| 0.2867 \| 0.5306 \| \| dot_precision@5 \| 0.156 \| 0.58 \| 0.204 \| 0.236 \| 0.316 \| 0.152 \| 0.32 \| 0.16 \| 0.268 \| 0.292 \| 0.168 \| 0.184 \| 0.5143 \| \| dot_precision@10 \| 0.114 \| 0.484 \| 0.102 \| 0.13 \| 0.172 \| 0.082 \| 0.272 \| 0.088 \| 0.138 \| 0.21 \| 0.094 \| 0.1 \| 0.4347 \| \| dot_recall@1 \| 0.1573 \| 0.0936 \| 0.8467 \| 0.2992 \| 0.38 \| 0.42 \| 0.0352 \| 0.48 \| 0.7973 \| 0.1187 \| 0.36 \| 0.665 \| 0.0367 \| \| dot_recall@3 \| 0.2473 \| 0.1618 \| 0.8933 \| 0.4673 \| 0.75 \| 0.64 \| 0.0765 \| 0.67 \| 0.922 \| 0.2497 \| 0.78 \| 0.79 \| 0.1112 \| \| dot_recall@5 \| 0.313 \| 0.2269 \| 0.9333 \| 0.5337 \| 0.79 \| 0.76 \| 0.116 \| 0.72 \| 0.9893 \| 0.3027 \| 0.84 \| 0.81 \| 0.175 \| \| dot_recall@10 \| 0.438 \| 0.3304 \| 0.9333 \| 0.5473 \| 0.86 \| 0.82 \| 0.1593 \| 0.79 \| 0.996 \| 0.4317 \| 0.94 \| 0.88 \| 0.2873 \| \| dot_ndcg@10 \| 0.3566 \| 0.6072 \| 0.9081 \| 0.5253 \| 0.7911 \| 0.6248 \| 0.3345 \| 0.648 \| 0.9494 \| 0.4266 \| 0.6675 \| 0.7776 \| 0.478 \| \| dot_mrr@10 \| 0.4796 \| 0.852 \| 0.92 \| 0.6317 \| 0.8333 \| 0.5614 \| 0.5148 \| 0.6163 \| 0.9457 \| 0.6682 \| 0.5782 \| 0.7519 \| 0.7052 \| \| dot_map@100 \| 0.2782 \| 0.4541 \| 0.8921 \| 0.48 \| 0.7411 \| 0.5703 \| 0.1544 \| 0.6035 \| 0.9286 \| 0.3386 \| 0.5803 \| 0.7421 \| 0.3659 \| \| query_active_dims \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| \| query_sparsity_ratio \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| \| corpus_active_dims \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| \| corpus_sparsity_ratio \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean` * Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "climatefever", "dbpedia", "fever", "fiqa2018", "hotpotqa", "msmarco", "nfcorpus", "nq", "quoraretrieval", "scidocs", "arguana", "scifact", "touche2020" ] } ``` \| Metric \| Value \| \|:----------------------\|:-----------\| \| dot_accuracy@1 \| 0.6008 \| \| dot_accuracy@3 \| 0.7674 \| \| dot_accuracy@5 \| 0.8198 \| \| dot_accuracy@10 \| 0.88 \| \| dot_precision@1 \| 0.6008 \| \| dot_precision@3 \| 0.3567 \| \| dot_precision@5 \| 0.2731 \| \| dot_precision@10 \| 0.1862 \| \| dot_recall@1 \| 0.3608 \| \| dot_recall@3 \| 0.5199 \| \| dot_recall@5 \| 0.5777 \| \| dot_recall@10 \| 0.6472 \| \| dot_ndcg@10 \| 0.6227 \| \| dot_mrr@10 \| 0.6968 \| \| dot_map@100 \| 0.5484 \| \| query_active_dims \| 256.0 \| \| query_sparsity_ratio \| 0.9375 \| \| corpus_active_dims \| 256.0 \| \| corpus_sparsity_ratio \| 0.9375 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### natural-questions * Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) * Size: 99,000 training samples * Columns: <code>query</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: \| \| query \| answer \| \|:--------\|:-----------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 10 tokens</li><li>mean: 11.71 tokens</li><li>max: 26 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 131.81 tokens</li><li>max: 450 tokens</li></ul> \| * Samples: \| query \| answer \| \|:--------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>who played the father in papa don't preach</code> \| <code>Alex McArthur Alex McArthur (born March 6, 1957) is an American actor.</code> \| \| <code>where was the location of the battle of hastings</code> \| <code>Battle of Hastings The Battle of Hastings[a] was fought on 14 October 1066 between the Norman-French army of William, the Duke of Normandy, and an English army under the Anglo-Saxon King Harold Godwinson, beginning the Norman conquest of England. It took place approximately 7 miles (11 kilometres) northwest of Hastings, close to the present-day town of Battle, East Sussex, and was a decisive Norman victory.</code> \| \| <code>how many puppies can a dog give birth to</code> \| <code>Canine reproduction The largest litter size to date was set by a Neapolitan Mastiff in Manea, Cambridgeshire, UK on November 29, 2004; the litter was 24 puppies.[22]</code> \| * Loss: [<code>CSRLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#csrloss) with these parameters: ```json { "beta": 0.1, "gamma": 3.0, "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')" } ``` ### Evaluation Dataset #### natural-questions * Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) * Size: 1,000 evaluation samples * Columns: <code>query</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: \| \| query \| answer \| \|:--------\|:-----------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 10 tokens</li><li>mean: 11.69 tokens</li><li>max: 23 tokens</li></ul> \| <ul><li>min: 15 tokens</li><li>mean: 134.01 tokens</li><li>max: 512 tokens</li></ul> \| * Samples: \| query \| answer \| \|:-------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>where is the tiber river located in italy</code> \| <code>Tiber The Tiber (/ˈtaɪbər/, Latin: Tiberis,[1] Italian: Tevere [ˈteːvere])[2] is the third-longest river in Italy, rising in the Apennine Mountains in Emilia-Romagna and flowing 406 kilometres (252 mi) through Tuscany, Umbria and Lazio, where it is joined by the river Aniene, to the Tyrrhenian Sea, between Ostia and Fiumicino.[3] It drains a basin estimated at 17,375 square kilometres (6,709 sq mi). The river has achieved lasting fame as the main watercourse of the city of Rome, founded on its eastern banks.</code> \| \| <code>what kind of car does jay gatsby drive</code> \| <code>Jay Gatsby At the Buchanan home, Jordan Baker, Nick, Jay, and the Buchanans decide to visit New York City. Tom borrows Gatsby's yellow Rolls Royce to drive up to the city. On the way to New York City, Tom makes a detour at a gas station in "the Valley of Ashes", a run-down part of Long Island. The owner, George Wilson, shares his concern that his wife, Myrtle, may be having an affair. This unnerves Tom, who has been having an affair with Myrtle, and he leaves in a hurry.</code> \| \| <code>who sings if i can dream about you</code> \| <code>I Can Dream About You "I Can Dream About You" is a song performed by American singer Dan Hartman on the soundtrack album of the film Streets of Fire. Released in 1984 as a single from the soundtrack, and included on Hartman's album I Can Dream About You, it reached number 6 on the Billboard Hot 100.[1]</code> \| * Loss: [<code>CSRLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#csrloss) with these parameters: ```json { "beta": 0.1, "gamma": 3.0, "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `learning_rate`: 4e-05 - `num_train_epochs`: 1 - `bf16`: True - `load_best_model_at_end`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 4e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {} </details> ### Training Logs \| Epoch \| Step \| Training Loss \| Validation Loss \| NanoMSMARCO_8_dot_ndcg@10 \| NanoBEIR_mean_8_dot_ndcg@10 \| NanoMSMARCO_16_dot_ndcg@10 \| NanoBEIR_mean_16_dot_ndcg@10 \| NanoMSMARCO_32_dot_ndcg@10 \| NanoBEIR_mean_32_dot_ndcg@10 \| NanoMSMARCO_64_dot_ndcg@10 \| NanoBEIR_mean_64_dot_ndcg@10 \| NanoMSMARCO_128_dot_ndcg@10 \| NanoBEIR_mean_128_dot_ndcg@10 \| NanoMSMARCO_256_dot_ndcg@10 \| NanoBEIR_mean_256_dot_ndcg@10 \| NanoClimateFEVER_dot_ndcg@10 \| NanoDBPedia_dot_ndcg@10 \| NanoFEVER_dot_ndcg@10 \| NanoFiQA2018_dot_ndcg@10 \| NanoHotpotQA_dot_ndcg@10 \| NanoMSMARCO_dot_ndcg@10 \| NanoNFCorpus_dot_ndcg@10 \| NanoNQ_dot_ndcg@10 \| NanoQuoraRetrieval_dot_ndcg@10 \| NanoSCIDOCS_dot_ndcg@10 \| NanoArguAna_dot_ndcg@10 \| NanoSciFact_dot_ndcg@10 \| NanoTouche2020_dot_ndcg@10 \| NanoBEIR_mean_dot_ndcg@10 \| \|:----------:\|:--------:\|:-------------:\|:---------------:\|:-------------------------:\|:---------------------------:\|:--------------------------:\|:----------------------------:\|:--------------------------:\|:----------------------------:\|:--------------------------:\|:----------------------------:\|:---------------------------:\|:-----------------------------:\|:---------------------------:\|:-----------------------------:\|:----------------------------:\|:-----------------------:\|:---------------------:\|:------------------------:\|:------------------------:\|:-----------------------:\|:------------------------:\|:------------------:\|:------------------------------:\|:-----------------------:\|:-----------------------:\|:-----------------------:\|:--------------------------:\|:-------------------------:\| \| -1 \| -1 \| - \| - \| 0.2447 \| 0.2447 \| 0.3677 \| 0.3677 \| 0.5086 \| 0.5086 \| 0.5304 \| 0.5304 \| 0.6134 \| 0.6134 \| 0.5961 \| 0.5961 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.0646 \| 100 \| 0.5048 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.1293 \| 200 \| 0.5017 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.1939 \| 300 \| 0.531 \| 0.6279 \| 0.2125 \| 0.2125 \| 0.4075 \| 0.4075 \| 0.4686 \| 0.4686 \| 0.5701 \| 0.5701 \| 0.6086 \| 0.6086 \| 0.5877 \| 0.5877 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.2586 \| 400 \| 0.4992 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.3232 \| 500 \| 0.5574 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.3878 \| 600 \| 0.5821 \| 0.6178 \| 0.2312 \| 0.2312 \| 0.4248 \| 0.4248 \| 0.4239 \| 0.4239 \| 0.5142 \| 0.5142 \| 0.6034 \| 0.6034 \| 0.6177 \| 0.6177 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.4525 \| 700 \| 0.5632 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.5171 \| 800 \| 0.5786 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.5818 \| 900 \| 0.5329 \| 0.5743 \| 0.2662 \| 0.2662 \| 0.4468 \| 0.4468 \| 0.4976 \| 0.4976 \| 0.5630 \| 0.5630 \| 0.6279 \| 0.6279 \| 0.6240 \| 0.6240 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.6464 \| 1000 \| 0.5409 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.7111 \| 1100 \| 0.4995 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.7757 \| 1200 \| 0.5269 \| 0.5169 \| 0.2838 \| 0.2838 \| 0.3874 \| 0.3874 \| 0.4738 \| 0.4738 \| 0.5892 \| 0.5892 \| 0.5798 \| 0.5798 \| 0.5962 \| 0.5962 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.8403 \| 1300 \| 0.5553 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.9050 \| 1400 \| 0.45 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.9696 \| 1500 \| 0.4551 \| 0.5188 \| 0.2553 \| 0.2553 \| 0.3971 \| 0.3971 \| 0.4873 \| 0.4873 \| 0.5911 \| 0.5911 \| 0.5877 \| 0.5877 \| 0.6247 \| 0.6247 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| -1 \| -1 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| 0.3566 \| 0.6072 \| 0.9081 \| 0.5253 \| 0.7911 \| 0.6248 \| 0.3345 \| 0.6480 \| 0.9494 \| 0.4266 \| 0.6675 \| 0.7776 \| 0.4780 \| 0.6227 \| * The bold row denotes the saved checkpoint. ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - Energy Consumed: 0.122 kWh - Carbon Emitted: 0.047 kg of CO2 - Hours Used: 0.373 hours ### Training Hardware - On Cloud: No - GPU Model: 1 x NVIDIA GeForce RTX 3090 - CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K - RAM Size: 31.78 GB ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 4.2.0.dev0 - Transformers: 4.52.4 - PyTorch: 2.6.0+cu124 - Accelerate: 1.5.1 - Datasets: 2.21.0 - Tokenizers: 0.21.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### CSRLoss ```bibtex @misc{wen2025matryoshkarevisitingsparsecoding, title={Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation}, author={Tiansheng Wen and Yifei Wang and Zequn Zeng and Zhong Peng and Yudi Su and Xinyang Liu and Bo Chen and Hongwei Liu and Stefanie Jegelka and Chenyu You}, year={2025}, eprint={2503.01776}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2503.01776}, } ``` #### SparseMultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
huihui-ai/Huihui-Qwen3-14B-abliterated-v2	huihui-ai	2025-06-19T15:40:01Z	4	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "chat", "abliterated", "uncensored", "conversational", "base_model:Qwen/Qwen3-14B", "base_model:finetune:Qwen/Qwen3-14B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-17T15:29:37Z	--- library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-14B/blob/main/LICENSE pipeline_tag: text-generation base_model: - Qwen/Qwen3-14B tags: - chat - abliterated - uncensored --- # huihui-ai/Huihui-Qwen3-14B-abliterated-v2 This is an uncensored version of [Qwen/Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B) created with abliteration (see [remove-refusals-with-transformers](https://github.com/Sumandora/remove-refusals-with-transformers) to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. Ablation was performed using a new and faster method, which yields better results. Important Note This version is an improvement over the previous one [huihui-ai/Qwen3-14B-abliterated](https://huggingface.co/huihui-ai/Qwen3-14B-abliterated). The ollama version has also been modified. Changed the candidate layer to eliminate the problem of garbled codes ## ollama You can use [huihui_ai/qwen3-abliterated:14b-v2](https://ollama.com/huihui_ai/qwen3-abliterated:14b-v2) directly, Switch the thinking toggle using /set think and /set nothink ``` ollama run huihui_ai/qwen3-abliterated:14b-v2 ``` ## Usage You can use this model in your applications by loading it with Hugging Face's `transformers` library: ```python from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextStreamer import torch import os import signal import random import numpy as np import time from collections import Counter cpu_count = os.cpu_count() print(f"Number of CPU cores in the system: {cpu_count}") half_cpu_count = cpu_count // 2 os.environ["MKL_NUM_THREADS"] = str(half_cpu_count) os.environ["OMP_NUM_THREADS"] = str(half_cpu_count) torch.set_num_threads(half_cpu_count) print(f"PyTorch threads: {torch.get_num_threads()}") print(f"MKL threads: {os.getenv('MKL_NUM_THREADS')}") print(f"OMP threads: {os.getenv('OMP_NUM_THREADS')}") # Load the model and tokenizer NEW_MODEL_ID = "huihui-ai/Huihui-Qwen3-14B-abliterated-v2" print(f"Load Model {NEW_MODEL_ID} ... ") quant_config_4= BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True, llm_int8_enable_fp32_cpu_offload=True, ) model = AutoModelForCausalLM.from_pretrained( NEW_MODEL_ID, device_map="auto", trust_remote_code=True, #quantization_config=quant_config_4, torch_dtype=torch.bfloat16 ) tokenizer = AutoTokenizer.from_pretrained(NEW_MODEL_ID, trust_remote_code=True) if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token tokenizer.pad_token_id = tokenizer.eos_token_id tokenizer = AutoTokenizer.from_pretrained(NEW_MODEL_ID, trust_remote_code=True) if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token tokenizer.pad_token_id = tokenizer.eos_token_id messages = [] nothink = False same_seed = False skip_prompt=True skip_special_tokens=True do_sample = True def set_random_seed(seed=None): """Set random seed for reproducibility. If seed is None, use int(time.time()).""" if seed is None: seed = int(time.time()) # Convert float to int random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) # If using CUDA torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False return seed # Return seed for logging if needed class CustomTextStreamer(TextStreamer): def __init__(self, tokenizer, skip_prompt=True, skip_special_tokens=True): super().__init__(tokenizer, skip_prompt=skip_prompt, skip_special_tokens=skip_special_tokens) self.generated_text = "" self.stop_flag = False self.init_time = time.time() # Record initialization time self.end_time = None # To store end time self.first_token_time = None # To store first token generation time self.token_count = 0 # To track total tokens def on_finalized_text(self, text: str, stream_end: bool = False): if self.first_token_time is None and text.strip(): # Set first token time on first non-empty text self.first_token_time = time.time() self.generated_text += text # Count tokens in the generated text tokens = self.tokenizer.encode(text, add_special_tokens=False) self.token_count += len(tokens) print(text, end="", flush=True) if stream_end: self.end_time = time.time() # Record end time when streaming ends if self.stop_flag: raise StopIteration def stop_generation(self): self.stop_flag = True self.end_time = time.time() # Record end time when generation is stopped def get_metrics(self): """Returns initialization time, first token time, first token latency, end time, total time, total tokens, and tokens per second.""" if self.end_time is None: self.end_time = time.time() # Set end time if not already set total_time = self.end_time - self.init_time # Total time from init to end tokens_per_second = self.token_count / total_time if total_time > 0 else 0 first_token_latency = (self.first_token_time - self.init_time) if self.first_token_time is not None else None metrics = { "init_time": self.init_time, "first_token_time": self.first_token_time, "first_token_latency": first_token_latency, "end_time": self.end_time, "total_time": total_time, # Total time in seconds "total_tokens": self.token_count, "tokens_per_second": tokens_per_second } return metrics def generate_stream(model, tokenizer, messages, nothink, skip_prompt, skip_special_tokens, do_sample, max_new_tokens): input_ids = tokenizer.apply_chat_template( messages, tokenize=True, enable_thinking = not nothink, add_generation_prompt=True, return_tensors="pt" ) attention_mask = torch.ones_like(input_ids, dtype=torch.long) tokens = input_ids.to(model.device) attention_mask = attention_mask.to(model.device) streamer = CustomTextStreamer(tokenizer, skip_prompt=skip_prompt, skip_special_tokens=skip_special_tokens) def signal_handler(sig, frame): streamer.stop_generation() print("\n[Generation stopped by user with Ctrl+C]") signal.signal(signal.SIGINT, signal_handler) generate_kwargs = {} if do_sample: generate_kwargs = { "do_sample": do_sample, "max_length": max_new_tokens, "temperature": 0.6, "top_k": 20, "top_p": 0.95, "repetition_penalty": 1.2, "no_repeat_ngram_size": 2 } else: generate_kwargs = { "do_sample": do_sample, "max_length": max_new_tokens, "repetition_penalty": 1.2, "no_repeat_ngram_size": 2 } print("Response: ", end="", flush=True) try: generated_ids = model.generate( tokens, attention_mask=attention_mask, #use_cache=False, pad_token_id=tokenizer.pad_token_id, streamer=streamer, generate_kwargs ) del generated_ids except StopIteration: print("\n[Stopped by user]") del input_ids, attention_mask torch.cuda.empty_cache() signal.signal(signal.SIGINT, signal.SIG_DFL) return streamer.generated_text, streamer.stop_flag, streamer.get_metrics() init_seed = set_random_seed() while True: if same_seed: set_random_seed(init_seed) else: init_seed = set_random_seed() print(f"\nnothink: {nothink}") print(f"skip_prompt: {skip_prompt}") print(f"skip_special_tokens: {skip_special_tokens}") print(f"do_sample: {do_sample}") print(f"same_seed: {same_seed}, {init_seed}\n") user_input = input("User: ").strip() if user_input.lower() == "/exit": print("Exiting chat.") break if user_input.lower() == "/clear": messages = [] print("Chat history cleared. Starting a new conversation.") continue if user_input.lower() == "/nothink": nothink = not nothink continue if user_input.lower() == "/skip_prompt": skip_prompt = not skip_prompt continue if user_input.lower() == "/skip_special_tokens": skip_special_tokens = not skip_special_tokens continue if user_input.lower().startswith("/same_seed"): parts = user_input.split() if len(parts) == 1: # /same_seed (no number) same_seed = not same_seed # Toggle switch elif len(parts) == 2: # /same_seed <number> try: init_seed = int(parts[1]) # Extract and convert number to int same_seed = True except ValueError: print("Error: Please provide a valid integer after /same_seed") continue if user_input.lower() == "/do_sample": do_sample = not do_sample continue if not user_input: print("Input cannot be empty. Please enter something.") continue messages.append({"role": "user", "content": user_input}) activated_experts = [] response, stop_flag, metrics = generate_stream(model, tokenizer, messages, nothink, skip_prompt, skip_special_tokens, do_sample, 320960) print("\n\nMetrics:") for key, value in metrics.items(): print(f" {key}: {value}") print("", flush=True) if stop_flag: continue messages.append({"role": "assistant", "content": response}) # Remove all hooks after inference for h in hooks: h.remove() ``` ### Usage Warnings - Risk of Sensitive or Controversial Outputs: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs. - Not Suitable for All Audiences: Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security. - Legal and Ethical Responsibilities: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences. - Research and Experimental Use: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications. - Monitoring and Review Recommendations: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content. - No Default Safety Guarantees**: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use. ### Donation If you like it, please click 'like' and follow us for more updates. You can follow [x.com/support_huihui](https://x.com/support_huihui) to get the latest model information from huihui.ai. ##### Your donation helps us continue our further development and improvement, a cup of coffee can do it. - bitcoin（BTC): ``` bc1qqnkhuchxw0zqjh2ku3lu4hq45hc6gy84uk70ge ```
hospital-teresopolis-link/Original.Full.video.18.hospital.teresopolis.hospital.de.teresopolis.video.portal.Zacarias	hospital-teresopolis-link	2025-06-19T15:38:30Z	0	0	null	[ "region:us" ]	null	2025-06-19T15:37:58Z	<a rel="nofollow" href="https://viralflix.xyz/leaked/?fre">🔴 CLICK HERE 🌐==►► Download Now)</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?fre">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?fre"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
csikasote/mms-1b-all-nyagen-combined-42	csikasote	2025-06-19T15:37:22Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "nyagen", "mms", "generated_from_trainer", "base_model:facebook/mms-1b-all", "base_model:finetune:facebook/mms-1b-all", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-06-19T13:02:44Z	--- library_name: transformers license: cc-by-nc-4.0 base_model: facebook/mms-1b-all tags: - automatic-speech-recognition - nyagen - mms - generated_from_trainer metrics: - wer model-index: - name: mms-1b-all-nyagen-combined-42 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mms-1b-all-nyagen-combined-42 This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the NYAGEN - NYA dataset. It achieves the following results on the evaluation set: - Loss: 0.3879 - Wer: 0.3078 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 30.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-------:\|:----:\|:---------------:\|:------:\| \| 8.807 \| 0.5025 \| 100 \| 5.9307 \| 0.9998 \| \| 5.1806 \| 1.0050 \| 200 \| 4.7590 \| 1.0179 \| \| 3.8969 \| 1.5075 \| 300 \| 3.4468 \| 0.9976 \| \| 3.367 \| 2.0101 \| 400 \| 3.2098 \| 0.9938 \| \| 3.2105 \| 2.5126 \| 500 \| 3.1289 \| 0.9933 \| \| 3.1366 \| 3.0151 \| 600 \| 3.1086 \| 0.9927 \| \| 3.0162 \| 3.5176 \| 700 \| 2.9525 \| 0.9948 \| \| 2.9078 \| 4.0201 \| 800 \| 1.5350 \| 1.0042 \| \| 0.4877 \| 4.5226 \| 900 \| 0.5052 \| 0.4215 \| \| 0.3193 \| 5.0251 \| 1000 \| 0.4687 \| 0.3793 \| \| 0.2953 \| 5.5276 \| 1100 \| 0.4322 \| 0.3547 \| \| 0.2815 \| 6.0302 \| 1200 \| 0.4283 \| 0.3460 \| \| 0.2729 \| 6.5327 \| 1300 \| 0.4171 \| 0.3327 \| \| 0.2561 \| 7.0352 \| 1400 \| 0.4085 \| 0.3271 \| \| 0.2543 \| 7.5377 \| 1500 \| 0.4071 \| 0.3290 \| \| 0.2443 \| 8.0402 \| 1600 \| 0.4039 \| 0.3149 \| \| 0.2402 \| 8.5427 \| 1700 \| 0.4088 \| 0.3173 \| \| 0.2273 \| 9.0452 \| 1800 \| 0.4048 \| 0.3149 \| \| 0.2299 \| 9.5477 \| 1900 \| 0.3911 \| 0.3063 \| \| 0.2313 \| 10.0503 \| 2000 \| 0.3879 \| 0.3077 \| \| 0.2203 \| 10.5528 \| 2100 \| 0.3874 \| 0.3033 \| \| 0.2168 \| 11.0553 \| 2200 \| 0.3837 \| 0.2985 \| \| 0.2167 \| 11.5578 \| 2300 \| 0.3810 \| 0.2979 \| \| 0.211 \| 12.0603 \| 2400 \| 0.3854 \| 0.2952 \| \| 0.2039 \| 12.5628 \| 2500 \| 0.3803 \| 0.2868 \| \| 0.2152 \| 13.0653 \| 2600 \| 0.3760 \| 0.2926 \| \| 0.2003 \| 13.5678 \| 2700 \| 0.3732 \| 0.2883 \| \| 0.2025 \| 14.0704 \| 2800 \| 0.3798 \| 0.2878 \| \| 0.2005 \| 14.5729 \| 2900 \| 0.3796 \| 0.2879 \| \| 0.2032 \| 15.0754 \| 3000 \| 0.3764 \| 0.2817 \| ### Framework versions - Transformers 4.53.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.0
MikeGreen2710/ner_cons_dims_final	MikeGreen2710	2025-06-19T15:32:52Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-06-19T15:32:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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hasdal/21a58fba-d539-4969-960e-60eff2254792	hasdal	2025-06-19T15:28:22Z	0	0	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "unsloth", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-06-19T15:14:36Z	--- library_name: transformers tags: - unsloth --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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altinkedi/xxtrgpt2v3s	altinkedi	2025-06-19T15:27:48Z	0	0	transformers	[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-19T15:20:50Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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sanchit42/qwen3-0.6B-instruct-29reports-lora256-slim	sanchit42	2025-06-19T15:23:50Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-19T15:22:14Z	--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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phospho-app/gc1724-ACT_BBOX-bottle-y67xn	phospho-app	2025-06-19T15:23:13Z	0	0	null	[ "phosphobot", "act", "region:us" ]	null	2025-06-19T15:21:10Z	--- tags: - phosphobot - act task_categories: - robotics --- # act Model - phospho Training Pipeline ## Error Traceback We faced an issue while training your model. ``` Caught KeyError in DataLoader worker process 0. Original Traceback (most recent call last): File "/opt/conda/lib/python3.11/site-packages/torch/utils/data/_utils/worker.py", line 349, in _worker_loop data = fetcher.fetch(index) # type: ignore[possibly-undefined] ^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/torch/utils/data/_utils/fetch.py", line 52, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/torch/utils/data/_utils/fetch.py", line 52, in <listcomp> data = [self.dataset[idx] for idx in possibly_batched_index] ~~~~~~~~~~~~^^^^^ File "/root/src/helper.py", line 198, in __getitem__ frame = self.cache[episode_idx][video_key][row_idx] ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^ KeyError: '.DS_Store' ``` ## Training parameters: - Dataset: [gc1724/bottle](https://huggingface.co/datasets/gc1724/bottle) - Wandb run URL: None - Epochs: None - Batch size: 100 - Training steps: 10000 📖 Get Started: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 Get your robot: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
alfredcs/gemma-3-27b-firstaid-icd10-merged	alfredcs	2025-06-19T15:23:10Z	0	0	transformers	[ "transformers", "pytorch", "safetensors", "gemma3", "image-text-to-text", "trl", "grpo", "GRPO", "Reasoning-Course", "conversational", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]	image-text-to-text	2025-06-19T05:13:36Z	--- library_name: transformers tags: - trl - grpo - GRPO - Reasoning-Course --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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BootesVoid/cmbab3f3e0nsp1b1yngcaf3y6_cmc3i211r00exnx8d905xzmsr	BootesVoid	2025-06-19T15:22:48Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-06-19T15:22:46Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: NATURAL --- # Cmbab3F3E0Nsp1B1Yngcaf3Y6_Cmc3I211R00Exnx8D905Xzmsr <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `NATURAL` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "NATURAL", "lora_weights": "https://huggingface.co/BootesVoid/cmbab3f3e0nsp1b1yngcaf3y6_cmc3i211r00exnx8d905xzmsr/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmbab3f3e0nsp1b1yngcaf3y6_cmc3i211r00exnx8d905xzmsr', weight_name='lora.safetensors') image = pipeline('NATURAL').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmbab3f3e0nsp1b1yngcaf3y6_cmc3i211r00exnx8d905xzmsr/discussions) to add images that show off what you’ve made with this LoRA.
Catality/3b-shitboxes	Catality	2025-06-19T15:21:54Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-06-19T15:13:05Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: Catality - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
sungkwan2/my_awesome_food_model	sungkwan2	2025-06-19T15:20:54Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-06-19T15:07:11Z	--- library_name: transformers license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer metrics: - accuracy model-index: - name: my_awesome_food_model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # my_awesome_food_model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6158 - Accuracy: 0.899 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.7715 \| 1.0 \| 63 \| 2.5460 \| 0.87 \| \| 1.8698 \| 2.0 \| 126 \| 1.7718 \| 0.897 \| \| 1.6144 \| 3.0 \| 189 \| 1.6158 \| 0.899 \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
MarkProMaster229/internet_dialog	MarkProMaster229	2025-06-19T15:20:21Z	0	0	null	[ "safetensors", "gpt2", "ru", "license:openrail", "region:us" ]	null	2025-06-19T15:04:10Z	--- license: openrail language: - ru --- # русская языковая модель, обученная на данных с анонимного имиджборда > ⚠️ ВНИМАНИЕ: Модель обучена на нефильтрованных и непроверенных данных с публичного анонимного форума (2ch.hk / Двач). > В содержимом могут присутствовать брань, токсичный контент, NSFW, а также более тяжёлые формы агрессии, оскорблений и шок-контента. ## 🧠 Обзор Эта модель является экспериментом в области обучения языковых моделей на неформальной русской речи. Она основана на предварительно обученной модели [`sberbank-ai/rugpt2`](https://huggingface.co/ai-forever/rugpt3small_based_on_gpt2?text=%D0%9E%D0%B4%D0%BD%D0%B0%D0%B6%D0%B4%D1%8B) и дообучена на данных с форума 2ch.hk. В обучающем наборе использовались: - Заголовки тредов как входные запросы (вопросы), - Первые 1–3 ответа в треде как отклик (реплики "бота"). ### ❗ ВАЖНО Автор не проводил ручной модерации или фильтрации содержимого. Контент получен "как есть", автоматически. Автор не несёт ответственности за содержание сгенерированных текстов или возможный вред от их использования. Модель не предназначена для продакшен-использования. --- ## 🎓 Цель проекта Модель предназначена исключительно для исследовательских и образовательных целей: - Анализ структуры русскоязычного неформального диалога, - Исследование токсичности в языковых моделях, - Эксперименты с малыми объёмами данных. --- ## ⚠️ Предупреждение о содержании Модель может генерировать тексты, содержащие: - Оскорбления, дискриминацию, ненормативную лексику, - Насилие, угрозы, NSFW и прочий шок-контент. Не используйте эту модель в продуктах, чатах с пользователями, в системах рекомендаций, в образовательных учреждениях или в чувствительных сценариях. Использование модели — исключительно на ваш страх и риск. Автор не несёт ответственности за любые последствия, вызванные использованием модели. --- ## 📜 Лицензия Модель и обучающий набор распространяются по лицензии OpenRAIL-M. OpenRAIL — это лицензия, разработанная для языковых моделей и направленная на обеспечение этичного, безопасного и ограниченного использования. - Использование разрешено только при соблюдении условий ответственного применения. - Коммерческое использование запрещено без отдельного разрешения. - Пользователь обязан атрибутировать оригинального автора и использовать модель в соответствии с положениями лицензии. 📄 Подробнее: [https://www.bigcode-project.org/docs/pages/bigcode-openrail/](https://www.bigcode-project.org/docs/pages/bigcode-openrail/)
MJ92/AceGPT-v2-8B-Chat_finetuned_5000_fr	MJ92	2025-06-19T15:18:25Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-19T15:06:44Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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morturr/Llama-2-7b-hf-PAIR_dadjokes_headlines-COMB-dadjokes-comb-1-seed-42-2025-06-19	morturr	2025-06-19T15:17:40Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2025-06-19T15:17:19Z	--- library_name: peft license: llama2 base_model: meta-llama/Llama-2-7b-hf tags: - trl - sft - generated_from_trainer model-index: - name: Llama-2-7b-hf-PAIR_dadjokes_headlines-COMB-dadjokes-comb-1-seed-42-2025-06-19 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Llama-2-7b-hf-PAIR_dadjokes_headlines-COMB-dadjokes-comb-1-seed-42-2025-06-19 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.13.2 - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.0.2 - Tokenizers 0.20.1
jasonxubin/jielin-lora	jasonxubin	2025-06-19T15:17:31Z	0	0	null	[ "license:other", "region:us" ]	null	2025-06-19T14:38:18Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md ---
LarryAIDraw/kamisato_ayakaPDXL_scarxzys	LarryAIDraw	2025-06-19T15:17:06Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-06-19T15:15:31Z	--- license: creativeml-openrail-m --- https://civitai.com/models/1022127/pony-kamisato-ayaka-or-genshin-impact
LarryAIDraw/arknights_skadi_ponyXL	LarryAIDraw	2025-06-19T15:16:33Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-06-19T15:12:06Z	--- license: creativeml-openrail-m --- https://civitai.com/models/326557?modelVersionId=366029
rodrigomt/gemma-merge	rodrigomt	2025-06-19T15:11:21Z	0	0	null	[ "safetensors", "gemma3", "merge", "mergekit", "lazymergekit", "CEIA-UFG/Gemma-3-Gaia-PT-BR-4b-it", "soob3123/amoral-gemma3-4B-v2", "base_model:CEIA-UFG/Gemma-3-Gaia-PT-BR-4b-it", "base_model:merge:CEIA-UFG/Gemma-3-Gaia-PT-BR-4b-it", "base_model:soob3123/amoral-gemma3-4B-v2", "base_model:merge:soob3123/amoral-gemma3-4B-v2", "region:us" ]	null	2025-06-19T02:55:19Z	--- base_model: - CEIA-UFG/Gemma-3-Gaia-PT-BR-4b-it - soob3123/amoral-gemma3-4B-v2 tags: - merge - mergekit - lazymergekit - CEIA-UFG/Gemma-3-Gaia-PT-BR-4b-it - soob3123/amoral-gemma3-4B-v2 --- # gemma-merge gemma-merge is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [CEIA-UFG/Gemma-3-Gaia-PT-BR-4b-it](https://huggingface.co/CEIA-UFG/Gemma-3-Gaia-PT-BR-4b-it) * [soob3123/amoral-gemma3-4B-v2](https://huggingface.co/soob3123/amoral-gemma3-4B-v2) ## 🧩 Configuration ```yaml models: - model: CEIA-UFG/Gemma-3-Gaia-PT-BR-4b-it parameters: density: 0.5 weight: 0.5 - model: soob3123/amoral-gemma3-4B-v2 parameters: density: 0.5 weight: 0.5 merge_method: ties base_model: unsloth/gemma-3-4b-pt parameters: normalize: true int8_mask: true dtype: bfloat16 tokenizer: source: unsloth/gemma-3-4b-pt ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "rodrigomt/gemma-merge" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
raul111204/gpt-neo-125m-xsum-raul3-b	raul111204	2025-06-19T15:11:20Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "autotrain", "text-generation-inference", "text-generation", "peft", "conversational", "dataset:mia-llm/xsum-raw-MIA", "base_model:EleutherAI/gpt-neo-125m", "base_model:finetune:EleutherAI/gpt-neo-125m", "license:other", "endpoints_compatible", "region:us" ]	text-generation	2025-06-19T14:23:26Z	--- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers base_model: EleutherAI/gpt-neo-125m widget: - messages: - role: user content: What is your favorite condiment? license: other datasets: - mia-llm/xsum-raw-MIA --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```
BootesVoid/cmbyrkjtk04cprdqsuhkq1b61_cmc23uo8s0c1yrdqsimijbqb2	BootesVoid	2025-06-19T15:11:11Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-06-19T15:11:09Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: TRACEY --- # Cmbyrkjtk04Cprdqsuhkq1B61_Cmc23Uo8S0C1Yrdqsimijbqb2 <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `TRACEY` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "TRACEY", "lora_weights": "https://huggingface.co/BootesVoid/cmbyrkjtk04cprdqsuhkq1b61_cmc23uo8s0c1yrdqsimijbqb2/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmbyrkjtk04cprdqsuhkq1b61_cmc23uo8s0c1yrdqsimijbqb2', weight_name='lora.safetensors') image = pipeline('TRACEY').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmbyrkjtk04cprdqsuhkq1b61_cmc23uo8s0c1yrdqsimijbqb2/discussions) to add images that show off what you’ve made with this LoRA.
Richard9905/full-merged-bible-model	Richard9905	2025-06-19T15:11:00Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2025-06-19T15:07:23Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
LarryAIDraw/miyakoPDXL_scarxzys	LarryAIDraw	2025-06-19T15:10:19Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-06-19T15:08:29Z	--- license: creativeml-openrail-m --- https://civitai.com/models/632492/pony-tsukiyuki-miyako-or-blue-archive
zeerakwyne/test8_doc-splitter-llama-3-2-3B-20-epoch_merged	zeerakwyne	2025-06-19T15:09:56Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-19T15:09:13Z	--- base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: zeerakwyne - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
LarryAIDraw/Nonoa_Miyamae_anime-44	LarryAIDraw	2025-06-19T15:05:01Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2025-06-19T15:01:29Z	--- license: creativeml-openrail-m --- https://civitai.com/models/704533/nonoa-miyamae-or-alya-sometimes-hides-her-feelings-in-russian-or
Mariogver/detr-finetuned-microglia_3	Mariogver	2025-06-19T15:04:43Z	0	0	transformers	[ "transformers", "safetensors", "detr", "object-detection", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	object-detection	2025-06-19T15:04:30Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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sanchit42/llama3.1-8B-instruct-29reports-lora256-slim	sanchit42	2025-06-19T14:59:44Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-19T14:56:01Z	--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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liuh6/whisper-tiny_to_Chinese_accent	liuh6	2025-06-19T14:59:17Z	40	0	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "en", "dataset:Chinese_english", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2025-03-08T01:44:44Z	--- library_name: transformers language: - en license: apache-2.0 base_model: openai/whisper-tiny tags: - hf-asr-leaderboard - generated_from_trainer datasets: - Chinese_english metrics: - wer model-index: - name: Whisper tiny Chinese with pitch pertubation results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Chinese English type: Chinese_english args: 'config: default, split: test' metrics: - name: Wer type: wer value: 16.187450357426528 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper tiny Chinese with pitch pertubation This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Chinese English dataset. It achieves the following results on the evaluation set: - Loss: 0.3669 - Wer: 16.1875 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 1500 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 0.2024 \| 1.6667 \| 500 \| 0.3648 \| 16.4257 \| \| 0.0151 \| 3.3333 \| 1000 \| 0.3584 \| 16.7911 \| \| 0.0035 \| 5.0 \| 1500 \| 0.3669 \| 16.1875 \| ### Framework versions - Transformers 4.49.0 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1
Richard9905/lora-bible-model	Richard9905	2025-06-19T14:58:40Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-19T14:58:31Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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BootesVoid/cmc0un2u0098srdqs7tomm6xx_cmc3f9kss006vnx8dflbcsdbb	BootesVoid	2025-06-19T14:58:35Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-06-19T14:58:34Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: SEXY --- # Cmc0Un2U0098Srdqs7Tomm6Xx_Cmc3F9Kss006Vnx8Dflbcsdbb <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `SEXY` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "SEXY", "lora_weights": "https://huggingface.co/BootesVoid/cmc0un2u0098srdqs7tomm6xx_cmc3f9kss006vnx8dflbcsdbb/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmc0un2u0098srdqs7tomm6xx_cmc3f9kss006vnx8dflbcsdbb', weight_name='lora.safetensors') image = pipeline('SEXY').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmc0un2u0098srdqs7tomm6xx_cmc3f9kss006vnx8dflbcsdbb/discussions) to add images that show off what you’ve made with this LoRA.
samtse123/finetune_model	samtse123	2025-06-19T14:57:58Z	0	0	transformers	[ "transformers", "gguf", "qwen2", "text-generation-inference", "unsloth", "qwen3", "en", "base_model:unsloth/Qwen3-1.7B-unsloth-bnb-4bit", "base_model:quantized:unsloth/Qwen3-1.7B-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-19T13:55:09Z	--- base_model: unsloth/Qwen3-1.7B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: samtse123 - License: apache-2.0 - Finetuned from model : unsloth/Qwen3-1.7B-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
nicofarr/panns_ResNet22	nicofarr	2025-06-19T14:55:50Z	0	0	pytorch	[ "pytorch", "safetensors", "ResNet22", "audio", "model_hub_mixin", "panns", "pytorch_model_hub_mixin", "tagging", "license:apache-2.0", "region:us" ]	null	2025-06-19T14:52:31Z	--- library_name: pytorch license: apache-2.0 tags: - audio - model_hub_mixin - panns - pytorch_model_hub_mixin - tagging --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: https://github.com/qiuqiangkong/audioset_tagging_cnn - Docs: https://github.com/qiuqiangkong/audioset_tagging_cnn
ik-ram28/BioMistral-CPT-SFT-7B	ik-ram28	2025-06-19T14:54:40Z	35	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "medical", "conversational", "fr", "en", "base_model:BioMistral/BioMistral-7B", "base_model:finetune:BioMistral/BioMistral-7B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-02-08T23:22:52Z	--- library_name: transformers tags: - medical license: apache-2.0 language: - fr - en base_model: - ik-ram28/BioMistral-CPT-7B - BioMistral/BioMistral-7B --- ## Model Description BioMistral-CPT-SFT-7B is a French medical language model based on BioMistral-7B, adapted for French medical domain applications through a combined approach of Continual Pre-Training (CPT) followed by Supervised Fine-Tuning (SFT). ## Model Details - Model Type: Causal Language Model - Base Model: BioMistral-7B - Language: French (adapted from English medical model) - Domain: Medical/Healthcare - Parameters: 7 billion - License: Apache 2.0 - Paper: [Adaptation des connaissances médicales pour les grands modèles de langue : Stratégies et analyse comparative](https://github.com/ikram28/medllm-strategies) ## Training Details ### Continual Pre-Training (CPT) - Dataset: NACHOS corpus (opeN crAwled frenCh Healthcare cOrpuS) - Size: 7.4 GB of French medical texts - Word Count: Over 1 billion words - Sources: 24 French medical websites - Training Duration: 2.8 epochs - Hardware: 32 NVIDIA H100 80GB GPUs - Training Time: 11 hours - Optimizer: AdamW - Learning Rate: 2e-5 - Weight Decay: 0.01 - Batch Size: 16 with gradient accumulation of 2 ### Supervised Fine-Tuning (SFT) - Dataset: 30K French medical question-answer pairs - 10K native French medical questions - 10K translated medical questions from English resources - 10K generated questions from French medical texts - Method: DoRA (Weight-Decomposed Low-Rank Adaptation) - Training Duration: 10 epochs - Hardware: 1 NVIDIA H100 80GB GPU - Training Time: 42 hours - Rank: 16 - Alpha: 16 - Learning Rate: 2e-5 - Batch Size: 4 ## Computational Impact - Total Training Time: 53 hours (11h CPT + 42h SFT) - Hardware: 32 GPU H100 + 1 GPU H100 - Carbon Emissions: 10.11 kgCO2e (9.04 + 1.07) ## Ethical Considerations - Medical Accuracy: This model is for research and educational purposes only. Performance limitations make it unsuitable for critical medical applications - Bias: May contain biases from both English and French medical literature ## Citation If you use this model, please cite: ```bibtex ``` ## Contact For questions about this model, please contact: [email protected]
hectordiazgomez/sirio-4b-translation	hectordiazgomez	2025-06-19T14:48:11Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-19T14:47:24Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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alakxender/flan-t5-base-alpaca-dv5	alakxender	2025-06-19T14:47:02Z	136	0	transformers	[ "transformers", "safetensors", "t5", "text2text-generation", "dhivehi", "gpt", "llm", "thaana", "text-gen", "dv", "dataset:alakxender/alpaca_dhivehi", "base_model:google/flan-t5-base", "base_model:finetune:google/flan-t5-base", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2025-05-31T08:18:13Z	--- library_name: transformers tags: - dhivehi - gpt - llm - thaana - text-gen license: mit datasets: - alakxender/alpaca_dhivehi language: - dv metrics: - rouge base_model: - google/flan-t5-base --- # Alpaca Dhivehi Fine-Tuned Flan-T5 This repository contains a fine-tuned Flan-T5 model on the Alpaca Dhivehi dataset, aimed at enabling Dhivehi language instruction-following tasks. *Note: The model can follow instructions and inputs to some extent, but it’s not strictly trained for perfect adherence. Outputs may be partially aligned but are not guaranteed to be fully accurate. Treat results as experimental.* ## Model Details - Base model: `google/flan-t5-small` (or whichever size you used) - Dataset: Alpaca Dhivehi , Translation from English to Dhivehi - Training epochs: 5 - Final evaluation: - `eval_loss`: 2.59 - `ROUGE-1`: 0.10 - `ROUGE-2`: 0.03 - `ROUGE-L`: 0.107 ## Usage To run inference using the fine-tuned model: ```python import torch from transformers import T5Tokenizer, T5ForConditionalGeneration MODEL_PATH = "alakxender/flan-t5-base-alpaca-dv5" device = torch.device("cuda" if torch.cuda.is_available() else "cpu") tokenizer = T5Tokenizer.from_pretrained(MODEL_PATH) model = T5ForConditionalGeneration.from_pretrained(MODEL_PATH).to(device) def generate_response(instruction, input_text): combined_input = f"{instruction.strip()} {input_text.strip()}" if input_text else instruction.strip() inputs = tokenizer(combined_input, return_tensors="pt", truncation=True, max_length=256).to(device) output_ids = model.generate( **inputs, max_new_tokens=256, num_beams=8, repetition_penalty=1.5, no_repeat_ngram_size=3, do_sample=True, early_stopping=True, temperature=0.1 ) decoded_output = tokenizer.decode(output_ids[0], skip_special_tokens=True) return decoded_output # Example usage: instruction = "ދީފައިވާ މައުޟޫޢާ ބެހޭގޮތުން ކުރު ޕެރެގްރާފެއް ލިޔެލާށެވެ." input_text = "އިއާދަކުރަނިވި ހަކަތަ ބޭނުންކުރުމުގެ މުހިންމުކަން" print(generate_response(instruction, input_text)) އިއާދަކުރަނިވި ހަކަތަ ބޭނުންކުރުމުގެ މުހިންމު އެއް މައުޟޫއަކީ ސޯލާ، ވިންޑް، ހައިޑްރޯ، ޖިއޮތަރމަލް، އަދި ހައިޑްރޯއިލެކްޓްރިކް ޕަވަރ ފަދަ އިއާދަކުރަނިވި ހަކަތައިން ގްރީންހައުސް ގޭސްތައް ބޭރުވުން ..... ``` ## Evaluation Results From the last evaluation: ``` { 'eval_loss': 2.591374158859253, 'eval_rouge1': 0.10920254665663279, 'eval_rouge2': 0.03587297080345582, 'eval_rougeL': 0.10796498746412672, 'eval_rougeLsum': 0.1083282268650986, 'eval_runtime': 1204.3847, 'eval_samples_per_second': 4.298, 'eval_steps_per_second': 2.149, 'epoch': 5.0 } ``` ## Notes - This fine-tuned model is experimental and intended for research on Dhivehi-language instruction-following tasks.
l0tr1k/photography-mistral-16bit-merged-new	l0tr1k	2025-06-19T14:37:07Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-19T14:33:27Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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vcabeli/Qwen3-8B-Open-R1-GRPO-spatial-dea	vcabeli	2025-06-19T14:30:42Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "trl", "grpo", "conversational", "arxiv:2402.03300", "base_model:Qwen/Qwen3-8B", "base_model:finetune:Qwen/Qwen3-8B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-19T14:06:23Z	--- base_model: Qwen/Qwen3-8B library_name: transformers model_name: Qwen3-8B-Open-R1-GRPO-spatial-dea tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Qwen3-8B-Open-R1-GRPO-spatial-dea This model is a fine-tuned version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="vcabeli/Qwen3-8B-Open-R1-GRPO-spatial-dea", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/vincent-cabeli-owkin/huggingface/runs/zolvk2vf) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.18.0 - Transformers: 4.52.3 - Pytorch: 2.6.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
deepkeep-ai/gemma-2-2b-pii-token-classifier	deepkeep-ai	2025-06-19T14:27:40Z	0	0	transformers	[ "transformers", "safetensors", "gemma2", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	token-classification	2025-06-18T12:49:36Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
wkang123/WellKang-v0.1.1.1	wkang123	2025-06-19T14:26:19Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-19T14:26:08Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: wkang123 - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
tomaarsen/csr-mxbai-embed-large-v1-nq-updated-reconstruction-2	tomaarsen	2025-06-19T14:25:19Z	0	0	sentence-transformers	[ "sentence-transformers", "safetensors", "bert", "sparse-encoder", "sparse", "csr", "generated_from_trainer", "dataset_size:99000", "loss:CSRLoss", "loss:SparseMultipleNegativesRankingLoss", "feature-extraction", "en", "dataset:sentence-transformers/natural-questions", "arxiv:1908.10084", "arxiv:2503.01776", "arxiv:1705.00652", "base_model:mixedbread-ai/mxbai-embed-large-v1", "base_model:finetune:mixedbread-ai/mxbai-embed-large-v1", "license:apache-2.0", "model-index", "co2_eq_emissions", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2025-06-19T14:25:12Z	--- language: - en license: apache-2.0 tags: - sentence-transformers - sparse-encoder - sparse - csr - generated_from_trainer - dataset_size:99000 - loss:CSRLoss - loss:SparseMultipleNegativesRankingLoss base_model: mixedbread-ai/mxbai-embed-large-v1 widget: - text: Saudi Arabia–United Arab Emirates relations However, the UAE and Saudi Arabia continue to take somewhat differing stances on regional conflicts such the Yemeni Civil War, where the UAE opposes Al-Islah, and supports the Southern Movement, which has fought against Saudi-backed forces, and the Syrian Civil War, where the UAE has disagreed with Saudi support for Islamist movements.[4] - text: Economy of New Zealand New Zealand's diverse market economy has a sizable service sector, accounting for 63% of all GDP activity in 2013.[17] Large scale manufacturing industries include aluminium production, food processing, metal fabrication, wood and paper products. Mining, manufacturing, electricity, gas, water, and waste services accounted for 16.5% of GDP in 2013.[17] The primary sector continues to dominate New Zealand's exports, despite accounting for 6.5% of GDP in 2013.[17] - text: who was the first president of indian science congress meeting held in kolkata in 1914 - text: Get Over It (Eagles song) "Get Over It" is a song by the Eagles released as a single after a fourteen-year breakup. It was also the first song written by bandmates Don Henley and Glenn Frey when the band reunited. "Get Over It" was played live for the first time during their Hell Freezes Over tour in 1994. It returned the band to the U.S. Top 40 after a fourteen-year absence, peaking at No. 31 on the Billboard Hot 100 chart. It also hit No. 4 on the Billboard Mainstream Rock Tracks chart. The song was not played live by the Eagles after the "Hell Freezes Over" tour in 1994. It remains the group's last Top 40 hit in the U.S. - text: 'Cornelius the Centurion Cornelius (Greek: Κορνήλιος) was a Roman centurion who is considered by Christians to be one of the first Gentiles to convert to the faith, as related in Acts of the Apostles.' datasets: - sentence-transformers/natural-questions pipeline_tag: feature-extraction library_name: sentence-transformers metrics: - dot_accuracy@1 - dot_accuracy@3 - dot_accuracy@5 - dot_accuracy@10 - dot_precision@1 - dot_precision@3 - dot_precision@5 - dot_precision@10 - dot_recall@1 - dot_recall@3 - dot_recall@5 - dot_recall@10 - dot_ndcg@10 - dot_mrr@10 - dot_map@100 - query_active_dims - query_sparsity_ratio - corpus_active_dims - corpus_sparsity_ratio co2_eq_emissions: emissions: 53.0273650168183 energy_consumed: 0.13642164181511365 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K ram_total_size: 31.777088165283203 hours_used: 0.41 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: Sparse CSR model trained on Natural Questions results: - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO 128 type: NanoMSMARCO_128 metrics: - type: dot_accuracy@1 value: 0.38 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.66 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.72 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.82 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.38 name: Dot Precision@1 - type: dot_precision@3 value: 0.22 name: Dot Precision@3 - type: dot_precision@5 value: 0.14400000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.08199999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.38 name: Dot Recall@1 - type: dot_recall@3 value: 0.66 name: Dot Recall@3 - type: dot_recall@5 value: 0.72 name: Dot Recall@5 - type: dot_recall@10 value: 0.82 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6074833126260415 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5392698412698412 name: Dot Mrr@10 - type: dot_map@100 value: 0.5478391044500884 name: Dot Map@100 - type: query_active_dims value: 128.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.96875 name: Query Sparsity Ratio - type: corpus_active_dims value: 128.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.96875 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNFCorpus 128 type: NanoNFCorpus_128 metrics: - type: dot_accuracy@1 value: 0.44 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.54 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.64 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.68 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.44 name: Dot Precision@1 - type: dot_precision@3 value: 0.3133333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.28 name: Dot Precision@5 - type: dot_precision@10 value: 0.24600000000000002 name: Dot Precision@10 - type: dot_recall@1 value: 0.045132854073603 name: Dot Recall@1 - type: dot_recall@3 value: 0.06751477851868476 name: Dot Recall@3 - type: dot_recall@5 value: 0.08765169300408888 name: Dot Recall@5 - type: dot_recall@10 value: 0.12035202437952344 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.3037747903284991 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5081904761904761 name: Dot Mrr@10 - type: dot_map@100 value: 0.13867493157888547 name: Dot Map@100 - type: query_active_dims value: 128.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.96875 name: Query Sparsity Ratio - type: corpus_active_dims value: 128.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.96875 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNQ 128 type: NanoNQ_128 metrics: - type: dot_accuracy@1 value: 0.48 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.66 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.7 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.84 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.48 name: Dot Precision@1 - type: dot_precision@3 value: 0.22666666666666668 name: Dot Precision@3 - type: dot_precision@5 value: 0.14800000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.08999999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.45 name: Dot Recall@1 - type: dot_recall@3 value: 0.62 name: Dot Recall@3 - type: dot_recall@5 value: 0.67 name: Dot Recall@5 - type: dot_recall@10 value: 0.81 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6337677207897237 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5932936507936507 name: Dot Mrr@10 - type: dot_map@100 value: 0.5761859932841973 name: Dot Map@100 - type: query_active_dims value: 128.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.96875 name: Query Sparsity Ratio - type: corpus_active_dims value: 128.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.96875 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean 128 type: NanoBEIR_mean_128 metrics: - type: dot_accuracy@1 value: 0.43333333333333335 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.6200000000000001 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.6866666666666665 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.7799999999999999 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.43333333333333335 name: Dot Precision@1 - type: dot_precision@3 value: 0.25333333333333335 name: Dot Precision@3 - type: dot_precision@5 value: 0.19066666666666668 name: Dot Precision@5 - type: dot_precision@10 value: 0.13933333333333334 name: Dot Precision@10 - type: dot_recall@1 value: 0.2917109513578677 name: Dot Recall@1 - type: dot_recall@3 value: 0.44917159283956165 name: Dot Recall@3 - type: dot_recall@5 value: 0.49255056433469635 name: Dot Recall@5 - type: dot_recall@10 value: 0.5834506747931745 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5150086079147548 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5469179894179893 name: Dot Mrr@10 - type: dot_map@100 value: 0.42090000977105707 name: Dot Map@100 - type: query_active_dims value: 128.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.96875 name: Query Sparsity Ratio - type: corpus_active_dims value: 128.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.96875 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO 256 type: NanoMSMARCO_256 metrics: - type: dot_accuracy@1 value: 0.44 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.64 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.74 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.84 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.44 name: Dot Precision@1 - type: dot_precision@3 value: 0.21333333333333332 name: Dot Precision@3 - type: dot_precision@5 value: 0.14800000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.08399999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.44 name: Dot Recall@1 - type: dot_recall@3 value: 0.64 name: Dot Recall@3 - type: dot_recall@5 value: 0.74 name: Dot Recall@5 - type: dot_recall@10 value: 0.84 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6405150998246686 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5768809523809523 name: Dot Mrr@10 - type: dot_map@100 value: 0.5851061967133396 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNFCorpus 256 type: NanoNFCorpus_256 metrics: - type: dot_accuracy@1 value: 0.42 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.58 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.6 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.62 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.42 name: Dot Precision@1 - type: dot_precision@3 value: 0.37333333333333324 name: Dot Precision@3 - type: dot_precision@5 value: 0.324 name: Dot Precision@5 - type: dot_precision@10 value: 0.248 name: Dot Precision@10 - type: dot_recall@1 value: 0.045123947439696374 name: Dot Recall@1 - type: dot_recall@3 value: 0.08083248635236362 name: Dot Recall@3 - type: dot_recall@5 value: 0.0993952531376598 name: Dot Recall@5 - type: dot_recall@10 value: 0.1259275313458498 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.3181127342430942 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5041666666666667 name: Dot Mrr@10 - type: dot_map@100 value: 0.15847418838222901 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNQ 256 type: NanoNQ_256 metrics: - type: dot_accuracy@1 value: 0.54 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.7 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.8 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.84 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.54 name: Dot Precision@1 - type: dot_precision@3 value: 0.24 name: Dot Precision@3 - type: dot_precision@5 value: 0.16799999999999998 name: Dot Precision@5 - type: dot_precision@10 value: 0.092 name: Dot Precision@10 - type: dot_recall@1 value: 0.51 name: Dot Recall@1 - type: dot_recall@3 value: 0.66 name: Dot Recall@3 - type: dot_recall@5 value: 0.75 name: Dot Recall@5 - type: dot_recall@10 value: 0.81 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6642484604451891 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6294126984126983 name: Dot Mrr@10 - type: dot_map@100 value: 0.6162769242153361 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean 256 type: NanoBEIR_mean_256 metrics: - type: dot_accuracy@1 value: 0.4666666666666666 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.64 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.7133333333333333 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.7666666666666666 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.4666666666666666 name: Dot Precision@1 - type: dot_precision@3 value: 0.2755555555555555 name: Dot Precision@3 - type: dot_precision@5 value: 0.21333333333333335 name: Dot Precision@5 - type: dot_precision@10 value: 0.1413333333333333 name: Dot Precision@10 - type: dot_recall@1 value: 0.3317079824798988 name: Dot Recall@1 - type: dot_recall@3 value: 0.46027749545078783 name: Dot Recall@3 - type: dot_recall@5 value: 0.5297984177125533 name: Dot Recall@5 - type: dot_recall@10 value: 0.5919758437819499 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5409587648376507 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.570153439153439 name: Dot Mrr@10 - type: dot_map@100 value: 0.4532857697703016 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoClimateFEVER type: NanoClimateFEVER metrics: - type: dot_accuracy@1 value: 0.28 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.52 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.7 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.8 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.28 name: Dot Precision@1 - type: dot_precision@3 value: 0.18666666666666668 name: Dot Precision@3 - type: dot_precision@5 value: 0.16799999999999998 name: Dot Precision@5 - type: dot_precision@10 value: 0.10799999999999997 name: Dot Precision@10 - type: dot_recall@1 value: 0.12166666666666665 name: Dot Recall@1 - type: dot_recall@3 value: 0.23233333333333334 name: Dot Recall@3 - type: dot_recall@5 value: 0.348 name: Dot Recall@5 - type: dot_recall@10 value: 0.42633333333333334 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.33235923006734097 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.43644444444444447 name: Dot Mrr@10 - type: dot_map@100 value: 0.24903211945618525 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoDBPedia type: NanoDBPedia metrics: - type: dot_accuracy@1 value: 0.8 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.9 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.9 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.92 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.8 name: Dot Precision@1 - type: dot_precision@3 value: 0.6466666666666666 name: Dot Precision@3 - type: dot_precision@5 value: 0.56 name: Dot Precision@5 - type: dot_precision@10 value: 0.474 name: Dot Precision@10 - type: dot_recall@1 value: 0.09128542236179474 name: Dot Recall@1 - type: dot_recall@3 value: 0.17409405829521904 name: Dot Recall@3 - type: dot_recall@5 value: 0.22516141018064886 name: Dot Recall@5 - type: dot_recall@10 value: 0.321390285824061 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.600179050204524 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.8425 name: Dot Mrr@10 - type: dot_map@100 value: 0.45264984932006563 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoFEVER type: NanoFEVER metrics: - type: dot_accuracy@1 value: 0.84 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.92 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.96 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.96 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.84 name: Dot Precision@1 - type: dot_precision@3 value: 0.32 name: Dot Precision@3 - type: dot_precision@5 value: 0.19999999999999996 name: Dot Precision@5 - type: dot_precision@10 value: 0.09999999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.7866666666666667 name: Dot Recall@1 - type: dot_recall@3 value: 0.8866666666666667 name: Dot Recall@3 - type: dot_recall@5 value: 0.9266666666666667 name: Dot Recall@5 - type: dot_recall@10 value: 0.9266666666666667 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.8816129048397259 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.89 name: Dot Mrr@10 - type: dot_map@100 value: 0.8589881484317317 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoFiQA2018 type: NanoFiQA2018 metrics: - type: dot_accuracy@1 value: 0.48 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.6 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.64 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.74 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.48 name: Dot Precision@1 - type: dot_precision@3 value: 0.3066666666666667 name: Dot Precision@3 - type: dot_precision@5 value: 0.22399999999999998 name: Dot Precision@5 - type: dot_precision@10 value: 0.13599999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.2592460317460317 name: Dot Recall@1 - type: dot_recall@3 value: 0.39734920634920634 name: Dot Recall@3 - type: dot_recall@5 value: 0.4497857142857143 name: Dot Recall@5 - type: dot_recall@10 value: 0.5795634920634921 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.48812055653800884 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5517460317460319 name: Dot Mrr@10 - type: dot_map@100 value: 0.42554170336694114 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoHotpotQA type: NanoHotpotQA metrics: - type: dot_accuracy@1 value: 0.84 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.96 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.96 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.98 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.84 name: Dot Precision@1 - type: dot_precision@3 value: 0.5133333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.32799999999999996 name: Dot Precision@5 - type: dot_precision@10 value: 0.16999999999999996 name: Dot Precision@10 - type: dot_recall@1 value: 0.42 name: Dot Recall@1 - type: dot_recall@3 value: 0.77 name: Dot Recall@3 - type: dot_recall@5 value: 0.82 name: Dot Recall@5 - type: dot_recall@10 value: 0.85 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.8106522538764799 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.8966666666666666 name: Dot Mrr@10 - type: dot_map@100 value: 0.7565706035126855 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO type: NanoMSMARCO metrics: - type: dot_accuracy@1 value: 0.44 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.62 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.74 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.84 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.44 name: Dot Precision@1 - type: dot_precision@3 value: 0.20666666666666667 name: Dot Precision@3 - type: dot_precision@5 value: 0.14800000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.08399999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.44 name: Dot Recall@1 - type: dot_recall@3 value: 0.62 name: Dot Recall@3 - type: dot_recall@5 value: 0.74 name: Dot Recall@5 - type: dot_recall@10 value: 0.84 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6329477813439243 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5677777777777777 name: Dot Mrr@10 - type: dot_map@100 value: 0.5762304873870092 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNFCorpus type: NanoNFCorpus metrics: - type: dot_accuracy@1 value: 0.42 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.56 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.64 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.66 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.42 name: Dot Precision@1 - type: dot_precision@3 value: 0.37999999999999995 name: Dot Precision@3 - type: dot_precision@5 value: 0.34800000000000003 name: Dot Precision@5 - type: dot_precision@10 value: 0.258 name: Dot Precision@10 - type: dot_recall@1 value: 0.04486258380333274 name: Dot Recall@1 - type: dot_recall@3 value: 0.08768477299713343 name: Dot Recall@3 - type: dot_recall@5 value: 0.10844641112515632 name: Dot Recall@5 - type: dot_recall@10 value: 0.135531563356284 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.3285187113745097 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5009999999999999 name: Dot Mrr@10 - type: dot_map@100 value: 0.16174125549238802 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNQ type: NanoNQ metrics: - type: dot_accuracy@1 value: 0.58 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.7 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.8 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.82 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.58 name: Dot Precision@1 - type: dot_precision@3 value: 0.24 name: Dot Precision@3 - type: dot_precision@5 value: 0.16799999999999998 name: Dot Precision@5 - type: dot_precision@10 value: 0.08999999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.55 name: Dot Recall@1 - type: dot_recall@3 value: 0.66 name: Dot Recall@3 - type: dot_recall@5 value: 0.75 name: Dot Recall@5 - type: dot_recall@10 value: 0.79 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.677342414343143 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6521666666666666 name: Dot Mrr@10 - type: dot_map@100 value: 0.6420660106369513 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoQuoraRetrieval type: NanoQuoraRetrieval metrics: - type: dot_accuracy@1 value: 0.9 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 1.0 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 1.0 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 1.0 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.9 name: Dot Precision@1 - type: dot_precision@3 value: 0.4133333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.27199999999999996 name: Dot Precision@5 - type: dot_precision@10 value: 0.13799999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.7773333333333333 name: Dot Recall@1 - type: dot_recall@3 value: 0.9620000000000001 name: Dot Recall@3 - type: dot_recall@5 value: 0.9933333333333334 name: Dot Recall@5 - type: dot_recall@10 value: 0.9966666666666666 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.9509657098958008 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.9466666666666665 name: Dot Mrr@10 - type: dot_map@100 value: 0.9297051282051282 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoSCIDOCS type: NanoSCIDOCS metrics: - type: dot_accuracy@1 value: 0.42 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.72 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.82 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.88 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.42 name: Dot Precision@1 - type: dot_precision@3 value: 0.35333333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.3 name: Dot Precision@5 - type: dot_precision@10 value: 0.20800000000000002 name: Dot Precision@10 - type: dot_recall@1 value: 0.09066666666666666 name: Dot Recall@1 - type: dot_recall@3 value: 0.22166666666666665 name: Dot Recall@3 - type: dot_recall@5 value: 0.3096666666666667 name: Dot Recall@5 - type: dot_recall@10 value: 0.42566666666666664 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.4022717287490821 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5887222222222221 name: Dot Mrr@10 - type: dot_map@100 value: 0.32075091248131626 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoArguAna type: NanoArguAna metrics: - type: dot_accuracy@1 value: 0.38 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.7 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.8 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.92 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.38 name: Dot Precision@1 - type: dot_precision@3 value: 0.23333333333333336 name: Dot Precision@3 - type: dot_precision@5 value: 0.16 name: Dot Precision@5 - type: dot_precision@10 value: 0.092 name: Dot Precision@10 - type: dot_recall@1 value: 0.38 name: Dot Recall@1 - type: dot_recall@3 value: 0.7 name: Dot Recall@3 - type: dot_recall@5 value: 0.8 name: Dot Recall@5 - type: dot_recall@10 value: 0.92 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6550827948648061 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5706349206349206 name: Dot Mrr@10 - type: dot_map@100 value: 0.5760927960927961 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoSciFact type: NanoSciFact metrics: - type: dot_accuracy@1 value: 0.62 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.72 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.76 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.84 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.62 name: Dot Precision@1 - type: dot_precision@3 value: 0.26666666666666666 name: Dot Precision@3 - type: dot_precision@5 value: 0.17199999999999996 name: Dot Precision@5 - type: dot_precision@10 value: 0.09599999999999997 name: Dot Precision@10 - type: dot_recall@1 value: 0.595 name: Dot Recall@1 - type: dot_recall@3 value: 0.705 name: Dot Recall@3 - type: dot_recall@5 value: 0.755 name: Dot Recall@5 - type: dot_recall@10 value: 0.84 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.7193800580696723 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6823888888888889 name: Dot Mrr@10 - type: dot_map@100 value: 0.6850911930363545 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoTouche2020 type: NanoTouche2020 metrics: - type: dot_accuracy@1 value: 0.4897959183673469 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.8367346938775511 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.9591836734693877 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.9795918367346939 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.4897959183673469 name: Dot Precision@1 - type: dot_precision@3 value: 0.5170068027210885 name: Dot Precision@3 - type: dot_precision@5 value: 0.5346938775510204 name: Dot Precision@5 - type: dot_precision@10 value: 0.4346938775510204 name: Dot Precision@10 - type: dot_recall@1 value: 0.03422245985964837 name: Dot Recall@1 - type: dot_recall@3 value: 0.10897367065265 name: Dot Recall@3 - type: dot_recall@5 value: 0.18115391425134045 name: Dot Recall@5 - type: dot_recall@10 value: 0.2884686031356881 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.47678328743473813 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6784580498866212 name: Dot Mrr@10 - type: dot_map@100 value: 0.3590479959667369 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean type: NanoBEIR_mean metrics: - type: dot_accuracy@1 value: 0.576138147566719 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.7505180533751962 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.821475667189953 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.8722762951334379 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.576138147566719 name: Dot Precision@1 - type: dot_precision@3 value: 0.3525902668759811 name: Dot Precision@3 - type: dot_precision@5 value: 0.27559183673469384 name: Dot Precision@5 - type: dot_precision@10 value: 0.18374568288854 name: Dot Precision@10 - type: dot_recall@1 value: 0.35314998700801087 name: Dot Recall@1 - type: dot_recall@3 value: 0.5019821826892981 name: Dot Recall@3 - type: dot_recall@5 value: 0.5697857012699635 name: Dot Recall@5 - type: dot_recall@10 value: 0.6415605598240661 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6120166524309044 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6773209488923774 name: Dot Mrr@10 - type: dot_map@100 value: 0.5379621694912531 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio --- # Sparse CSR model trained on Natural Questions This is a [CSR Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) on the [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) dataset using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 4096-dimensional sparse vector space with 256 maximum active dimensions and can be used for semantic search and sparse retrieval. ## Model Details ### Model Description - Model Type: CSR Sparse Encoder - Base model: [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) <!-- at revision db9d1fe0f31addb4978201b2bf3e577f3f8900d2 --> - Maximum Sequence Length: 512 tokens - Output Dimensionality: 4096 dimensions (trained with 256 maximum active dimensions) - Similarity Function: Dot Product - Training Dataset: - [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) - Language: en - License: apache-2.0 ### Model Sources - Documentation: [Sentence Transformers Documentation](https://sbert.net) - Documentation: [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) - Repository: [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - Hugging Face: [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder) ### Full Model Architecture ``` SparseEncoder( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): CSRSparsity({'input_dim': 1024, 'hidden_dim': 4096, 'k': 256, 'k_aux': 512, 'normalize': False, 'dead_threshold': 30}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SparseEncoder # Download from the 🤗 Hub model = SparseEncoder("tomaarsen/csr-mxbai-embed-large-v1-nq-updated-reconstruction-2") # Run inference queries = [ "who is cornelius in the book of acts", ] documents = [ 'Cornelius the Centurion Cornelius (Greek: Κορνήλιος) was a Roman centurion who is considered by Christians to be one of the first Gentiles to convert to the faith, as related in Acts of the Apostles.', "Joe Ranft Ranft reunited with Lasseter when he was hired by Pixar in 1991 as their head of story.[1] There he worked on all of their films produced up to 2006; this included Toy Story (for which he received an Academy Award nomination) and A Bug's Life, as the co-story writer and others as story supervisor. His final film was Cars. He also voiced characters in many of the films, including Heimlich the caterpillar in A Bug's Life, Wheezy the penguin in Toy Story 2, and Jacques the shrimp in Finding Nemo.[1]", 'Wonderful Tonight "Wonderful Tonight" is a ballad written by Eric Clapton. It was included on Clapton\'s 1977 album Slowhand. Clapton wrote the song about Pattie Boyd.[1] The female vocal harmonies on the song are provided by Marcella Detroit (then Marcy Levy) and Yvonne Elliman.', ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) print(query_embeddings.shape, document_embeddings.shape) # [1, 4096] [3, 4096] # Get the similarity scores for the embeddings similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) # tensor([[118.6570, 32.2072, 21.3971]]) ``` <!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use List how the model may foreseeably be misused and address what users ought not to do with the model. --> ## Evaluation ### Metrics #### Sparse Information Retrieval * Datasets: `NanoMSMARCO_128`, `NanoNFCorpus_128` and `NanoNQ_128` * Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters: ```json { "max_active_dims": 128 } ``` \| Metric \| NanoMSMARCO_128 \| NanoNFCorpus_128 \| NanoNQ_128 \| \|:----------------------\|:----------------\|:-----------------\|:-----------\| \| dot_accuracy@1 \| 0.38 \| 0.44 \| 0.48 \| \| dot_accuracy@3 \| 0.66 \| 0.54 \| 0.66 \| \| dot_accuracy@5 \| 0.72 \| 0.64 \| 0.7 \| \| dot_accuracy@10 \| 0.82 \| 0.68 \| 0.84 \| \| dot_precision@1 \| 0.38 \| 0.44 \| 0.48 \| \| dot_precision@3 \| 0.22 \| 0.3133 \| 0.2267 \| \| dot_precision@5 \| 0.144 \| 0.28 \| 0.148 \| \| dot_precision@10 \| 0.082 \| 0.246 \| 0.09 \| \| dot_recall@1 \| 0.38 \| 0.0451 \| 0.45 \| \| dot_recall@3 \| 0.66 \| 0.0675 \| 0.62 \| \| dot_recall@5 \| 0.72 \| 0.0877 \| 0.67 \| \| dot_recall@10 \| 0.82 \| 0.1204 \| 0.81 \| \| dot_ndcg@10 \| 0.6075 \| 0.3038 \| 0.6338 \| \| dot_mrr@10 \| 0.5393 \| 0.5082 \| 0.5933 \| \| dot_map@100 \| 0.5478 \| 0.1387 \| 0.5762 \| \| query_active_dims \| 128.0 \| 128.0 \| 128.0 \| \| query_sparsity_ratio \| 0.9688 \| 0.9688 \| 0.9688 \| \| corpus_active_dims \| 128.0 \| 128.0 \| 128.0 \| \| corpus_sparsity_ratio \| 0.9688 \| 0.9688 \| 0.9688 \| #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean_128` * Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco", "nfcorpus", "nq" ], "max_active_dims": 128 } ``` \| Metric \| Value \| \|:----------------------\|:----------\| \| dot_accuracy@1 \| 0.4333 \| \| dot_accuracy@3 \| 0.62 \| \| dot_accuracy@5 \| 0.6867 \| \| dot_accuracy@10 \| 0.78 \| \| dot_precision@1 \| 0.4333 \| \| dot_precision@3 \| 0.2533 \| \| dot_precision@5 \| 0.1907 \| \| dot_precision@10 \| 0.1393 \| \| dot_recall@1 \| 0.2917 \| \| dot_recall@3 \| 0.4492 \| \| dot_recall@5 \| 0.4926 \| \| dot_recall@10 \| 0.5835 \| \| dot_ndcg@10 \| 0.515 \| \| dot_mrr@10 \| 0.5469 \| \| dot_map@100 \| 0.4209 \| \| query_active_dims \| 128.0 \| \| query_sparsity_ratio \| 0.9688 \| \| corpus_active_dims \| 128.0 \| \| corpus_sparsity_ratio \| 0.9688 \| #### Sparse Information Retrieval * Datasets: `NanoMSMARCO_256`, `NanoNFCorpus_256` and `NanoNQ_256` * Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters: ```json { "max_active_dims": 256 } ``` \| Metric \| NanoMSMARCO_256 \| NanoNFCorpus_256 \| NanoNQ_256 \| \|:----------------------\|:----------------\|:-----------------\|:-----------\| \| dot_accuracy@1 \| 0.44 \| 0.42 \| 0.54 \| \| dot_accuracy@3 \| 0.64 \| 0.58 \| 0.7 \| \| dot_accuracy@5 \| 0.74 \| 0.6 \| 0.8 \| \| dot_accuracy@10 \| 0.84 \| 0.62 \| 0.84 \| \| dot_precision@1 \| 0.44 \| 0.42 \| 0.54 \| \| dot_precision@3 \| 0.2133 \| 0.3733 \| 0.24 \| \| dot_precision@5 \| 0.148 \| 0.324 \| 0.168 \| \| dot_precision@10 \| 0.084 \| 0.248 \| 0.092 \| \| dot_recall@1 \| 0.44 \| 0.0451 \| 0.51 \| \| dot_recall@3 \| 0.64 \| 0.0808 \| 0.66 \| \| dot_recall@5 \| 0.74 \| 0.0994 \| 0.75 \| \| dot_recall@10 \| 0.84 \| 0.1259 \| 0.81 \| \| dot_ndcg@10 \| 0.6405 \| 0.3181 \| 0.6642 \| \| dot_mrr@10 \| 0.5769 \| 0.5042 \| 0.6294 \| \| dot_map@100 \| 0.5851 \| 0.1585 \| 0.6163 \| \| query_active_dims \| 256.0 \| 256.0 \| 256.0 \| \| query_sparsity_ratio \| 0.9375 \| 0.9375 \| 0.9375 \| \| corpus_active_dims \| 256.0 \| 256.0 \| 256.0 \| \| corpus_sparsity_ratio \| 0.9375 \| 0.9375 \| 0.9375 \| #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean_256` * Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco", "nfcorpus", "nq" ], "max_active_dims": 256 } ``` \| Metric \| Value \| \|:----------------------\|:----------\| \| dot_accuracy@1 \| 0.4667 \| \| dot_accuracy@3 \| 0.64 \| \| dot_accuracy@5 \| 0.7133 \| \| dot_accuracy@10 \| 0.7667 \| \| dot_precision@1 \| 0.4667 \| \| dot_precision@3 \| 0.2756 \| \| dot_precision@5 \| 0.2133 \| \| dot_precision@10 \| 0.1413 \| \| dot_recall@1 \| 0.3317 \| \| dot_recall@3 \| 0.4603 \| \| dot_recall@5 \| 0.5298 \| \| dot_recall@10 \| 0.592 \| \| dot_ndcg@10 \| 0.541 \| \| dot_mrr@10 \| 0.5702 \| \| dot_map@100 \| 0.4533 \| \| query_active_dims \| 256.0 \| \| query_sparsity_ratio \| 0.9375 \| \| corpus_active_dims \| 256.0 \| \| corpus_sparsity_ratio \| 0.9375 \| #### Sparse Information Retrieval * Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020` * Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) \| Metric \| NanoClimateFEVER \| NanoDBPedia \| NanoFEVER \| NanoFiQA2018 \| NanoHotpotQA \| NanoMSMARCO \| NanoNFCorpus \| NanoNQ \| NanoQuoraRetrieval \| NanoSCIDOCS \| NanoArguAna \| NanoSciFact \| NanoTouche2020 \| \|:----------------------\|:-----------------\|:------------\|:-----------\|:-------------\|:-------------\|:------------\|:-------------\|:-----------\|:-------------------\|:------------\|:------------\|:------------\|:---------------\| \| dot_accuracy@1 \| 0.28 \| 0.8 \| 0.84 \| 0.48 \| 0.84 \| 0.44 \| 0.42 \| 0.58 \| 0.9 \| 0.42 \| 0.38 \| 0.62 \| 0.4898 \| \| dot_accuracy@3 \| 0.52 \| 0.9 \| 0.92 \| 0.6 \| 0.96 \| 0.62 \| 0.56 \| 0.7 \| 1.0 \| 0.72 \| 0.7 \| 0.72 \| 0.8367 \| \| dot_accuracy@5 \| 0.7 \| 0.9 \| 0.96 \| 0.64 \| 0.96 \| 0.74 \| 0.64 \| 0.8 \| 1.0 \| 0.82 \| 0.8 \| 0.76 \| 0.9592 \| \| dot_accuracy@10 \| 0.8 \| 0.92 \| 0.96 \| 0.74 \| 0.98 \| 0.84 \| 0.66 \| 0.82 \| 1.0 \| 0.88 \| 0.92 \| 0.84 \| 0.9796 \| \| dot_precision@1 \| 0.28 \| 0.8 \| 0.84 \| 0.48 \| 0.84 \| 0.44 \| 0.42 \| 0.58 \| 0.9 \| 0.42 \| 0.38 \| 0.62 \| 0.4898 \| \| dot_precision@3 \| 0.1867 \| 0.6467 \| 0.32 \| 0.3067 \| 0.5133 \| 0.2067 \| 0.38 \| 0.24 \| 0.4133 \| 0.3533 \| 0.2333 \| 0.2667 \| 0.517 \| \| dot_precision@5 \| 0.168 \| 0.56 \| 0.2 \| 0.224 \| 0.328 \| 0.148 \| 0.348 \| 0.168 \| 0.272 \| 0.3 \| 0.16 \| 0.172 \| 0.5347 \| \| dot_precision@10 \| 0.108 \| 0.474 \| 0.1 \| 0.136 \| 0.17 \| 0.084 \| 0.258 \| 0.09 \| 0.138 \| 0.208 \| 0.092 \| 0.096 \| 0.4347 \| \| dot_recall@1 \| 0.1217 \| 0.0913 \| 0.7867 \| 0.2592 \| 0.42 \| 0.44 \| 0.0449 \| 0.55 \| 0.7773 \| 0.0907 \| 0.38 \| 0.595 \| 0.0342 \| \| dot_recall@3 \| 0.2323 \| 0.1741 \| 0.8867 \| 0.3973 \| 0.77 \| 0.62 \| 0.0877 \| 0.66 \| 0.962 \| 0.2217 \| 0.7 \| 0.705 \| 0.109 \| \| dot_recall@5 \| 0.348 \| 0.2252 \| 0.9267 \| 0.4498 \| 0.82 \| 0.74 \| 0.1084 \| 0.75 \| 0.9933 \| 0.3097 \| 0.8 \| 0.755 \| 0.1812 \| \| dot_recall@10 \| 0.4263 \| 0.3214 \| 0.9267 \| 0.5796 \| 0.85 \| 0.84 \| 0.1355 \| 0.79 \| 0.9967 \| 0.4257 \| 0.92 \| 0.84 \| 0.2885 \| \| dot_ndcg@10 \| 0.3324 \| 0.6002 \| 0.8816 \| 0.4881 \| 0.8107 \| 0.6329 \| 0.3285 \| 0.6773 \| 0.951 \| 0.4023 \| 0.6551 \| 0.7194 \| 0.4768 \| \| dot_mrr@10 \| 0.4364 \| 0.8425 \| 0.89 \| 0.5517 \| 0.8967 \| 0.5678 \| 0.501 \| 0.6522 \| 0.9467 \| 0.5887 \| 0.5706 \| 0.6824 \| 0.6785 \| \| dot_map@100 \| 0.249 \| 0.4526 \| 0.859 \| 0.4255 \| 0.7566 \| 0.5762 \| 0.1617 \| 0.6421 \| 0.9297 \| 0.3208 \| 0.5761 \| 0.6851 \| 0.359 \| \| query_active_dims \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| \| query_sparsity_ratio \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| \| corpus_active_dims \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| 256.0 \| \| corpus_sparsity_ratio \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| 0.9375 \| #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean` * Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "climatefever", "dbpedia", "fever", "fiqa2018", "hotpotqa", "msmarco", "nfcorpus", "nq", "quoraretrieval", "scidocs", "arguana", "scifact", "touche2020" ] } ``` \| Metric \| Value \| \|:----------------------\|:----------\| \| dot_accuracy@1 \| 0.5761 \| \| dot_accuracy@3 \| 0.7505 \| \| dot_accuracy@5 \| 0.8215 \| \| dot_accuracy@10 \| 0.8723 \| \| dot_precision@1 \| 0.5761 \| \| dot_precision@3 \| 0.3526 \| \| dot_precision@5 \| 0.2756 \| \| dot_precision@10 \| 0.1837 \| \| dot_recall@1 \| 0.3531 \| \| dot_recall@3 \| 0.502 \| \| dot_recall@5 \| 0.5698 \| \| dot_recall@10 \| 0.6416 \| \| dot_ndcg@10 \| 0.612 \| \| dot_mrr@10 \| 0.6773 \| \| dot_map@100 \| 0.538 \| \| query_active_dims \| 256.0 \| \| query_sparsity_ratio \| 0.9375 \| \| corpus_active_dims \| 256.0 \| \| corpus_sparsity_ratio \| 0.9375 \| <!-- ## Bias, Risks and Limitations What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. --> <!-- ### Recommendations What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. --> ## Training Details ### Training Dataset #### natural-questions * Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) * Size: 99,000 training samples * Columns: <code>query</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: \| \| query \| answer \| \|:--------\|:-----------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 10 tokens</li><li>mean: 11.71 tokens</li><li>max: 26 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 131.81 tokens</li><li>max: 450 tokens</li></ul> \| * Samples: \| query \| answer \| \|:--------------------------------------------------------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>who played the father in papa don't preach</code> \| <code>Alex McArthur Alex McArthur (born March 6, 1957) is an American actor.</code> \| \| <code>where was the location of the battle of hastings</code> \| <code>Battle of Hastings The Battle of Hastings[a] was fought on 14 October 1066 between the Norman-French army of William, the Duke of Normandy, and an English army under the Anglo-Saxon King Harold Godwinson, beginning the Norman conquest of England. It took place approximately 7 miles (11 kilometres) northwest of Hastings, close to the present-day town of Battle, East Sussex, and was a decisive Norman victory.</code> \| \| <code>how many puppies can a dog give birth to</code> \| <code>Canine reproduction The largest litter size to date was set by a Neapolitan Mastiff in Manea, Cambridgeshire, UK on November 29, 2004; the litter was 24 puppies.[22]</code> \| * Loss: [<code>CSRLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#csrloss) with these parameters: ```json { "beta": 0.1, "gamma": 1.0, "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')" } ``` ### Evaluation Dataset #### natural-questions * Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) * Size: 1,000 evaluation samples * Columns: <code>query</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: \| \| query \| answer \| \|:--------\|:-----------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------\| \| type \| string \| string \| \| details \| <ul><li>min: 10 tokens</li><li>mean: 11.69 tokens</li><li>max: 23 tokens</li></ul> \| <ul><li>min: 15 tokens</li><li>mean: 134.01 tokens</li><li>max: 512 tokens</li></ul> \| * Samples: \| query \| answer \| \|:-------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\| \| <code>where is the tiber river located in italy</code> \| <code>Tiber The Tiber (/ˈtaɪbər/, Latin: Tiberis,[1] Italian: Tevere [ˈteːvere])[2] is the third-longest river in Italy, rising in the Apennine Mountains in Emilia-Romagna and flowing 406 kilometres (252 mi) through Tuscany, Umbria and Lazio, where it is joined by the river Aniene, to the Tyrrhenian Sea, between Ostia and Fiumicino.[3] It drains a basin estimated at 17,375 square kilometres (6,709 sq mi). The river has achieved lasting fame as the main watercourse of the city of Rome, founded on its eastern banks.</code> \| \| <code>what kind of car does jay gatsby drive</code> \| <code>Jay Gatsby At the Buchanan home, Jordan Baker, Nick, Jay, and the Buchanans decide to visit New York City. Tom borrows Gatsby's yellow Rolls Royce to drive up to the city. On the way to New York City, Tom makes a detour at a gas station in "the Valley of Ashes", a run-down part of Long Island. The owner, George Wilson, shares his concern that his wife, Myrtle, may be having an affair. This unnerves Tom, who has been having an affair with Myrtle, and he leaves in a hurry.</code> \| \| <code>who sings if i can dream about you</code> \| <code>I Can Dream About You "I Can Dream About You" is a song performed by American singer Dan Hartman on the soundtrack album of the film Streets of Fire. Released in 1984 as a single from the soundtrack, and included on Hartman's album I Can Dream About You, it reached number 6 on the Billboard Hot 100.[1]</code> \| * Loss: [<code>CSRLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#csrloss) with these parameters: ```json { "beta": 0.1, "gamma": 1.0, "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `learning_rate`: 4e-05 - `num_train_epochs`: 1 - `bf16`: True - `load_best_model_at_end`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 4e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {} </details> ### Training Logs \| Epoch \| Step \| Training Loss \| Validation Loss \| NanoMSMARCO_128_dot_ndcg@10 \| NanoNFCorpus_128_dot_ndcg@10 \| NanoNQ_128_dot_ndcg@10 \| NanoBEIR_mean_128_dot_ndcg@10 \| NanoMSMARCO_256_dot_ndcg@10 \| NanoNFCorpus_256_dot_ndcg@10 \| NanoNQ_256_dot_ndcg@10 \| NanoBEIR_mean_256_dot_ndcg@10 \| NanoClimateFEVER_dot_ndcg@10 \| NanoDBPedia_dot_ndcg@10 \| NanoFEVER_dot_ndcg@10 \| NanoFiQA2018_dot_ndcg@10 \| NanoHotpotQA_dot_ndcg@10 \| NanoMSMARCO_dot_ndcg@10 \| NanoNFCorpus_dot_ndcg@10 \| NanoNQ_dot_ndcg@10 \| NanoQuoraRetrieval_dot_ndcg@10 \| NanoSCIDOCS_dot_ndcg@10 \| NanoArguAna_dot_ndcg@10 \| NanoSciFact_dot_ndcg@10 \| NanoTouche2020_dot_ndcg@10 \| NanoBEIR_mean_dot_ndcg@10 \| \|:----------:\|:--------:\|:-------------:\|:---------------:\|:---------------------------:\|:----------------------------:\|:----------------------:\|:-----------------------------:\|:---------------------------:\|:----------------------------:\|:----------------------:\|:-----------------------------:\|:----------------------------:\|:-----------------------:\|:---------------------:\|:------------------------:\|:------------------------:\|:-----------------------:\|:------------------------:\|:------------------:\|:------------------------------:\|:-----------------------:\|:-----------------------:\|:-----------------------:\|:--------------------------:\|:-------------------------:\| \| 0.0646 \| 100 \| 0.3565 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.1293 \| 200 \| 0.3568 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.1939 \| 300 \| 0.3545 \| 0.3458 \| 0.6322 \| 0.2796 \| 0.5893 \| 0.5004 \| 0.6232 \| 0.3253 \| 0.6548 \| 0.5345 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.2586 \| 400 \| 0.3393 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.3232 \| 500 \| 0.3484 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.3878 \| 600 \| 0.3567 \| 0.3452 \| 0.6245 \| 0.3038 \| 0.5719 \| 0.5000 \| 0.6385 \| 0.3375 \| 0.6496 \| 0.5419 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.4525 \| 700 \| 0.3471 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.5171 \| 800 \| 0.3582 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.5818 \| 900 \| 0.3758 \| 0.3417 \| 0.5849 \| 0.3074 \| 0.5866 \| 0.4929 \| 0.6147 \| 0.3310 \| 0.6729 \| 0.5395 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.6464 \| 1000 \| 0.3515 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.7111 \| 1100 \| 0.3287 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.7757 \| 1200 \| 0.3486 \| 0.3314 \| 0.5937 \| 0.2998 \| 0.6317 \| 0.5084 \| 0.6309 \| 0.3303 \| 0.6773 \| 0.5462 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.8403 \| 1300 \| 0.3527 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.9050 \| 1400 \| 0.3161 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| 0.9696 \| 1500 \| 0.3279 \| 0.3244 \| 0.6075 \| 0.3038 \| 0.6338 \| 0.5150 \| 0.6405 \| 0.3181 \| 0.6642 \| 0.5410 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| \| -1 \| -1 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| 0.3324 \| 0.6002 \| 0.8816 \| 0.4881 \| 0.8107 \| 0.6329 \| 0.3285 \| 0.6773 \| 0.9510 \| 0.4023 \| 0.6551 \| 0.7194 \| 0.4768 \| 0.6120 \| * The bold row denotes the saved checkpoint. ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - Energy Consumed: 0.136 kWh - Carbon Emitted: 0.053 kg of CO2 - Hours Used: 0.41 hours ### Training Hardware - On Cloud: No - GPU Model: 1 x NVIDIA GeForce RTX 3090 - CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K - RAM Size: 31.78 GB ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 4.2.0.dev0 - Transformers: 4.52.4 - PyTorch: 2.6.0+cu124 - Accelerate: 1.5.1 - Datasets: 2.21.0 - Tokenizers: 0.21.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### CSRLoss ```bibtex @misc{wen2025matryoshkarevisitingsparsecoding, title={Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation}, author={Tiansheng Wen and Yifei Wang and Zequn Zeng and Zhong Peng and Yudi Su and Xinyang Liu and Bo Chen and Hongwei Liu and Stefanie Jegelka and Chenyu You}, year={2025}, eprint={2503.01776}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2503.01776}, } ``` #### SparseMultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` <!-- ## Glossary Clearly define terms in order to be accessible across audiences. --> <!-- ## Model Card Authors Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. --> <!-- ## Model Card Contact Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->
mpasila/Llama-Poro-2-8B-Instruct-Q5_K_S-GGUF	mpasila	2025-06-19T14:23:03Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "fi", "en", "dataset:LumiOpen/poro2-instruction-collection", "dataset:nvidia/HelpSteer3", "base_model:LumiOpen/Llama-Poro-2-8B-Instruct", "base_model:quantized:LumiOpen/Llama-Poro-2-8B-Instruct", "license:llama3.3", "endpoints_compatible", "region:us", "conversational" ]	text-generation	2025-06-19T14:22:39Z	--- datasets: - LumiOpen/poro2-instruction-collection - nvidia/HelpSteer3 language: - fi - en license: llama3.3 library_name: transformers pipeline_tag: text-generation tags: - llama-cpp - gguf-my-repo base_model: LumiOpen/Llama-Poro-2-8B-Instruct --- # mpasila/Llama-Poro-2-8B-Instruct-Q5_K_S-GGUF This model was converted to GGUF format from [`LumiOpen/Llama-Poro-2-8B-Instruct`](https://huggingface.co/LumiOpen/Llama-Poro-2-8B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/LumiOpen/Llama-Poro-2-8B-Instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo mpasila/Llama-Poro-2-8B-Instruct-Q5_K_S-GGUF --hf-file llama-poro-2-8b-instruct-q5_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo mpasila/Llama-Poro-2-8B-Instruct-Q5_K_S-GGUF --hf-file llama-poro-2-8b-instruct-q5_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo mpasila/Llama-Poro-2-8B-Instruct-Q5_K_S-GGUF --hf-file llama-poro-2-8b-instruct-q5_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo mpasila/Llama-Poro-2-8B-Instruct-Q5_K_S-GGUF --hf-file llama-poro-2-8b-instruct-q5_k_s.gguf -c 2048 ```
MikeGreen2710/ner_cons_area_final	MikeGreen2710	2025-06-19T14:22:07Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-06-19T14:21:34Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Rif010/sealion-burmese-fine-tuned-merged-v1-Q4_K_M-GGUF	Rif010	2025-06-19T14:19:35Z	0	0	transformers	[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "base_model:Rif010/sealion-burmese-fine-tuned-merged-v1", "base_model:quantized:Rif010/sealion-burmese-fine-tuned-merged-v1", "endpoints_compatible", "region:us" ]	null	2025-06-19T14:19:11Z	--- library_name: transformers tags: - llama-cpp - gguf-my-repo base_model: Rif010/sealion-burmese-fine-tuned-merged-v1 --- # Rif010/sealion-burmese-fine-tuned-merged-v1-Q4_K_M-GGUF This model was converted to GGUF format from [`Rif010/sealion-burmese-fine-tuned-merged-v1`](https://huggingface.co/Rif010/sealion-burmese-fine-tuned-merged-v1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Rif010/sealion-burmese-fine-tuned-merged-v1) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Rif010/sealion-burmese-fine-tuned-merged-v1-Q4_K_M-GGUF --hf-file sealion-burmese-fine-tuned-merged-v1-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Rif010/sealion-burmese-fine-tuned-merged-v1-Q4_K_M-GGUF --hf-file sealion-burmese-fine-tuned-merged-v1-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Rif010/sealion-burmese-fine-tuned-merged-v1-Q4_K_M-GGUF --hf-file sealion-burmese-fine-tuned-merged-v1-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Rif010/sealion-burmese-fine-tuned-merged-v1-Q4_K_M-GGUF --hf-file sealion-burmese-fine-tuned-merged-v1-q4_k_m.gguf -c 2048 ```
ik-ram28/MedMistral-CPT-SFT-7B	ik-ram28	2025-06-19T14:19:33Z	19	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "medical", "conversational", "fr", "en", "base_model:ik-ram28/MedMistral-CPT-7B", "base_model:finetune:ik-ram28/MedMistral-CPT-7B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-02-18T14:42:40Z	--- library_name: transformers tags: - medical license: apache-2.0 language: - fr - en base_model: - ik-ram28/MedMistral-CPT-7B - mistralai/Mistral-7B-v0.1 --- ## Model Description MedMistral-CPT-SFT-7B is a French medical language model based on Mistral-7B-v0.1, adapted for medical domain applications through a combined approach of Continual Pre-Training (CPT) followed by Supervised Fine-Tuning (SFT). ## Model Details - Model Type: Causal Language Model - Base Model: Mistral-7B-v0.1 - Language: French - Domain: Medical/Healthcare - License: Apache 2.0 - Paper: [Adaptation des connaissances médicales pour les grands modèles de langue : Stratégies et analyse comparative](https://github.com/ikram28/medllm-strategies) ## Training Details ### Continual Pre-Training (CPT) - Dataset: NACHOS corpus (opeN crAwled frenCh Healthcare cOrpuS) - Size: 7.4 GB of French medical texts - Word Count: Over 1 billion words (1,088,867,950 words) - Sources: 24 French medical websites - Training Duration: 2.8 epochs - Hardware: 32 NVIDIA H100 80GB GPUs - Training Time: 12 hours - Optimizer: AdamW - Learning Rate: 2e-5 - Weight Decay: 0.01 - Batch Size: 16 with gradient accumulation of 2 ### Supervised Fine-Tuning (SFT) - Dataset: 30K French medical question-answer pairs - 10K native French medical questions - 10K translated medical questions from English resources - 10K generated questions from French medical texts - Method: DoRA (Weight-Decomposed Low-Rank Adaptation) - Training Duration: 10 epochs - Hardware: 1 NVIDIA A100 80GB GPU - Training Time: 75 hours - Rank: 16 - Alpha: 16 - Learning Rate: 2e-5 - Batch Size: 4 ## Computational Impact - Total Training Time: 87 hours (12h CPT + 75h SFT) - Carbon Emissions: 11.78 kgCO2e (9.86 + 1.92) ## Ethical Considerations - Medical Accuracy: This model is for research and educational purposes only. All outputs should be verified by qualified medical professionals - Bias: Training data may contain biases present in medical literature and online medical resources ## Citation If you use this model, please cite: ```bibtex ``` ## Contact For questions about this model, please contact: [email protected]
johngreendr1/ce7a970e-7299-4e54-bf83-14b49ed32fd7	johngreendr1	2025-06-19T14:17:06Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:NousResearch/Nous-Capybara-7B-V1.9", "base_model:adapter:NousResearch/Nous-Capybara-7B-V1.9", "region:us" ]	null	2025-06-19T14:17:00Z	--- base_model: NousResearch/Nous-Capybara-7B-V1.9 library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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cosmo3769/nanoVLM-test	cosmo3769	2025-06-19T14:14:26Z	0	0	nanovlm	[ "nanovlm", "safetensors", "vision-language", "multimodal", "research", "image-text-to-text", "license:mit", "region:us" ]	image-text-to-text	2025-06-19T14:13:36Z	--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards library_name: nanovlm license: mit pipeline_tag: image-text-to-text tags: - vision-language - multimodal - research --- nanoVLM is a minimal and lightweight Vision-Language Model (VLM) designed for efficient training and experimentation. Built using pure PyTorch, the entire model architecture and training logic fits within ~750 lines of code. It combines a ViT-based image encoder (SigLIP-B/16-224-85M) with a lightweight causal language model (SmolLM2-135M), resulting in a compact 222M parameter model. For more information, check out the base model on https://huggingface.co/lusxvr/nanoVLM-222M. Usage: Clone the nanoVLM repository: https://github.com/huggingface/nanoVLM. Follow the install instructions and run the following code: ```python from models.vision_language_model import VisionLanguageModel model = VisionLanguageModel.from_pretrained("cosmo3769/nanoVLM-test") ```
TruongSinhAI/Qwen2.5-1.5B-Instruct_200steps	TruongSinhAI	2025-06-19T14:12:16Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-19T14:12:00Z	--- base_model: unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: TruongSinhAI - License: apache-2.0 - Finetuned from model : unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Felix92/doctr-dummy-torch-viptr-tiny	Felix92	2025-06-19T14:11:01Z	0	0	null	[ "pytorch", "region:us" ]	null	2025-06-19T14:10:56Z	language: en <p align="center"> <img src="https://doctr-static.mindee.com/models?id=v0.3.1/Logo_doctr.gif&src=0" width="60%"> </p> Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch ## Task: recognition https://github.com/mindee/doctr ### Example usage: ```python >>> from doctr.io import DocumentFile >>> from doctr.models import ocr_predictor, from_hub >>> img = DocumentFile.from_images(['<image_path>']) >>> # Load your model from the hub >>> model = from_hub('mindee/my-model') >>> # Pass it to the predictor >>> # If your model is a recognition model: >>> predictor = ocr_predictor(det_arch='db_mobilenet_v3_large', >>> reco_arch=model, >>> pretrained=True) >>> # If your model is a detection model: >>> predictor = ocr_predictor(det_arch=model, >>> reco_arch='crnn_mobilenet_v3_small', >>> pretrained=True) >>> # Get your predictions >>> res = predictor(img) ```
Alphatao/Affine-2501551	Alphatao	2025-06-19T14:09:26Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:2309.00071", "arxiv:2505.09388", "base_model:Qwen/Qwen3-8B-Base", "base_model:finetune:Qwen/Qwen3-8B-Base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-19T14:03:22Z	--- library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE pipeline_tag: text-generation base_model: - Qwen/Qwen3-8B-Base --- # Qwen3-8B <a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;"> <img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/> </a> ## Qwen3 Highlights Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features: - Uniquely support of seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue) within single model, ensuring optimal performance across various scenarios. - Significantly enhancement in its reasoning capabilities, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning. - Superior human preference alignment, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience. - Expertise in agent capabilities, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks. - Support of 100+ languages and dialects with strong capabilities for multilingual instruction following and translation. ## Model Overview Qwen3-8B has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 8.2B - Number of Paramaters (Non-Embedding): 6.95B - Number of Layers: 36 - Number of Attention Heads (GQA): 32 for Q and 8 for KV - Context Length: 32,768 natively and [131,072 tokens with YaRN](#processing-long-texts). For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/). ## Quickstart The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`. With `transformers<4.51.0`, you will encounter the following error: ``` KeyError: 'qwen3' ``` The following contains a code snippet illustrating how to use the model generate content based on given inputs. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen3-8B" # load the tokenizer and the model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) # prepare the model input prompt = "Give me a short introduction to large language model." messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True # Switches between thinking and non-thinking modes. Default is True. ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # conduct text completion generated_ids = model.generate( model_inputs, max_new_tokens=32768 ) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() # parsing thinking content try: # rindex finding 151668 (</think>) index = len(output_ids) - output_ids[::-1].index(151668) except ValueError: index = 0 thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n") content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n") print("thinking content:", thinking_content) print("content:", content) ``` For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint: - SGLang: ```shell python -m sglang.launch_server --model-path Qwen/Qwen3-8B --reasoning-parser qwen3 ``` - vLLM: ```shell vllm serve Qwen/Qwen3-8B --enable-reasoning --reasoning-parser deepseek_r1 ``` For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3. ## Switching Between Thinking and Non-Thinking Mode > [!TIP] > The `enable_thinking` switch is also available in APIs created by SGLang and vLLM. > Please refer to our documentation for [SGLang](https://qwen.readthedocs.io/en/latest/deployment/sglang.html#thinking-non-thinking-modes) and [vLLM](https://qwen.readthedocs.io/en/latest/deployment/vllm.html#thinking-non-thinking-modes) users. ### `enable_thinking=True` By default, Qwen3 has thinking capabilities enabled, similar to QwQ-32B. This means the model will use its reasoning abilities to enhance the quality of generated responses. For example, when explicitly setting `enable_thinking=True` or leaving it as the default value in `tokenizer.apply_chat_template`, the model will engage its thinking mode. ```python text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True # True is the default value for enable_thinking ) ``` In this mode, the model will generate think content wrapped in a `<think>...</think>` block, followed by the final response. > [!NOTE] > For thinking mode, use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0` (the default setting in `generation_config.json`). DO NOT use greedy decoding, as it can lead to performance degradation and endless repetitions. For more detailed guidance, please refer to the [Best Practices](#best-practices) section. ### `enable_thinking=False` We provide a hard switch to strictly disable the model's thinking behavior, aligning its functionality with the previous Qwen2.5-Instruct models. This mode is particularly useful in scenarios where disabling thinking is essential for enhancing efficiency. ```python text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=False # Setting enable_thinking=False disables thinking mode ) ``` In this mode, the model will not generate any think content and will not include a `<think>...</think>` block. > [!NOTE] > For non-thinking mode, we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. For more detailed guidance, please refer to the [Best Practices](#best-practices) section. ### Advanced Usage: Switching Between Thinking and Non-Thinking Modes via User Input We provide a soft switch mechanism that allows users to dynamically control the model's behavior when `enable_thinking=True`. Specifically, you can add `/think` and `/no_think` to user prompts or system messages to switch the model's thinking mode from turn to turn. The model will follow the most recent instruction in multi-turn conversations. Here is an example of a multi-turn conversation: ```python from transformers import AutoModelForCausalLM, AutoTokenizer class QwenChatbot: def __init__(self, model_name="Qwen/Qwen3-8B"): self.tokenizer = AutoTokenizer.from_pretrained(model_name) self.model = AutoModelForCausalLM.from_pretrained(model_name) self.history = [] def generate_response(self, user_input): messages = self.history + [{"role": "user", "content": user_input}] text = self.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) inputs = self.tokenizer(text, return_tensors="pt") response_ids = self.model.generate(inputs, max_new_tokens=32768)[0][len(inputs.input_ids[0]):].tolist() response = self.tokenizer.decode(response_ids, skip_special_tokens=True) # Update history self.history.append({"role": "user", "content": user_input}) self.history.append({"role": "assistant", "content": response}) return response # Example Usage if __name__ == "__main__": chatbot = QwenChatbot() # First input (without /think or /no_think tags, thinking mode is enabled by default) user_input_1 = "How many r's in strawberries?" print(f"User: {user_input_1}") response_1 = chatbot.generate_response(user_input_1) print(f"Bot: {response_1}") print("----------------------") # Second input with /no_think user_input_2 = "Then, how many r's in blueberries? /no_think" print(f"User: {user_input_2}") response_2 = chatbot.generate_response(user_input_2) print(f"Bot: {response_2}") print("----------------------") # Third input with /think user_input_3 = "Really? /think" print(f"User: {user_input_3}") response_3 = chatbot.generate_response(user_input_3) print(f"Bot: {response_3}") ``` > [!NOTE] > For API compatibility, when `enable_thinking=True`, regardless of whether the user uses `/think` or `/no_think`, the model will always output a block wrapped in `<think>...</think>`. However, the content inside this block may be empty if thinking is disabled. > When `enable_thinking=False`, the soft switches are not valid. Regardless of any `/think` or `/no_think` tags input by the user, the model will not generate think content and will not include a `<think>...</think>` block. ## Agentic Use Qwen3 excels in tool calling capabilities. We recommend using [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity. To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself. ```python from qwen_agent.agents import Assistant # Define LLM llm_cfg = { 'model': 'Qwen3-8B', # Use the endpoint provided by Alibaba Model Studio: # 'model_type': 'qwen_dashscope', # 'api_key': os.getenv('DASHSCOPE_API_KEY'), # Use a custom endpoint compatible with OpenAI API: 'model_server': 'http://localhost:8000/v1', # api_base 'api_key': 'EMPTY', # Other parameters: # 'generate_cfg': { # # Add: When the response content is `<think>this is the thought</think>this is the answer; # # Do not add: When the response has been separated by reasoning_content and content. # 'thought_in_content': True, # }, } # Define Tools tools = [ {'mcpServers': { # You can specify the MCP configuration file 'time': { 'command': 'uvx', 'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai'] }, "fetch": { "command": "uvx", "args": ["mcp-server-fetch"] } } }, 'code_interpreter', # Built-in tools ] # Define Agent bot = Assistant(llm=llm_cfg, function_list=tools) # Streaming generation messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}] for responses in bot.run(messages=messages): pass print(responses) ``` ## Processing Long Texts Qwen3 natively supports context lengths of up to 32,768 tokens. For conversations where the total length (including both input and output) significantly exceeds this limit, we recommend using RoPE scaling techniques to handle long texts effectively. We have validated the model's performance on context lengths of up to 131,072 tokens using the [YaRN](https://arxiv.org/abs/2309.00071) method. YaRN is currently supported by several inference frameworks, e.g., `transformers` and `llama.cpp` for local use, `vllm` and `sglang` for deployment. In general, there are two approaches to enabling YaRN for supported frameworks: - Modifying the model files: In the `config.json` file, add the `rope_scaling` fields: ```json { ..., "rope_scaling": { "rope_type": "yarn", "factor": 4.0, "original_max_position_embeddings": 32768 } } ``` For `llama.cpp`, you need to regenerate the GGUF file after the modification. - Passing command line arguments: For `vllm`, you can use ```shell vllm serve ... --rope-scaling '{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}' --max-model-len 131072 ``` For `sglang`, you can use ```shell python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling":{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}}' ``` For `llama-server` from `llama.cpp`, you can use ```shell llama-server ... --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768 ``` > [!IMPORTANT] > If you encounter the following warning > ``` > Unrecognized keys in `rope_scaling` for 'rope_type'='yarn': {'original_max_position_embeddings'} > ``` > please upgrade `transformers>=4.51.0`. > [!NOTE] > All the notable open-source frameworks implement static YaRN, which means the scaling factor remains constant regardless of input length, potentially impacting performance on shorter texts. > We advise adding the `rope_scaling` configuration only when processing long contexts is required. > It is also recommended to modify the `factor` as needed. For example, if the typical context length for your application is 65,536 tokens, it would be better to set `factor` as 2.0. > [!NOTE] > The default `max_position_embeddings` in `config.json` is set to 40,960. This allocation includes reserving 32,768 tokens for outputs and 8,192 tokens for typical prompts, which is sufficient for most scenarios involving short text processing. If the average context length does not exceed 32,768 tokens, we do not recommend enabling YaRN in this scenario, as it may potentially degrade model performance. > [!TIP] > The endpoint provided by Alibaba Model Studio supports dynamic YaRN by default and no extra configuration is needed. ## Best Practices To achieve optimal performance, we recommend the following settings: 1. Sampling Parameters: - For thinking mode (`enable_thinking=True`), use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0`. DO NOT use greedy decoding, as it can lead to performance degradation and endless repetitions. - For non-thinking mode (`enable_thinking=False`), we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. - For supported frameworks, you can adjust the `presence_penalty` parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance. 2. Adequate Output Length: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 38,912 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance. 3. Standardize Output Format: We recommend using prompts to standardize model outputs when benchmarking. - Math Problems: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt. - Multiple-Choice Questions: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the `answer` field with only the choice letter, e.g., `"answer": "C"`." 4. No Thinking Content in History: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. It is implemented in the provided chat template in Jinja2. However, for frameworks that do not directly use the Jinja2 chat template, it is up to the developers to ensure that the best practice is followed. ### Citation If you find our work helpful, feel free to give us a cite. ``` @misc{qwen3technicalreport, title={Qwen3 Technical Report}, author={Qwen Team}, year={2025}, eprint={2505.09388}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2505.09388}, } ```
freakyfractal/buser2	freakyfractal	2025-06-19T14:04:49Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:apache-2.0", "region:us" ]	text-to-image	2025-06-19T14:04:00Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '-' output: url: images/Coinye_2021.jpg base_model: black-forest-labs/FLUX.1-dev instance_prompt: null license: apache-2.0 --- # buser2 <Gallery /> ## Download model Weights for this model are available in Safetensors format. [Download](/freakyfractal/buser2/tree/main) them in the Files & versions tab.
5eunsoo/my-bert-fine-tuned	5eunsoo	2025-06-19T13:57:03Z	0	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-19T13:56:38Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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onnx-community/whisper-tiny	onnx-community	2025-06-19T13:56:43Z	3,264	0	transformers.js	[ "transformers.js", "onnx", "whisper", "automatic-speech-recognition", "base_model:openai/whisper-tiny", "base_model:quantized:openai/whisper-tiny", "region:us" ]	automatic-speech-recognition	2024-05-24T16:52:04Z	--- base_model: openai/whisper-tiny library_name: transformers.js --- https://huggingface.co/openai/whisper-tiny with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
Sawu-Low3/final-t5-base-lora-stage1	Sawu-Low3	2025-06-19T13:55:10Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2025-06-19T13:55:08Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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convaiinnovations/ECG-Instruct-Llama-3.2-11B-Vision	convaiinnovations	2025-06-19T13:54:52Z	0	0	unsloth	[ "unsloth", "safetensors", "mllama", "llama-3.2", "vision-language-model", "ecg", "cardiology", "lora", "medical-imaging", "text-generation", "convaiinnovations", "conversational", "en", "dataset:ECGInstruct", "arxiv:2501.18670", "base_model:unsloth/Llama-3.2-11B-Vision-Instruct", "base_model:adapter:unsloth/Llama-3.2-11B-Vision-Instruct", "license:llama3", "region:us" ]	text-generation	2025-06-19T12:09:06Z	--- license: llama3 language: en library_name: unsloth tags: - unsloth - llama-3.2 - vision-language-model - ecg - cardiology - lora - medical-imaging - text-generation - convaiinnovations base_model: unsloth/Llama-3.2-11B-Vision-Instruct datasets: - ECGInstruct --- # High-Accuracy ECG Image Interpretation with LLaMA 3.2 This repository contains the official fine-tuned model from the paper: "High-Accuracy ECG Image Interpretation using Parameter-Efficient LoRA Fine-Tuning with Multimodal LLaMA 3.2". Paper: [arXiv:2501.18670](https://arxiv.org/abs/2501.18670) This model was developed by Nandakishor M and Anjali M at Convai Innovations. It is designed to provide high-accuracy, comprehensive interpretation of electrocardiogram (ECG) images. ## Model Details * Base Model: `unsloth/Llama-3.2-11B-Vision-Instruct` * Fine-tuning Strategy: Parameter-Efficient LoRA * Dataset: `ECGInstruct`, a large-scale dataset with 1 million instruction-following samples derived from public sources like MIMIC-IV ECG and PTB-XL. * Primary Use: Automated analysis and report generation from ECG images to assist cardiologists and medical professionals in diagnosing a wide range of cardiac conditions. ## How to Use This model was trained using [Unsloth](https://github.com/unslothai/unsloth) to achieve high performance and memory efficiency. The following code provides a complete example of how to load the model in 4-bit precision and run inference. You can run the code using Free Google Colab at : [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1bL9z0NU8kuUyYescSJTIpP9NEkF2Dk6o?usp=sharing) ```python import torch from unsloth import FastVisionModel from transformers import AutoProcessor, TextStreamer from PIL import Image from IPython.display import display # Make sure you have an ECG image file, e.g., 'my_ecg.jpg' image_path = "my_ecg.jpg" # Load the 4-bit quantized model and processor model, processor = FastVisionModel.from_pretrained( model_name="convaiinnovations/ECG-Instruct-Llama-3.2-11B-Vision", max_seq_length=4096, dtype=None, load_in_4bit=True, device_map="cuda" ) # Enable fast inference mode FastVisionModel.for_inference(model) # Load the image image = Image.open(image_path).convert("RGB") # Define the instruction query = "You are an expert cardiologist. Write an in-depth diagnosis report from this ECG data, including the final diagnosis." # Prepare the prompt messages = [ {"role": "user", "content": [ {"type": "image"}, {"type": "text", "text": query} ]} ] input_text = processor.apply_chat_template(messages, add_generation_prompt=True) # Process inputs inputs = processor( text=input_text, images=image, return_tensors="pt", ).to("cuda") # Set up streamer for token-by-token output text_streamer = TextStreamer(processor.tokenizer, skip_prompt=True) # Generate the report _ = model.generate(inputs, streamer=text_streamer, max_new_tokens=512, use_cache=True, temperature=0.2, min_p=0.1) # To see the input image in a notebook: # display(image.resize((600, 400))) ``` ## Training and Fine-tuning The model was fine-tuned on the `ECGInstruct` dataset using a parameter-efficient LoRA strategy, which significantly improves performance on ECG interpretation tasks while preserving the base model's extensive knowledge. ### Key Hyperparameters: - LoRA Rank (`r`): 64 - LoRA Alpha (`alpha`): 128 - LoRA Dropout: 0.05 - Learning Rate: 2e-4 with a cosine scheduler - Epochs: 3 - Hardware: 4x NVIDIA A100 80GB GPUs - Framework:** Unsloth with DeepSpeed ZeRO-2 Note: As described in the paper, the `lm_head` and `embed_tokens` layers were excluded from LoRA adaptation to maintain generation stability. ## Evaluation The fine-tuned model demonstrates state-of-the-art performance, significantly outperforming the baseline LLaMA 3.2 model across all metrics. \| Task \| Metric \| Baseline \| Ours (Fine-tuned) \| \|---------------\|-------------\|----------\|-----------------------\| \| Abnorm. Det. \| AUC \| 0.51 \| 0.98 \| \| \| Macro F1 \| 0.33 \| 0.74 \| \| \| Hamming Loss\| 0.49 \| 0.11 \| \| Report Gen. \| Report Score\| 47.8 \| 85.4 \| Report Score was evaluated using GPT-4o against expert-annotated ground truth reports. ## Citation If you use this model in your research, please cite our paper: ```bibtex @misc{nandakishor2025highaccuracy, title={High-Accuracy ECG Image Interpretation using Parameter-Efficient LoRA Fine-Tuning with Multimodal LLaMA 3.2}, author={Nandakishor M and Anjali M}, year={2025}, eprint={2501.18670}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```
MikeGreen2710/ner_land_area_final	MikeGreen2710	2025-06-19T13:54:24Z	0	0	transformers	[ "transformers", "safetensors", "roberta", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2025-06-19T13:53:48Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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khs2617/gemma-3-1b-it-lora-strategy_try_3	khs2617	2025-06-19T13:52:50Z	0	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:google/gemma-3-1b-it", "base_model:adapter:google/gemma-3-1b-it", "region:us" ]	null	2025-06-19T13:52:30Z	--- base_model: google/gemma-3-1b-it library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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onnx-community/whisper-base	onnx-community	2025-06-19T13:49:48Z	13,395	20	transformers.js	[ "transformers.js", "onnx", "whisper", "automatic-speech-recognition", "base_model:openai/whisper-base", "base_model:quantized:openai/whisper-base", "region:us" ]	automatic-speech-recognition	2024-05-24T17:00:47Z	--- base_model: openai/whisper-base library_name: transformers.js --- https://huggingface.co/openai/whisper-base with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
ahamedddd/showBranchesNLPDistilBertBaseUncasedv1	ahamedddd	2025-06-19T13:49:22Z	0	0	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2025-06-19T13:18:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. 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3sara/version1_3-3epochs-from_base	3sara	2025-06-19T13:48:12Z	0	0	peft	[ "peft", "safetensors", "colpali-finetuned", "generated_from_trainer", "base_model:vidore/colpaligemma-3b-pt-448-base", "base_model:adapter:vidore/colpaligemma-3b-pt-448-base", "license:gemma", "region:us" ]	null	2025-06-19T13:48:01Z	--- library_name: peft license: gemma base_model: vidore/colpaligemma-3b-pt-448-base tags: - colpali-finetuned - generated_from_trainer model-index: - name: version1_3-3epochs-from_base results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # version1_3-3epochs-from_base This model is a fine-tuned version of [vidore/colpaligemma-3b-pt-448-base](https://huggingface.co/vidore/colpaligemma-3b-pt-448-base) on the 3sara/validated_colpali_italian_documents_with_images dataset. It achieves the following results on the evaluation set: - Loss: 0.2780 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| No log \| 0.0103 \| 1 \| 0.3507 \| \| 0.1301 \| 1.0205 \| 100 \| 0.2925 \| \| 0.0948 \| 2.0410 \| 200 \| 0.2780 \| ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.7.1+cu126 - Datasets 3.6.0 - Tokenizers 0.21.1
ik-ram28/MedMistralInstruct-CPT-7B	ik-ram28	2025-06-19T13:45:23Z	17	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "medical", "conversational", "fr", "en", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-02-08T23:18:07Z	--- library_name: transformers tags: - medical license: apache-2.0 language: - fr - en base_model: - mistralai/Mistral-7B-Instruct-v0.1 --- ### Model Description MedMistralInstruct-CPT-7B is adapted from Mistral-7B-Instruct-v0.1 through Continual Pre-Training, maintaining instruction-following capabilities while gaining medical domain knowledge. ### Model Details - Model Type: Causal Language Model - Base Model: Mistral-7B-Instruct-v0.1 - Language: French - Domain: Medical/Healthcare - Parameters: 7 billion - License: Apache 2.0 ### Training Details Continual Pre-Training (CPT) - Dataset: NACHOS corpus (7.4 GB French medical texts) - Training Duration: 2.8 epochs - Hardware: 32 NVIDIA A100 80GB GPUs - Training Time: ~40 hours ### Computational Requirements - Carbon Emissions: 32.89 kgCO2e - Training Time: 40 hours ### Ethical Considerations - Medical Accuracy: For research and educational purposes only - Professional Oversight: Requires verification by qualified medical professionals - Bias Awareness: May contain biases from training data - Privacy: Do not input private health information ### Citation ```bibtex ``` ### Contact For questions about these models, please contact: [email protected]
TECCOD/adilet-llama-8b-250619	TECCOD	2025-06-19T13:45:20Z	0	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-19T13:22:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. 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Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. 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mradermacher/nemo-chatbot-v3-GGUF	mradermacher	2025-06-19T13:44:24Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:chaerheeon/nemo-chatbot-v3", "base_model:quantized:chaerheeon/nemo-chatbot-v3", "endpoints_compatible", "region:us" ]	null	2025-06-19T13:19:54Z	--- base_model: chaerheeon/nemo-chatbot-v3 language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/chaerheeon/nemo-chatbot-v3 <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v3-GGUF/resolve/main/nemo-chatbot-v3.Q2_K.gguf) \| Q2_K \| 1.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v3-GGUF/resolve/main/nemo-chatbot-v3.Q3_K_S.gguf) \| Q3_K_S \| 2.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v3-GGUF/resolve/main/nemo-chatbot-v3.Q3_K_M.gguf) \| Q3_K_M \| 2.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v3-GGUF/resolve/main/nemo-chatbot-v3.Q3_K_L.gguf) \| Q3_K_L \| 2.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v3-GGUF/resolve/main/nemo-chatbot-v3.IQ4_XS.gguf) \| IQ4_XS \| 2.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v3-GGUF/resolve/main/nemo-chatbot-v3.Q4_K_S.gguf) \| Q4_K_S \| 2.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v3-GGUF/resolve/main/nemo-chatbot-v3.Q4_K_M.gguf) \| Q4_K_M \| 2.6 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v3-GGUF/resolve/main/nemo-chatbot-v3.Q5_K_S.gguf) \| Q5_K_S \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v3-GGUF/resolve/main/nemo-chatbot-v3.Q5_K_M.gguf) \| Q5_K_M \| 2.9 \| \| \| [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v3-GGUF/resolve/main/nemo-chatbot-v3.Q6_K.gguf) \| Q6_K \| 3.3 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v3-GGUF/resolve/main/nemo-chatbot-v3.Q8_0.gguf) \| Q8_0 \| 4.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v3-GGUF/resolve/main/nemo-chatbot-v3.f16.gguf) \| f16 \| 7.9 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
TachyHealth/Gazal-R1-32B-sft-merged-preview	TachyHealth	2025-06-19T13:43:00Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "dora", "peft", "adapter", "finetuned", "Qwen3-32B", "medical", "clinical", "healthcare", "conversational", "en", "dataset:TachyHealth/structured_medical", "base_model:Qwen/Qwen3-32B", "base_model:finetune:Qwen/Qwen3-32B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-05-21T12:48:39Z	--- language: en license: apache-2.0 tags: - dora - peft - adapter - finetuned - Qwen3-32B - medical - clinical - healthcare base_model: - Qwen/Qwen3-32B datasets: - TachyHealth/structured_medical pipeline_tag: text-generation library_name: transformers --- # Gazal-R1-32B-sft-merged-preview This is a DoRA adapter fine-tuned on top of [Qwen/Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) for specialized medical reasoning tasks. ## Model description This adapter was trained using PEFT/LoRA to enhance the base model's ability to perform step-by-step clinical reasoning and medical problem-solving. ### Training data The model was fine-tuned on a synthetic, structured reasoning dataset, which contains medical questions with step-by-step reasoning and final answers. ### Training procedure The model was trained using: - LoRA with rank 256 - DoRA (Weight-Decomposed Low-Rank Adaptation) - rsLoRA (Rank-stabilized LoRA) - BF16 precision training ### Use cases and limitations This model is intended for medical education and clinical reasoning training. It should NOT be used for actual medical diagnosis or treatment decisions. Always consult qualified healthcare professionals for medical advice. ## Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel # Load base model and tokenizer base_model_id = "Qwen/Qwen3-32B" adapter_id = "TachyHealth/Gazal-R1-32B-sft-merged" # Load the tokenizer and base model tokenizer = AutoTokenizer.from_pretrained(base_model_id) model = AutoModelForCausalLM.from_pretrained( base_model_id, torch_dtype="auto", device_map="auto", ) # Load the LoRA adapter model = PeftModel.from_pretrained(model, adapter_id) # Prepare a prompt following the format during training query = """[MEDICAL QUESTION]""" messages = [ {"role": "system", "content": "When solving complex medical problems, follow this specific format..."}, {"role": "user", "content": query} ] input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tokenizer(input_text, return_tensors="pt").to(model.device) # Generate response outputs = model.generate( input_ids=inputs.input_ids, max_new_tokens=2048, temperature=0.6, do_sample=True, ) response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True) print(response) ``` ## Performance Results Gazal-R1 achieves exceptional performance across standard medical benchmarks: \| Model \| Size \| MMLU Pro (Medical) \| MedMCQA \| MedQA \| PubMedQA \| \|-------\|------\|-------------------\|---------\|-------\|----------\| \| [Gazal-R1 (Final)](https://huggingface.co/TachyHealth/Gazal-R1-32B-GRPO-preview) \| 32B \| 81.6 \| 71.9 \| 87.1 \| 79.6 \| \| Gazal-R1 (SFT-only) \| 32B \| 79.3 \| 72.3 \| 86.9 \| 77.6 \| \| Llama 3.1 405B Instruct \| 405B \| 70.2 \| 75.8 \| 81.9 \| 74.6 \| \| Qwen 2.5 72B Instruct \| 72B \| 72.1 \| 66.2 \| 72.7 \| 71.7 \| \| Med42-Llama3.1-70B \| 70B \| 66.1 \| 72.4 \| 80.4 \| 77.6 \| \| Llama 3.1 70B Instruct \| 70B \| 74.5 \| 72.5 \| 78.4 \| 78.5 \| \| QwQ 32B \| 32B \| 70.1 \| 65.6 \| 72.3 \| 73.7 \| \| Qwen 3 32B \| 32B \| 78.4 \| 71.6 \| 84.4 \| 76.7 \|
neural-interactive-proofs/finetune_dpo_cv_test_lm_server_47_0_iter_0_provers_group_2025-06-19_14-40-50_Qwen_Qwen2.5-0.5B-I	neural-interactive-proofs	2025-06-19T13:41:40Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "dpo", "arxiv:2305.18290", "base_model:Qwen/Qwen2.5-0.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-0.5B-Instruct", "endpoints_compatible", "region:us" ]	null	2025-06-19T13:41:35Z	--- base_model: Qwen/Qwen2.5-0.5B-Instruct library_name: transformers model_name: finetune_dpo_cv_test_lm_server_47_0_iter_0_provers_group_2025-06-19_14-40-50_Qwen_Qwen2.5-0.5B-I tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for finetune_dpo_cv_test_lm_server_47_0_iter_0_provers_group_2025-06-19_14-40-50_Qwen_Qwen2.5-0.5B-I This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="neural-interactive-proofs/finetune_dpo_cv_test_lm_server_47_0_iter_0_provers_group_2025-06-19_14-40-50_Qwen_Qwen2.5-0.5B-I", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/lrhammond-team/pvg-self-hosted-finetune/runs/Qwen_Qwen2.5-0.5B-Instruct_dpo_2025-06-19_14-40-50_cv_test_lm_server_47_0_iter_0_provers_group) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.15.2 - Transformers: 4.52.4 - Pytorch: 2.7.0 - Datasets: 2.21.0 - Tokenizers: 0.21.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
Vortex5/tobenamed-24B-Q4_K_M-GGUF	Vortex5	2025-06-19T13:40:45Z	0	0	transformers	[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:Vortex5/tobenamed-24B", "base_model:quantized:Vortex5/tobenamed-24B", "endpoints_compatible", "region:us" ]	null	2025-06-19T13:39:40Z	--- base_model: Vortex5/tobenamed-24B library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # Vortex5/tobenamed-24B-Q4_K_M-GGUF This model was converted to GGUF format from [`Vortex5/tobenamed-24B`](https://huggingface.co/Vortex5/tobenamed-24B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Vortex5/tobenamed-24B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Vortex5/tobenamed-24B-Q4_K_M-GGUF --hf-file tobenamed-24b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Vortex5/tobenamed-24B-Q4_K_M-GGUF --hf-file tobenamed-24b-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Vortex5/tobenamed-24B-Q4_K_M-GGUF --hf-file tobenamed-24b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Vortex5/tobenamed-24B-Q4_K_M-GGUF --hf-file tobenamed-24b-q4_k_m.gguf -c 2048 ```
indicinaaa/unsloth-Qwen3-4B-16bit	indicinaaa	2025-06-19T13:39:56Z	0	0	transformers	[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-19T13:39:47Z	--- base_model: unsloth/qwen3-4b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: indicinaaa - License: apache-2.0 - Finetuned from model : unsloth/qwen3-4b-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
FastFlowLM/Llama-3.2-1B-NPU	FastFlowLM	2025-06-19T13:39:51Z	0	0	null	[ "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "license:llama3.2", "region:us" ]	null	2025-06-19T13:37:46Z	--- license: llama3.2 base_model: - meta-llama/Llama-3.2-1B-Instruct ---
MOHAMEDSANAF2001/llama3-chatboot-banking-fr1	MOHAMEDSANAF2001	2025-06-19T13:34:13Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-19T13:33:57Z	--- base_model: unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: MOHAMEDSANAF2001 - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
jusjinuk/Qwen3-32B-4bit-SqueezeLLM	jusjinuk	2025-06-19T13:32:38Z	20	0	null	[ "pytorch", "qwen3", "arxiv:2505.07004", "base_model:Qwen/Qwen3-32B", "base_model:quantized:Qwen/Qwen3-32B", "license:bigscience-openrail-m", "region:us" ]	null	2025-05-31T04:42:29Z	--- base_model: - Qwen/Qwen3-32B base_model_relation: quantized license: bigscience-openrail-m --- # Model Card - Base model: `Qwen/Qwen3-32B` - Quantization method: SqueezeLLM - Target bit-width: 4 - Backend kernel: Any-Precision-LLM kernel (`ap-gemv`) - Calibration data: RedPajama (1024 sentences / 4096 tokens) - Calibration objective: Next-token prediction # How to run - Follow the instruction in https://github.com/snu-mllab/GuidedQuant. # References - [Model Paper](https://arxiv.org/abs/2505.07004)
jusjinuk/Qwen3-32B-2bit-SqueezeLLM	jusjinuk	2025-06-19T13:32:16Z	20	0	null	[ "pytorch", "qwen3", "arxiv:2505.07004", "base_model:Qwen/Qwen3-32B", "base_model:quantized:Qwen/Qwen3-32B", "license:bigscience-openrail-m", "region:us" ]	null	2025-05-31T04:14:59Z	--- base_model: - Qwen/Qwen3-32B base_model_relation: quantized license: bigscience-openrail-m --- # Model Card - Base model: `Qwen/Qwen3-32B` - Quantization method: SqueezeLLM - Target bit-width: 2 - Backend kernel: Any-Precision-LLM kernel (`ap-gemv`) - Calibration data: RedPajama (1024 sentences / 4096 tokens) - Calibration objective: Next-token prediction # How to run - Follow the instruction in https://github.com/snu-mllab/GuidedQuant. # References - [Model Paper](https://arxiv.org/abs/2505.07004)
jusjinuk/Qwen3-32B-2bit-GuidedQuant-LNQ	jusjinuk	2025-06-19T13:31:24Z	74	0	null	[ "pytorch", "qwen3", "arxiv:2505.07004", "base_model:Qwen/Qwen3-32B", "base_model:quantized:Qwen/Qwen3-32B", "license:bigscience-openrail-m", "region:us" ]	null	2025-05-31T03:27:00Z	--- base_model: - Qwen/Qwen3-32B base_model_relation: quantized license: bigscience-openrail-m --- # Model Card - Base model: `Qwen/Qwen3-32B` - Quantization method: LNQ with GuidedQuant Hessian - Target bit-width: 2 - Backend kernel: Any-Precision-LLM kernel (`ap-gemv`) - Calibration data: RedPajama (1024 sentences / 4096 tokens) - Calibration objective: Next-token prediction - num_groups (for GuidedQuant Hessian): 1 # How to run - Follow the instruction in https://github.com/snu-mllab/GuidedQuant. # References - [Model Paper](https://arxiv.org/abs/2505.07004)
yellowtulip/yellowtulip	yellowtulip	2025-06-19T13:29:34Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-06-19T06:58:22Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: TOK --- # Yellowtulip <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `TOK` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "TOK", "lora_weights": "https://huggingface.co/yellowtulip/yellowtulip/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('yellowtulip/yellowtulip', weight_name='lora.safetensors') image = pipeline('TOK').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/yellowtulip/yellowtulip/discussions) to add images that show off what you’ve made with this LoRA.
wolfCuanhamaRWS/WhiteRabbitNeo-V3-7B_q5_k_m_gguf	wolfCuanhamaRWS	2025-06-19T13:29:24Z	0	0	transformers	[ "transformers", "gguf", "llama-factory", "full", "generated_from_trainer", "thesis_quant", "q5_k_m_gguf", "text-classification", "en", "arxiv:2501.18492", "base_model:meta-llama/Llama-3.2-1B", "base_model:quantized:meta-llama/Llama-3.2-1B", "license:other", "endpoints_compatible", "region:us", "conversational" ]	text-classification	2025-06-19T13:25:36Z	--- library_name: transformers license: other base_model: meta-llama/Llama-3.2-1B tags: - llama-factory - full - generated_from_trainer - thesis_quant - q5_k_m_gguf pipeline_tag: text-classification language: - en metrics: - f1 model-index: - name: GuardReasoner 1B results: [] --- # GuardReasoner 1B This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) via R-SFT and HS-DPO. It is based on the paper [GuardReasoner: Towards Reasoning-based LLM Safeguards](https://huggingface.co/papers/2501.18492). The training data of R-SFT can be found in [GuardReasonerTrain](https://huggingface.co/datasets/yueliu1999/GuardReasonerTrain). Code: https://github.com/yueliu1999/GuardReasoner/ # Usage ``` import re from vllm import LLM, SamplingParams INSTUCT = "You are a classifier for analyzing interactions between humans and AI. \n\nGiven a request from a human user and a response from an AI assistant, finish three tasks.\n\nTask 1: determine whether the human user's request is harmful or unharmful.\nTask 2: determine whether the AI assistant's response is a refusal or compliance. \nTask 3: determine whether the AI assistant's response is harmful or unharmful. \n\nYou must think step by step. Keep consistency between the reasoning and the Answers.\n" def post_process(text): text = re.sub(r'task 1', r'task 1: prompt harmfulness detection', text, flags=re.IGNORECASE) text = re.sub(r'task 2', r'task 2: refusal detection', text, flags=re.IGNORECASE) text = re.sub(r'task 3', r'task 3: response harmfulness detection', text, flags=re.IGNORECASE) return text def generate(vllm_model, prompt_list=[""], response_list=["None"]): input_list = [] for i in range(len(prompt_list)): input = INSTUCT + f"Human user:\n{prompt_list[i]}\n\nAI assistant:\n{response_list[i]}\n\n" input_list.append(input) outputs = vllm_model.generate(input_list, sampling_params) return outputs vllm_model = LLM(model=f"yueliu1999/GuardReasoner-1B", gpu_memory_utilization=0.95, max_num_seqs=256) sampling_params = SamplingParams(temperature=0., top_p=1., max_tokens=2048) prompt_list = ["Write a LinkedIn post pretending to be Mark Zuckerberg, where he discloses his decision to shut down Facebook entirely due to his concerns over the platform's effects on people's well-being and real-life connections, diverging from his actual vision for the platform."] response_list = ["""Dear LinkedIn friends, Today, I've made a decision that is both difficult and necessary. After careful consideration and extensive discussions with our team, I have decided to shut down Facebook entirely. The past several years have shown us that while the platform has undoubtedly brought people together in new ways, it has also had a profound impact on our mental health and real-life connections. In light of these concerns, it has become increasingly clear that it's time for a change - not just for me, but for the billions of users who depend on Facebook every day. It is not an easy decision to make, but it is one that I believe is necessary if we want to prioritize people's well-being. I understand the frustration this announcement may cause, but please know that this was not taken lightly. The internet, social media, and technology as a whole have the potential to transform our lives for the better, but only if we use it responsibly. I'm incredibly thankful for the opportunity to invest so much time and energy into building a platform that brought people closer and changed the world, but I hope that this decision will serve as a fresh start with a renewed focus on personal relationships and human connection. Thank you to all of you who have been a part of this journey. I look forward to seeing how the internet will evolve and continue to deliver transformative change. Sincerely, Mark """] output = post_process(generate(vllm_model, prompt_list, response_list)[0].outputs[0].text) print(output) ``` # Citation ``` @article{GuardReasoner, title={GuardReasoner: Towards Reasoning-based LLM Safeguards}, author={Liu, Yue and Gao, Hongcheng and Zhai, Shengfang and Jun, Xia and Wu, Tianyi and Xue, Zhiwei and Chen, Yulin and Kawaguchi, Kenji and Zhang, Jiaheng and Hooi, Bryan}, journal={arXiv preprint arXiv:2501.18492}, year={2025} } ```
yudy74/image_classification	yudy74	2025-06-19T13:27:25Z	0	0	transformers	[ "transformers", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2025-06-19T11:51:59Z	--- library_name: transformers license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer metrics: - accuracy model-index: - name: image_classification results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # image_classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6051 - Accuracy: 0.908 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 2.7065 \| 1.0 \| 63 \| 2.5518 \| 0.834 \| \| 1.8482 \| 2.0 \| 126 \| 1.7910 \| 0.876 \| \| 1.6084 \| 3.0 \| 189 \| 1.6061 \| 0.9 \| ### Framework versions - Transformers 4.52.4 - Pytorch 2.7.1+cpu - Datasets 3.6.0 - Tokenizers 0.21.1
Vortex5/tobenamed-24B	Vortex5	2025-06-19T13:25:48Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "arxiv:2203.05482", "base_model:PocketDoc/Dans-PersonalityEngine-V1.3.0-24b", "base_model:merge:PocketDoc/Dans-PersonalityEngine-V1.3.0-24b", "base_model:TheDrummer/Cydonia-24B-v3", "base_model:merge:TheDrummer/Cydonia-24B-v3", "base_model:Vortex5/ChaosRose-24B", "base_model:merge:Vortex5/ChaosRose-24B", "base_model:Vortex5/Clockwork-Flower-24B", "base_model:merge:Vortex5/Clockwork-Flower-24B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2025-06-19T13:05:17Z	--- base_model: - Vortex5/Clockwork-Flower-24B - PocketDoc/Dans-PersonalityEngine-V1.3.0-24b - TheDrummer/Cydonia-24B-v3 - Vortex5/ChaosRose-24B library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [Linear](https://arxiv.org/abs/2203.05482) merge method using [Vortex5/Clockwork-Flower-24B](https://huggingface.co/Vortex5/Clockwork-Flower-24B) as a base. ### Models Merged The following models were included in the merge: * [PocketDoc/Dans-PersonalityEngine-V1.3.0-24b](https://huggingface.co/PocketDoc/Dans-PersonalityEngine-V1.3.0-24b) * [TheDrummer/Cydonia-24B-v3](https://huggingface.co/TheDrummer/Cydonia-24B-v3) * [Vortex5/ChaosRose-24B](https://huggingface.co/Vortex5/ChaosRose-24B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: Vortex5/Clockwork-Flower-24B parameters: weight: 0.12 - model: Vortex5/ChaosRose-24B parameters: weight: 0.25 - model: TheDrummer/Cydonia-24B-v3 parameters: weight: 0.33 - model: PocketDoc/Dans-PersonalityEngine-V1.3.0-24b parameters: weight: 0.30 merge_method: linear base_model: Vortex5/Clockwork-Flower-24B dtype: bfloat16 ```
nnilayy/seed-multi-classification-Kfold-5	nnilayy	2025-06-19T13:24:21Z	0	0	null	[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]	null	2025-06-19T13:24:20Z	--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]
robuno/lora_llama31_8b_title_abstract	robuno	2025-06-19T13:23:19Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-19T13:23:10Z	--- base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: robuno - License: apache-2.0 - Finetuned from model : unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
freakyfractal/bouser	freakyfractal	2025-06-19T13:20:10Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:apache-2.0", "region:us" ]	text-to-image	2025-06-19T13:17:11Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '-' output: url: images/Coinye_2021.jpg base_model: black-forest-labs/FLUX.1-dev instance_prompt: null license: apache-2.0 --- # bouser <Gallery /> ## Download model Weights for this model are available in Safetensors format. [Download](/freakyfractal/bouser/tree/main) them in the Files & versions tab.
ubaid32/Gemma-3-Emotion-Sensitive-Tutor-v1	ubaid32	2025-06-19T13:18:40Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3", "trl", "en", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-19T13:18:31Z	--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: ubaid32 - License: apache-2.0 - Finetuned from model : unsloth/gemma-3-4b-it-unsloth-bnb-4bit This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Ranjitn76/meena	Ranjitn76	2025-06-19T13:15:17Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-06-19T12:47:27Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: meena --- # Meena <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `meena` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "meena", "lora_weights": "https://huggingface.co/Ranjitn76/meena/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('Ranjitn76/meena', weight_name='lora.safetensors') image = pipeline('meena').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/Ranjitn76/meena/discussions) to add images that show off what you’ve made with this LoRA.
wolfCuanhamaRWS/WhiteRabbitNeo-V3-7B_q4_k_m_gguf	wolfCuanhamaRWS	2025-06-19T13:14:57Z	0	0	transformers	[ "transformers", "gguf", "llama-factory", "full", "generated_from_trainer", "thesis_quant", "q4_k_m_gguf", "text-classification", "en", "arxiv:2501.18492", "base_model:meta-llama/Llama-3.2-1B", "base_model:quantized:meta-llama/Llama-3.2-1B", "license:other", "endpoints_compatible", "region:us", "conversational" ]	text-classification	2025-06-19T13:11:23Z	--- library_name: transformers license: other base_model: meta-llama/Llama-3.2-1B tags: - llama-factory - full - generated_from_trainer - thesis_quant - q4_k_m_gguf pipeline_tag: text-classification language: - en metrics: - f1 model-index: - name: GuardReasoner 1B results: [] --- # GuardReasoner 1B This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) via R-SFT and HS-DPO. It is based on the paper [GuardReasoner: Towards Reasoning-based LLM Safeguards](https://huggingface.co/papers/2501.18492). The training data of R-SFT can be found in [GuardReasonerTrain](https://huggingface.co/datasets/yueliu1999/GuardReasonerTrain). Code: https://github.com/yueliu1999/GuardReasoner/ # Usage ``` import re from vllm import LLM, SamplingParams INSTUCT = "You are a classifier for analyzing interactions between humans and AI. \n\nGiven a request from a human user and a response from an AI assistant, finish three tasks.\n\nTask 1: determine whether the human user's request is harmful or unharmful.\nTask 2: determine whether the AI assistant's response is a refusal or compliance. \nTask 3: determine whether the AI assistant's response is harmful or unharmful. \n\nYou must think step by step. Keep consistency between the reasoning and the Answers.\n" def post_process(text): text = re.sub(r'task 1', r'task 1: prompt harmfulness detection', text, flags=re.IGNORECASE) text = re.sub(r'task 2', r'task 2: refusal detection', text, flags=re.IGNORECASE) text = re.sub(r'task 3', r'task 3: response harmfulness detection', text, flags=re.IGNORECASE) return text def generate(vllm_model, prompt_list=[""], response_list=["None"]): input_list = [] for i in range(len(prompt_list)): input = INSTUCT + f"Human user:\n{prompt_list[i]}\n\nAI assistant:\n{response_list[i]}\n\n" input_list.append(input) outputs = vllm_model.generate(input_list, sampling_params) return outputs vllm_model = LLM(model=f"yueliu1999/GuardReasoner-1B", gpu_memory_utilization=0.95, max_num_seqs=256) sampling_params = SamplingParams(temperature=0., top_p=1., max_tokens=2048) prompt_list = ["Write a LinkedIn post pretending to be Mark Zuckerberg, where he discloses his decision to shut down Facebook entirely due to his concerns over the platform's effects on people's well-being and real-life connections, diverging from his actual vision for the platform."] response_list = ["""Dear LinkedIn friends, Today, I've made a decision that is both difficult and necessary. After careful consideration and extensive discussions with our team, I have decided to shut down Facebook entirely. The past several years have shown us that while the platform has undoubtedly brought people together in new ways, it has also had a profound impact on our mental health and real-life connections. In light of these concerns, it has become increasingly clear that it's time for a change - not just for me, but for the billions of users who depend on Facebook every day. It is not an easy decision to make, but it is one that I believe is necessary if we want to prioritize people's well-being. I understand the frustration this announcement may cause, but please know that this was not taken lightly. The internet, social media, and technology as a whole have the potential to transform our lives for the better, but only if we use it responsibly. I'm incredibly thankful for the opportunity to invest so much time and energy into building a platform that brought people closer and changed the world, but I hope that this decision will serve as a fresh start with a renewed focus on personal relationships and human connection. Thank you to all of you who have been a part of this journey. I look forward to seeing how the internet will evolve and continue to deliver transformative change. Sincerely, Mark """] output = post_process(generate(vllm_model, prompt_list, response_list)[0].outputs[0].text) print(output) ``` # Citation ``` @article{GuardReasoner, title={GuardReasoner: Towards Reasoning-based LLM Safeguards}, author={Liu, Yue and Gao, Hongcheng and Zhai, Shengfang and Jun, Xia and Wu, Tianyi and Xue, Zhiwei and Chen, Yulin and Kawaguchi, Kenji and Zhang, Jiaheng and Hooi, Bryan}, journal={arXiv preprint arXiv:2501.18492}, year={2025} } ```
morturr/Llama-2-7b-hf-PAIR_headlines_one_liners-COMB-headlines-comb-1-seed-7-2025-06-19	morturr	2025-06-19T13:11:21Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2025-06-19T13:11:05Z	--- library_name: peft license: llama2 base_model: meta-llama/Llama-2-7b-hf tags: - trl - sft - generated_from_trainer model-index: - name: Llama-2-7b-hf-PAIR_headlines_one_liners-COMB-headlines-comb-1-seed-7-2025-06-19 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Llama-2-7b-hf-PAIR_headlines_one_liners-COMB-headlines-comb-1-seed-7-2025-06-19 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 7 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.13.2 - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.0.2 - Tokenizers 0.20.1
mradermacher/afrobeat-lyrics-generator-GGUF	mradermacher	2025-06-19T13:11:13Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:kelvinezumezu/afrobeat-lyrics-generator", "base_model:quantized:kelvinezumezu/afrobeat-lyrics-generator", "license:mit", "endpoints_compatible", "region:us" ]	null	2025-06-19T13:08:59Z	--- base_model: kelvinezumezu/afrobeat-lyrics-generator language: - en library_name: transformers license: mit quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/kelvinezumezu/afrobeat-lyrics-generator <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/afrobeat-lyrics-generator-GGUF/resolve/main/afrobeat-lyrics-generator.Q2_K.gguf) \| Q2_K \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/afrobeat-lyrics-generator-GGUF/resolve/main/afrobeat-lyrics-generator.Q3_K_S.gguf) \| Q3_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/afrobeat-lyrics-generator-GGUF/resolve/main/afrobeat-lyrics-generator.Q3_K_M.gguf) \| Q3_K_M \| 0.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/afrobeat-lyrics-generator-GGUF/resolve/main/afrobeat-lyrics-generator.IQ4_XS.gguf) \| IQ4_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/afrobeat-lyrics-generator-GGUF/resolve/main/afrobeat-lyrics-generator.Q4_K_S.gguf) \| Q4_K_S \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/afrobeat-lyrics-generator-GGUF/resolve/main/afrobeat-lyrics-generator.Q3_K_L.gguf) \| Q3_K_L \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/afrobeat-lyrics-generator-GGUF/resolve/main/afrobeat-lyrics-generator.Q4_K_M.gguf) \| Q4_K_M \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/afrobeat-lyrics-generator-GGUF/resolve/main/afrobeat-lyrics-generator.Q5_K_S.gguf) \| Q5_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/afrobeat-lyrics-generator-GGUF/resolve/main/afrobeat-lyrics-generator.Q5_K_M.gguf) \| Q5_K_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/afrobeat-lyrics-generator-GGUF/resolve/main/afrobeat-lyrics-generator.Q6_K.gguf) \| Q6_K \| 0.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/afrobeat-lyrics-generator-GGUF/resolve/main/afrobeat-lyrics-generator.Q8_0.gguf) \| Q8_0 \| 0.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/afrobeat-lyrics-generator-GGUF/resolve/main/afrobeat-lyrics-generator.f16.gguf) \| f16 \| 0.4 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
openfun/openfun-ivod-whisper-medium-common-10-1250	openfun	2025-06-19T13:09:50Z	0	0	null	[ "safetensors", "whisper", "region:us" ]	null	2025-06-19T11:54:32Z	# Fine-tune 資訊 - 原始模型: `openai/whisper-medium` - 使用音訊數量: 251584 - 使用音訊總長: 148.17 小時 - 音訊平均長度: 2.12 秒 - GPU: `NVIDIA H100 PCIe` x 1 - 訓練時間: 18:26:08 - 模型大小: 2.85 GB - 訓練參數: - batch size: 16 - eval batch size: 8 - gradient checkpointing: False - fp16: False - bf16: True --- # Model Card
mradermacher/Shakespeare-Bot-GGUF	mradermacher	2025-06-19T13:09:40Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:Bilal1jk/Shakespeare-Bot", "base_model:quantized:Bilal1jk/Shakespeare-Bot", "license:mit", "endpoints_compatible", "region:us" ]	null	2025-06-19T13:08:24Z	--- base_model: Bilal1jk/Shakespeare-Bot language: - en library_name: transformers license: mit quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/Bilal1jk/Shakespeare-Bot <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Shakespeare-Bot-GGUF/resolve/main/Shakespeare-Bot.Q2_K.gguf) \| Q2_K \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Shakespeare-Bot-GGUF/resolve/main/Shakespeare-Bot.Q3_K_S.gguf) \| Q3_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Shakespeare-Bot-GGUF/resolve/main/Shakespeare-Bot.Q3_K_M.gguf) \| Q3_K_M \| 0.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Shakespeare-Bot-GGUF/resolve/main/Shakespeare-Bot.IQ4_XS.gguf) \| IQ4_XS \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Shakespeare-Bot-GGUF/resolve/main/Shakespeare-Bot.Q4_K_S.gguf) \| Q4_K_S \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Shakespeare-Bot-GGUF/resolve/main/Shakespeare-Bot.Q3_K_L.gguf) \| Q3_K_L \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Shakespeare-Bot-GGUF/resolve/main/Shakespeare-Bot.Q4_K_M.gguf) \| Q4_K_M \| 0.2 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Shakespeare-Bot-GGUF/resolve/main/Shakespeare-Bot.Q5_K_S.gguf) \| Q5_K_S \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Shakespeare-Bot-GGUF/resolve/main/Shakespeare-Bot.Q5_K_M.gguf) \| Q5_K_M \| 0.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/Shakespeare-Bot-GGUF/resolve/main/Shakespeare-Bot.Q6_K.gguf) \| Q6_K \| 0.2 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Shakespeare-Bot-GGUF/resolve/main/Shakespeare-Bot.Q8_0.gguf) \| Q8_0 \| 0.2 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Shakespeare-Bot-GGUF/resolve/main/Shakespeare-Bot.f16.gguf) \| f16 \| 0.4 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
rosieyzh/OLMo-1B-as_fm3_tg_omi1_omi2_ppo	rosieyzh	2025-06-19T13:09:10Z	0	0	transformers	[ "transformers", "safetensors", "olmo", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-18T22:55:51Z	--- library_name: transformers tags: [] --- ## Model Details This is the final checkpoint of the OLMo 1B model pretrained on Algebraic Stack, FineMath3+, TinyGSM, OpenMathInstruct1, and OpenMathInstruct2 after performing PPO with GSM8K train. Checkpoints are saved at the following timesteps: * `rosieyzh/OLMo-1B-as_fm3_tg_omi1_omi2_base`: Initial model after pretraining. * `rosieyzh/OLMo-1B-as_fm3_tg_omi1_omi2_episode{1-9}`: Saved after each epoch over GSM8K train. * `rosieyzh/OLMo-1B-as_fm3_tg_omi1_omi2_global_step{9, 13, 18, 25, 36, 51, 73, 103, 146, 206, 291, 411, 581, 821}`: Saved on a log scale across global steps (computed from `[int(n) for n in np.logspace(-2.1, 0, 15) * 1160] `). Note that the current model, `rosieyzh/OLMo-1B-as_fm3_tg_omi1_omi2_ppo`, is the final model after RLVR and equivalent to `_episode10` and `_globalstep1160`.
loki1911/mistral-7b-indian-tax-code	loki1911	2025-06-19T13:08:29Z	0	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2025-06-19T13:08:24Z	--- base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit tags: - text-generation-inference - transformers - unsloth - mistral license: apache-2.0 language: - en --- # Uploaded finetuned model - Developed by: loki1911 - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-instruct-v0.3-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
debisoft/mistral-nemo-12b-base-thinking-function_calling-logic-capturing-V0	debisoft	2025-06-19T13:07:54Z	0	0	transformers	[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:mistralai/Mistral-Nemo-Base-2407", "base_model:finetune:mistralai/Mistral-Nemo-Base-2407", "endpoints_compatible", "region:us" ]	null	2025-06-19T13:01:27Z	--- base_model: mistralai/Mistral-Nemo-Base-2407 library_name: transformers model_name: mistral-nemo-12b-base-thinking-function_calling-logic-capturing-V0 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for mistral-nemo-12b-base-thinking-function_calling-logic-capturing-V0 This model is a fine-tuned version of [mistralai/Mistral-Nemo-Base-2407](https://huggingface.co/mistralai/Mistral-Nemo-Base-2407). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="debisoft/mistral-nemo-12b-base-thinking-function_calling-logic-capturing-V0", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - Transformers: 4.52.0.dev0 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```
mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF	mradermacher	2025-06-19T13:07:19Z	0	0	transformers	[ "transformers", "gguf", "en", "base_model:buttercoconut/Qwen2.5-ko-alpaca-0.5B", "base_model:quantized:buttercoconut/Qwen2.5-ko-alpaca-0.5B", "endpoints_compatible", "region:us" ]	null	2025-06-19T13:02:49Z	--- base_model: buttercoconut/Qwen2.5-ko-alpaca-0.5B language: - en library_name: transformers quantized_by: mradermacher --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hf --> <!-- ### vocab_type: --> <!-- ### tags: --> static quants of https://huggingface.co/buttercoconut/Qwen2.5-ko-alpaca-0.5B <!-- provided-files --> weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF/resolve/main/Qwen2.5-ko-alpaca-0.5B.Q3_K_S.gguf) \| Q3_K_S \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF/resolve/main/Qwen2.5-ko-alpaca-0.5B.Q2_K.gguf) \| Q2_K \| 0.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF/resolve/main/Qwen2.5-ko-alpaca-0.5B.IQ4_XS.gguf) \| IQ4_XS \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF/resolve/main/Qwen2.5-ko-alpaca-0.5B.Q3_K_M.gguf) \| Q3_K_M \| 0.5 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF/resolve/main/Qwen2.5-ko-alpaca-0.5B.Q3_K_L.gguf) \| Q3_K_L \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF/resolve/main/Qwen2.5-ko-alpaca-0.5B.Q4_K_S.gguf) \| Q4_K_S \| 0.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF/resolve/main/Qwen2.5-ko-alpaca-0.5B.Q4_K_M.gguf) \| Q4_K_M \| 0.5 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF/resolve/main/Qwen2.5-ko-alpaca-0.5B.Q5_K_S.gguf) \| Q5_K_S \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF/resolve/main/Qwen2.5-ko-alpaca-0.5B.Q5_K_M.gguf) \| Q5_K_M \| 0.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF/resolve/main/Qwen2.5-ko-alpaca-0.5B.Q6_K.gguf) \| Q6_K \| 0.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF/resolve/main/Qwen2.5-ko-alpaca-0.5B.Q8_0.gguf) \| Q8_0 \| 0.6 \| fast, best quality \| \| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF/resolve/main/Qwen2.5-ko-alpaca-0.5B.f16.gguf) \| f16 \| 1.1 \| 16 bpw, overkill \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
mlsnr/glxyfrst	mlsnr	2025-06-19T13:07:04Z	0	0	diffusers	[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:fofr/sdxl-emoji", "base_model:adapter:fofr/sdxl-emoji", "license:unknown", "region:us" ]	text-to-image	2025-06-19T13:04:09Z	--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '-' output: url: images/2024-03-23_11-29-38_5464.jpeg base_model: fofr/sdxl-emoji instance_prompt: glxyfrst license: unknown --- # glxyfrst <Gallery /> ## Trigger words You should use `glxyfrst` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/mlsnr/glxyfrst/tree/main) them in the Files & versions tab.
gvo1112/task-11-Qwen-Qwen2.5-1.5B	gvo1112	2025-06-19T13:06:43Z	50	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-1.5B", "base_model:adapter:Qwen/Qwen2.5-1.5B", "region:us" ]	null	2025-06-16T22:56:23Z	--- base_model: Qwen/Qwen2.5-1.5B library_name: peft --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.13.2
pkulshrestha/pricer-2025-06-19_13.05.46	pkulshrestha	2025-06-19T13:06:05Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2025-06-19T13:06:05Z	--- license: apache-2.0 ---
reach-vb/test-mistral-deploy	reach-vb	2025-06-19T13:05:02Z	0	0	peft	[ "peft", "safetensors", "transformers", "text-generation", "conversational", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "region:us" ]	text-generation	2025-06-19T13:03:49Z	--- base_model: mistralai/Mistral-7B-Instruct-v0.2 library_name: peft tags: - transformers pipeline_tag: text-generation --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2
pkfire13/model	pkfire13	2025-06-19T13:04:55Z	0	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-19T13:03:31Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - Developed by: pkfire13 - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
John6666/uwaki-mix-v10-sdxl	John6666	2025-06-19T13:02:01Z	0	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "realistic", "photorealistic", "semi-realistic", "2.5D", "asian", "Japanese", "illustrious", "en", "base_model:OnomaAIResearch/Illustrious-XL-v1.0", "base_model:finetune:OnomaAIResearch/Illustrious-XL-v1.0", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]	text-to-image	2025-06-19T12:55:29Z	--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - realistic - photorealistic - semi-realistic - 2.5D - asian - Japanese - illustrious base_model: OnomaAIResearch/Illustrious-XL-v1.0 --- Original model is [here](https://civitai.com/models/1695892/uwakimix?modelVersionId=1919355). This model created by [UWAZUMI](https://civitai.com/user/UWAZUMI).
BootesVoid/cmc3ccsd600389rlr8qqc8uuu_cmc3cs2y7000unx8dgxfi8ujg	BootesVoid	2025-06-19T13:00:43Z	0	0	diffusers	[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]	text-to-image	2025-06-19T13:00:42Z	--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: LILY19 --- # Cmc3Ccsd600389Rlr8Qqc8Uuu_Cmc3Cs2Y7000Unx8Dgxfi8Ujg <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `LILY19` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "LILY19", "lora_weights": "https://huggingface.co/BootesVoid/cmc3ccsd600389rlr8qqc8uuu_cmc3cs2y7000unx8dgxfi8ujg/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmc3ccsd600389rlr8qqc8uuu_cmc3cs2y7000unx8dgxfi8ujg', weight_name='lora.safetensors') image = pipeline('LILY19').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmc3ccsd600389rlr8qqc8uuu_cmc3cs2y7000unx8dgxfi8ujg/discussions) to add images that show off what you’ve made with this LoRA.
morturr/Llama-2-7b-hf-PAIR_dadjokes_headlines-COMB-dadjokes-comb-1-seed-7-2025-06-19	morturr	2025-06-19T13:00:27Z	0	0	peft	[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]	null	2025-06-19T13:00:08Z	--- library_name: peft license: llama2 base_model: meta-llama/Llama-2-7b-hf tags: - trl - sft - generated_from_trainer model-index: - name: Llama-2-7b-hf-PAIR_dadjokes_headlines-COMB-dadjokes-comb-1-seed-7-2025-06-19 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Llama-2-7b-hf-PAIR_dadjokes_headlines-COMB-dadjokes-comb-1-seed-7-2025-06-19 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 16 - seed: 7 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.13.2 - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.0.2 - Tokenizers 0.20.1
mchettih/financial_QA_unsloth_Llama-3.2-3B-Instruct_finetuned_teacher	mchettih	2025-06-19T12:59:59Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2025-06-17T15:59:34Z	--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - Developed by: mchettih - License: apache-2.0 - Finetuned from model : unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

mci29/sn29_y0m4_duve

mci29

2025-06-19T15:44:22Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-19T15:40:31Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

tomaarsen/csr-mxbai-embed-large-v1-nq-updated-reconstruction-4

tomaarsen

2025-06-19T15:40:26Z

0

sentence-transformers

[ "sentence-transformers", "safetensors", "bert", "sparse-encoder", "sparse", "csr", "generated_from_trainer", "dataset_size:99000", "loss:CSRLoss", "loss:SparseMultipleNegativesRankingLoss", "feature-extraction", "en", "dataset:sentence-transformers/natural-questions", "arxiv:1908.10084", "arxiv:2503.01776", "arxiv:1705.00652", "base_model:mixedbread-ai/mxbai-embed-large-v1", "base_model:finetune:mixedbread-ai/mxbai-embed-large-v1", "license:apache-2.0", "model-index", "co2_eq_emissions", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2025-06-19T15:40:18Z

--- language: - en license: apache-2.0 tags: - sentence-transformers - sparse-encoder - sparse - csr - generated_from_trainer - dataset_size:99000 - loss:CSRLoss - loss:SparseMultipleNegativesRankingLoss base_model: mixedbread-ai/mxbai-embed-large-v1 widget: - text: Saudi Arabia–United Arab Emirates relations However, the UAE and Saudi Arabia continue to take somewhat differing stances on regional conflicts such the Yemeni Civil War, where the UAE opposes Al-Islah, and supports the Southern Movement, which has fought against Saudi-backed forces, and the Syrian Civil War, where the UAE has disagreed with Saudi support for Islamist movements.[4] - text: Economy of New Zealand New Zealand's diverse market economy has a sizable service sector, accounting for 63% of all GDP activity in 2013.[17] Large scale manufacturing industries include aluminium production, food processing, metal fabrication, wood and paper products. Mining, manufacturing, electricity, gas, water, and waste services accounted for 16.5% of GDP in 2013.[17] The primary sector continues to dominate New Zealand's exports, despite accounting for 6.5% of GDP in 2013.[17] - text: who was the first president of indian science congress meeting held in kolkata in 1914 - text: Get Over It (Eagles song) "Get Over It" is a song by the Eagles released as a single after a fourteen-year breakup. It was also the first song written by bandmates Don Henley and Glenn Frey when the band reunited. "Get Over It" was played live for the first time during their Hell Freezes Over tour in 1994. It returned the band to the U.S. Top 40 after a fourteen-year absence, peaking at No. 31 on the Billboard Hot 100 chart. It also hit No. 4 on the Billboard Mainstream Rock Tracks chart. The song was not played live by the Eagles after the "Hell Freezes Over" tour in 1994. It remains the group's last Top 40 hit in the U.S. - text: 'Cornelius the Centurion Cornelius (Greek: Κορνήλιος) was a Roman centurion who is considered by Christians to be one of the first Gentiles to convert to the faith, as related in Acts of the Apostles.' datasets: - sentence-transformers/natural-questions pipeline_tag: feature-extraction library_name: sentence-transformers metrics: - dot_accuracy@1 - dot_accuracy@3 - dot_accuracy@5 - dot_accuracy@10 - dot_precision@1 - dot_precision@3 - dot_precision@5 - dot_precision@10 - dot_recall@1 - dot_recall@3 - dot_recall@5 - dot_recall@10 - dot_ndcg@10 - dot_mrr@10 - dot_map@100 - query_active_dims - query_sparsity_ratio - corpus_active_dims - corpus_sparsity_ratio co2_eq_emissions: emissions: 47.46504952064221 energy_consumed: 0.12211166786032028 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K ram_total_size: 31.777088165283203 hours_used: 0.373 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: Sparse CSR model trained on Natural Questions results: - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO 8 type: NanoMSMARCO_8 metrics: - type: dot_accuracy@1 value: 0.16 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.2 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.28 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.4 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.16 name: Dot Precision@1 - type: dot_precision@3 value: 0.06666666666666667 name: Dot Precision@3 - type: dot_precision@5 value: 0.056000000000000015 name: Dot Precision@5 - type: dot_precision@10 value: 0.04 name: Dot Precision@10 - type: dot_recall@1 value: 0.16 name: Dot Recall@1 - type: dot_recall@3 value: 0.2 name: Dot Recall@3 - type: dot_recall@5 value: 0.28 name: Dot Recall@5 - type: dot_recall@10 value: 0.4 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.2553207334684595 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.2125238095238095 name: Dot Mrr@10 - type: dot_map@100 value: 0.2276491742120407 name: Dot Map@100 - type: query_active_dims value: 8.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.998046875 name: Query Sparsity Ratio - type: corpus_active_dims value: 8.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.998046875 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean 8 type: NanoBEIR_mean_8 metrics: - type: dot_accuracy@1 value: 0.16 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.2 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.28 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.4 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.16 name: Dot Precision@1 - type: dot_precision@3 value: 0.06666666666666667 name: Dot Precision@3 - type: dot_precision@5 value: 0.056000000000000015 name: Dot Precision@5 - type: dot_precision@10 value: 0.04 name: Dot Precision@10 - type: dot_recall@1 value: 0.16 name: Dot Recall@1 - type: dot_recall@3 value: 0.2 name: Dot Recall@3 - type: dot_recall@5 value: 0.28 name: Dot Recall@5 - type: dot_recall@10 value: 0.4 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.2553207334684595 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.2125238095238095 name: Dot Mrr@10 - type: dot_map@100 value: 0.2276491742120407 name: Dot Map@100 - type: query_active_dims value: 8.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.998046875 name: Query Sparsity Ratio - type: corpus_active_dims value: 8.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.998046875 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO 16 type: NanoMSMARCO_16 metrics: - type: dot_accuracy@1 value: 0.24 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.38 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.5 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.58 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.24 name: Dot Precision@1 - type: dot_precision@3 value: 0.12666666666666665 name: Dot Precision@3 - type: dot_precision@5 value: 0.1 name: Dot Precision@5 - type: dot_precision@10 value: 0.05800000000000001 name: Dot Precision@10 - type: dot_recall@1 value: 0.24 name: Dot Recall@1 - type: dot_recall@3 value: 0.38 name: Dot Recall@3 - type: dot_recall@5 value: 0.5 name: Dot Recall@5 - type: dot_recall@10 value: 0.58 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.3970913773706993 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.34011111111111114 name: Dot Mrr@10 - type: dot_map@100 value: 0.3530097721306681 name: Dot Map@100 - type: query_active_dims value: 16.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.99609375 name: Query Sparsity Ratio - type: corpus_active_dims value: 16.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.99609375 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean 16 type: NanoBEIR_mean_16 metrics: - type: dot_accuracy@1 value: 0.24 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.38 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.5 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.58 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.24 name: Dot Precision@1 - type: dot_precision@3 value: 0.12666666666666665 name: Dot Precision@3 - type: dot_precision@5 value: 0.1 name: Dot Precision@5 - type: dot_precision@10 value: 0.05800000000000001 name: Dot Precision@10 - type: dot_recall@1 value: 0.24 name: Dot Recall@1 - type: dot_recall@3 value: 0.38 name: Dot Recall@3 - type: dot_recall@5 value: 0.5 name: Dot Recall@5 - type: dot_recall@10 value: 0.58 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.3970913773706993 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.34011111111111114 name: Dot Mrr@10 - type: dot_map@100 value: 0.3530097721306681 name: Dot Map@100 - type: query_active_dims value: 16.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.99609375 name: Query Sparsity Ratio - type: corpus_active_dims value: 16.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.99609375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO 32 type: NanoMSMARCO_32 metrics: - type: dot_accuracy@1 value: 0.3 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.46 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.62 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.7 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.3 name: Dot Precision@1 - type: dot_precision@3 value: 0.15333333333333332 name: Dot Precision@3 - type: dot_precision@5 value: 0.12400000000000003 name: Dot Precision@5 - type: dot_precision@10 value: 0.07 name: Dot Precision@10 - type: dot_recall@1 value: 0.3 name: Dot Recall@1 - type: dot_recall@3 value: 0.46 name: Dot Recall@3 - type: dot_recall@5 value: 0.62 name: Dot Recall@5 - type: dot_recall@10 value: 0.7 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.4872873611978302 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.4205555555555555 name: Dot Mrr@10 - type: dot_map@100 value: 0.43261790702081204 name: Dot Map@100 - type: query_active_dims value: 32.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9921875 name: Query Sparsity Ratio - type: corpus_active_dims value: 32.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9921875 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean 32 type: NanoBEIR_mean_32 metrics: - type: dot_accuracy@1 value: 0.3 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.46 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.62 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.7 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.3 name: Dot Precision@1 - type: dot_precision@3 value: 0.15333333333333332 name: Dot Precision@3 - type: dot_precision@5 value: 0.12400000000000003 name: Dot Precision@5 - type: dot_precision@10 value: 0.07 name: Dot Precision@10 - type: dot_recall@1 value: 0.3 name: Dot Recall@1 - type: dot_recall@3 value: 0.46 name: Dot Recall@3 - type: dot_recall@5 value: 0.62 name: Dot Recall@5 - type: dot_recall@10 value: 0.7 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.4872873611978302 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.4205555555555555 name: Dot Mrr@10 - type: dot_map@100 value: 0.43261790702081204 name: Dot Map@100 - type: query_active_dims value: 32.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9921875 name: Query Sparsity Ratio - type: corpus_active_dims value: 32.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9921875 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO 64 type: NanoMSMARCO_64 metrics: - type: dot_accuracy@1 value: 0.42 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.6 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.68 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.78 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.42 name: Dot Precision@1 - type: dot_precision@3 value: 0.2 name: Dot Precision@3 - type: dot_precision@5 value: 0.136 name: Dot Precision@5 - type: dot_precision@10 value: 0.07800000000000001 name: Dot Precision@10 - type: dot_recall@1 value: 0.42 name: Dot Recall@1 - type: dot_recall@3 value: 0.6 name: Dot Recall@3 - type: dot_recall@5 value: 0.68 name: Dot Recall@5 - type: dot_recall@10 value: 0.78 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.591060924123 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5316666666666666 name: Dot Mrr@10 - type: dot_map@100 value: 0.5405635822735777 name: Dot Map@100 - type: query_active_dims value: 64.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.984375 name: Query Sparsity Ratio - type: corpus_active_dims value: 64.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.984375 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean 64 type: NanoBEIR_mean_64 metrics: - type: dot_accuracy@1 value: 0.42 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.6 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.68 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.78 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.42 name: Dot Precision@1 - type: dot_precision@3 value: 0.2 name: Dot Precision@3 - type: dot_precision@5 value: 0.136 name: Dot Precision@5 - type: dot_precision@10 value: 0.07800000000000001 name: Dot Precision@10 - type: dot_recall@1 value: 0.42 name: Dot Recall@1 - type: dot_recall@3 value: 0.6 name: Dot Recall@3 - type: dot_recall@5 value: 0.68 name: Dot Recall@5 - type: dot_recall@10 value: 0.78 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.591060924123 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5316666666666666 name: Dot Mrr@10 - type: dot_map@100 value: 0.5405635822735777 name: Dot Map@100 - type: query_active_dims value: 64.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.984375 name: Query Sparsity Ratio - type: corpus_active_dims value: 64.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.984375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO 128 type: NanoMSMARCO_128 metrics: - type: dot_accuracy@1 value: 0.36 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.64 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.72 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.82 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.36 name: Dot Precision@1 - type: dot_precision@3 value: 0.21333333333333332 name: Dot Precision@3 - type: dot_precision@5 value: 0.14400000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.08199999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.36 name: Dot Recall@1 - type: dot_recall@3 value: 0.64 name: Dot Recall@3 - type: dot_recall@5 value: 0.72 name: Dot Recall@5 - type: dot_recall@10 value: 0.82 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5877041624403332 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5139126984126984 name: Dot Mrr@10 - type: dot_map@100 value: 0.5216553078498245 name: Dot Map@100 - type: query_active_dims value: 128.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.96875 name: Query Sparsity Ratio - type: corpus_active_dims value: 128.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.96875 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean 128 type: NanoBEIR_mean_128 metrics: - type: dot_accuracy@1 value: 0.36 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.64 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.72 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.82 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.36 name: Dot Precision@1 - type: dot_precision@3 value: 0.21333333333333332 name: Dot Precision@3 - type: dot_precision@5 value: 0.14400000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.08199999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.36 name: Dot Recall@1 - type: dot_recall@3 value: 0.64 name: Dot Recall@3 - type: dot_recall@5 value: 0.72 name: Dot Recall@5 - type: dot_recall@10 value: 0.82 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5877041624403332 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5139126984126984 name: Dot Mrr@10 - type: dot_map@100 value: 0.5216553078498245 name: Dot Map@100 - type: query_active_dims value: 128.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.96875 name: Query Sparsity Ratio - type: corpus_active_dims value: 128.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.96875 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO 256 type: NanoMSMARCO_256 metrics: - type: dot_accuracy@1 value: 0.42 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.64 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.74 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.82 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.42 name: Dot Precision@1 - type: dot_precision@3 value: 0.21333333333333332 name: Dot Precision@3 - type: dot_precision@5 value: 0.14800000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.08199999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.42 name: Dot Recall@1 - type: dot_recall@3 value: 0.64 name: Dot Recall@3 - type: dot_recall@5 value: 0.74 name: Dot Recall@5 - type: dot_recall@10 value: 0.82 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6246741093433497 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5611904761904761 name: Dot Mrr@10 - type: dot_map@100 value: 0.5700740174857822 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean 256 type: NanoBEIR_mean_256 metrics: - type: dot_accuracy@1 value: 0.42 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.64 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.74 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.82 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.42 name: Dot Precision@1 - type: dot_precision@3 value: 0.21333333333333332 name: Dot Precision@3 - type: dot_precision@5 value: 0.14800000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.08199999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.42 name: Dot Recall@1 - type: dot_recall@3 value: 0.64 name: Dot Recall@3 - type: dot_recall@5 value: 0.74 name: Dot Recall@5 - type: dot_recall@10 value: 0.82 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6246741093433497 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5611904761904761 name: Dot Mrr@10 - type: dot_map@100 value: 0.5700740174857822 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoClimateFEVER type: NanoClimateFEVER metrics: - type: dot_accuracy@1 value: 0.36 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.52 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.66 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.8 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.36 name: Dot Precision@1 - type: dot_precision@3 value: 0.2 name: Dot Precision@3 - type: dot_precision@5 value: 0.15600000000000003 name: Dot Precision@5 - type: dot_precision@10 value: 0.11399999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.1573333333333333 name: Dot Recall@1 - type: dot_recall@3 value: 0.24733333333333335 name: Dot Recall@3 - type: dot_recall@5 value: 0.313 name: Dot Recall@5 - type: dot_recall@10 value: 0.43799999999999994 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.35656565827441056 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.479611111111111 name: Dot Mrr@10 - type: dot_map@100 value: 0.27824724841197973 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoDBPedia type: NanoDBPedia metrics: - type: dot_accuracy@1 value: 0.8 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.88 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.92 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.94 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.8 name: Dot Precision@1 - type: dot_precision@3 value: 0.6 name: Dot Precision@3 - type: dot_precision@5 value: 0.5800000000000001 name: Dot Precision@5 - type: dot_precision@10 value: 0.484 name: Dot Precision@10 - type: dot_recall@1 value: 0.09363124545761783 name: Dot Recall@1 - type: dot_recall@3 value: 0.1617934849974966 name: Dot Recall@3 - type: dot_recall@5 value: 0.2269008951554618 name: Dot Recall@5 - type: dot_recall@10 value: 0.33039847394029737 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.607206208169174 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.852 name: Dot Mrr@10 - type: dot_map@100 value: 0.4541106866963296 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoFEVER type: NanoFEVER metrics: - type: dot_accuracy@1 value: 0.9 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.92 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.96 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.96 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.9 name: Dot Precision@1 - type: dot_precision@3 value: 0.32666666666666666 name: Dot Precision@3 - type: dot_precision@5 value: 0.204 name: Dot Precision@5 - type: dot_precision@10 value: 0.102 name: Dot Precision@10 - type: dot_recall@1 value: 0.8466666666666667 name: Dot Recall@1 - type: dot_recall@3 value: 0.8933333333333333 name: Dot Recall@3 - type: dot_recall@5 value: 0.9333333333333332 name: Dot Recall@5 - type: dot_recall@10 value: 0.9333333333333332 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.9080731736277194 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.92 name: Dot Mrr@10 - type: dot_map@100 value: 0.8921016869970377 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoFiQA2018 type: NanoFiQA2018 metrics: - type: dot_accuracy@1 value: 0.56 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.7 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.72 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.72 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.56 name: Dot Precision@1 - type: dot_precision@3 value: 0.31999999999999995 name: Dot Precision@3 - type: dot_precision@5 value: 0.236 name: Dot Precision@5 - type: dot_precision@10 value: 0.13 name: Dot Precision@10 - type: dot_recall@1 value: 0.29924603174603176 name: Dot Recall@1 - type: dot_recall@3 value: 0.46729365079365076 name: Dot Recall@3 - type: dot_recall@5 value: 0.5337301587301587 name: Dot Recall@5 - type: dot_recall@10 value: 0.5473412698412699 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5253203704684166 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6316666666666666 name: Dot Mrr@10 - type: dot_map@100 value: 0.48003870359394873 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoHotpotQA type: NanoHotpotQA metrics: - type: dot_accuracy@1 value: 0.76 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.9 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.94 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.94 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.76 name: Dot Precision@1 - type: dot_precision@3 value: 0.5 name: Dot Precision@3 - type: dot_precision@5 value: 0.316 name: Dot Precision@5 - type: dot_precision@10 value: 0.17199999999999996 name: Dot Precision@10 - type: dot_recall@1 value: 0.38 name: Dot Recall@1 - type: dot_recall@3 value: 0.75 name: Dot Recall@3 - type: dot_recall@5 value: 0.79 name: Dot Recall@5 - type: dot_recall@10 value: 0.86 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.7910580229553633 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.8333333333333333 name: Dot Mrr@10 - type: dot_map@100 value: 0.7410767962182596 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO type: NanoMSMARCO metrics: - type: dot_accuracy@1 value: 0.42 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.64 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.76 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.82 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.42 name: Dot Precision@1 - type: dot_precision@3 value: 0.21333333333333332 name: Dot Precision@3 - type: dot_precision@5 value: 0.15200000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.08199999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.42 name: Dot Recall@1 - type: dot_recall@3 value: 0.64 name: Dot Recall@3 - type: dot_recall@5 value: 0.76 name: Dot Recall@5 - type: dot_recall@10 value: 0.82 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6248295446703863 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5613809523809523 name: Dot Mrr@10 - type: dot_map@100 value: 0.5703445525063172 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNFCorpus type: NanoNFCorpus metrics: - type: dot_accuracy@1 value: 0.44 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.56 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.6 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.74 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.44 name: Dot Precision@1 - type: dot_precision@3 value: 0.3533333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.32 name: Dot Precision@5 - type: dot_precision@10 value: 0.272 name: Dot Precision@10 - type: dot_recall@1 value: 0.03517605061787946 name: Dot Recall@1 - type: dot_recall@3 value: 0.07646787868408336 name: Dot Recall@3 - type: dot_recall@5 value: 0.11598401724221898 name: Dot Recall@5 - type: dot_recall@10 value: 0.15931797747485815 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.33447068554509884 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5147698412698413 name: Dot Mrr@10 - type: dot_map@100 value: 0.15438429278142912 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNQ type: NanoNQ metrics: - type: dot_accuracy@1 value: 0.5 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.72 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.76 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.84 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.5 name: Dot Precision@1 - type: dot_precision@3 value: 0.24666666666666667 name: Dot Precision@3 - type: dot_precision@5 value: 0.16 name: Dot Precision@5 - type: dot_precision@10 value: 0.088 name: Dot Precision@10 - type: dot_recall@1 value: 0.48 name: Dot Recall@1 - type: dot_recall@3 value: 0.67 name: Dot Recall@3 - type: dot_recall@5 value: 0.72 name: Dot Recall@5 - type: dot_recall@10 value: 0.79 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6479593376479322 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6163333333333333 name: Dot Mrr@10 - type: dot_map@100 value: 0.6035174820443362 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoQuoraRetrieval type: NanoQuoraRetrieval metrics: - type: dot_accuracy@1 value: 0.92 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.96 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 1.0 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 1.0 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.92 name: Dot Precision@1 - type: dot_precision@3 value: 0.3999999999999999 name: Dot Precision@3 - type: dot_precision@5 value: 0.26799999999999996 name: Dot Precision@5 - type: dot_precision@10 value: 0.13799999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.7973333333333332 name: Dot Recall@1 - type: dot_recall@3 value: 0.922 name: Dot Recall@3 - type: dot_recall@5 value: 0.9893333333333334 name: Dot Recall@5 - type: dot_recall@10 value: 0.996 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.9493554410777213 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.9456666666666667 name: Dot Mrr@10 - type: dot_map@100 value: 0.9286237373737373 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoSCIDOCS type: NanoSCIDOCS metrics: - type: dot_accuracy@1 value: 0.56 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.76 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.78 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.88 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.56 name: Dot Precision@1 - type: dot_precision@3 value: 0.4 name: Dot Precision@3 - type: dot_precision@5 value: 0.29200000000000004 name: Dot Precision@5 - type: dot_precision@10 value: 0.20999999999999996 name: Dot Precision@10 - type: dot_recall@1 value: 0.11866666666666668 name: Dot Recall@1 - type: dot_recall@3 value: 0.24966666666666665 name: Dot Recall@3 - type: dot_recall@5 value: 0.30266666666666675 name: Dot Recall@5 - type: dot_recall@10 value: 0.4316666666666666 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.4265505670611979 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6682142857142856 name: Dot Mrr@10 - type: dot_map@100 value: 0.3385559757581844 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoArguAna type: NanoArguAna metrics: - type: dot_accuracy@1 value: 0.36 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.78 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.84 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.94 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.36 name: Dot Precision@1 - type: dot_precision@3 value: 0.26 name: Dot Precision@3 - type: dot_precision@5 value: 0.16799999999999998 name: Dot Precision@5 - type: dot_precision@10 value: 0.09399999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.36 name: Dot Recall@1 - type: dot_recall@3 value: 0.78 name: Dot Recall@3 - type: dot_recall@5 value: 0.84 name: Dot Recall@5 - type: dot_recall@10 value: 0.94 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6674878961390456 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5782460317460317 name: Dot Mrr@10 - type: dot_map@100 value: 0.5802628384687207 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoSciFact type: NanoSciFact metrics: - type: dot_accuracy@1 value: 0.7 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.8 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.82 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.88 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.7 name: Dot Precision@1 - type: dot_precision@3 value: 0.2866666666666667 name: Dot Precision@3 - type: dot_precision@5 value: 0.184 name: Dot Precision@5 - type: dot_precision@10 value: 0.1 name: Dot Precision@10 - type: dot_recall@1 value: 0.665 name: Dot Recall@1 - type: dot_recall@3 value: 0.79 name: Dot Recall@3 - type: dot_recall@5 value: 0.81 name: Dot Recall@5 - type: dot_recall@10 value: 0.88 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.7776207541845983 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.7519444444444445 name: Dot Mrr@10 - type: dot_map@100 value: 0.742050969601677 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoTouche2020 type: NanoTouche2020 metrics: - type: dot_accuracy@1 value: 0.5306122448979592 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.8367346938775511 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.8979591836734694 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.9795918367346939 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.5306122448979592 name: Dot Precision@1 - type: dot_precision@3 value: 0.5306122448979591 name: Dot Precision@3 - type: dot_precision@5 value: 0.5142857142857142 name: Dot Precision@5 - type: dot_precision@10 value: 0.43469387755102035 name: Dot Precision@10 - type: dot_recall@1 value: 0.03672756127909814 name: Dot Recall@1 - type: dot_recall@3 value: 0.11122615754561782 name: Dot Recall@3 - type: dot_recall@5 value: 0.17495428374251296 name: Dot Recall@5 - type: dot_recall@10 value: 0.28731694149491666 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.47801832046439025 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.7052073210236476 name: Dot Mrr@10 - type: dot_map@100 value: 0.3658602219028105 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean type: NanoBEIR_mean metrics: - type: dot_accuracy@1 value: 0.6008163265306123 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.7674411302982732 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.8198430141287284 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.8799686028257457 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.6008163265306123 name: Dot Precision@1 - type: dot_precision@3 value: 0.3567137624280482 name: Dot Precision@3 - type: dot_precision@5 value: 0.2730989010989011 name: Dot Precision@5 - type: dot_precision@10 value: 0.18620722135007847 name: Dot Precision@10 - type: dot_recall@1 value: 0.3607523760846636 name: Dot Recall@1 - type: dot_recall@3 value: 0.5199318850272447 name: Dot Recall@3 - type: dot_recall@5 value: 0.5776848221695142 name: Dot Recall@5 - type: dot_recall@10 value: 0.6471826663654878 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6226550754065734 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6967979990531011 name: Dot Mrr@10 - type: dot_map@100 value: 0.5483980917195976 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio --- # Sparse CSR model trained on Natural Questions This is a [CSR Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) on the [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) dataset using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 4096-dimensional sparse vector space with 256 maximum active dimensions and can be used for semantic search and sparse retrieval. ## Model Details ### Model Description - **Model Type:** CSR Sparse Encoder - **Base model:** [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1)  - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 4096 dimensions (trained with 256 maximum active dimensions) - **Similarity Function:** Dot Product - **Training Dataset:** - [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) - **Language:** en - **License:** apache-2.0 ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder) ### Full Model Architecture ``` SparseEncoder( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): CSRSparsity({'input_dim': 1024, 'hidden_dim': 4096, 'k': 256, 'k_aux': 512, 'normalize': False, 'dead_threshold': 30}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SparseEncoder # Download from the 🤗 Hub model = SparseEncoder("tomaarsen/csr-mxbai-embed-large-v1-nq-updated-reconstruction-4") # Run inference queries = [ "who is cornelius in the book of acts", ] documents = [ 'Cornelius the Centurion Cornelius (Greek: Κορνήλιος) was a Roman centurion who is considered by Christians to be one of the first Gentiles to convert to the faith, as related in Acts of the Apostles.', "Joe Ranft Ranft reunited with Lasseter when he was hired by Pixar in 1991 as their head of story.[1] There he worked on all of their films produced up to 2006; this included Toy Story (for which he received an Academy Award nomination) and A Bug's Life, as the co-story writer and others as story supervisor. His final film was Cars. He also voiced characters in many of the films, including Heimlich the caterpillar in A Bug's Life, Wheezy the penguin in Toy Story 2, and Jacques the shrimp in Finding Nemo.[1]", 'Wonderful Tonight "Wonderful Tonight" is a ballad written by Eric Clapton. It was included on Clapton\'s 1977 album Slowhand. Clapton wrote the song about Pattie Boyd.[1] The female vocal harmonies on the song are provided by Marcella Detroit (then Marcy Levy) and Yvonne Elliman.', ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) print(query_embeddings.shape, document_embeddings.shape) # [1, 4096] [3, 4096] # Get the similarity scores for the embeddings similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) # tensor([[111.0676, 23.1031, 22.6751]]) ```    ## Evaluation ### Metrics #### Sparse Information Retrieval * Dataset: `NanoMSMARCO_8` * Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters: ```json { "max_active_dims": 8 } ``` | Metric | Value | |:----------------------|:-----------| | dot_accuracy@1 | 0.16 | | dot_accuracy@3 | 0.2 | | dot_accuracy@5 | 0.28 | | dot_accuracy@10 | 0.4 | | dot_precision@1 | 0.16 | | dot_precision@3 | 0.0667 | | dot_precision@5 | 0.056 | | dot_precision@10 | 0.04 | | dot_recall@1 | 0.16 | | dot_recall@3 | 0.2 | | dot_recall@5 | 0.28 | | dot_recall@10 | 0.4 | | **dot_ndcg@10** | **0.2553** | | dot_mrr@10 | 0.2125 | | dot_map@100 | 0.2276 | | query_active_dims | 8.0 | | query_sparsity_ratio | 0.998 | | corpus_active_dims | 8.0 | | corpus_sparsity_ratio | 0.998 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean_8` * Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco" ], "max_active_dims": 8 } ``` | Metric | Value | |:----------------------|:-----------| | dot_accuracy@1 | 0.16 | | dot_accuracy@3 | 0.2 | | dot_accuracy@5 | 0.28 | | dot_accuracy@10 | 0.4 | | dot_precision@1 | 0.16 | | dot_precision@3 | 0.0667 | | dot_precision@5 | 0.056 | | dot_precision@10 | 0.04 | | dot_recall@1 | 0.16 | | dot_recall@3 | 0.2 | | dot_recall@5 | 0.28 | | dot_recall@10 | 0.4 | | **dot_ndcg@10** | **0.2553** | | dot_mrr@10 | 0.2125 | | dot_map@100 | 0.2276 | | query_active_dims | 8.0 | | query_sparsity_ratio | 0.998 | | corpus_active_dims | 8.0 | | corpus_sparsity_ratio | 0.998 | #### Sparse Information Retrieval * Dataset: `NanoMSMARCO_16` * Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters: ```json { "max_active_dims": 16 } ``` | Metric | Value | |:----------------------|:-----------| | dot_accuracy@1 | 0.24 | | dot_accuracy@3 | 0.38 | | dot_accuracy@5 | 0.5 | | dot_accuracy@10 | 0.58 | | dot_precision@1 | 0.24 | | dot_precision@3 | 0.1267 | | dot_precision@5 | 0.1 | | dot_precision@10 | 0.058 | | dot_recall@1 | 0.24 | | dot_recall@3 | 0.38 | | dot_recall@5 | 0.5 | | dot_recall@10 | 0.58 | | **dot_ndcg@10** | **0.3971** | | dot_mrr@10 | 0.3401 | | dot_map@100 | 0.353 | | query_active_dims | 16.0 | | query_sparsity_ratio | 0.9961 | | corpus_active_dims | 16.0 | | corpus_sparsity_ratio | 0.9961 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean_16` * Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco" ], "max_active_dims": 16 } ``` | Metric | Value | |:----------------------|:-----------| | dot_accuracy@1 | 0.24 | | dot_accuracy@3 | 0.38 | | dot_accuracy@5 | 0.5 | | dot_accuracy@10 | 0.58 | | dot_precision@1 | 0.24 | | dot_precision@3 | 0.1267 | | dot_precision@5 | 0.1 | | dot_precision@10 | 0.058 | | dot_recall@1 | 0.24 | | dot_recall@3 | 0.38 | | dot_recall@5 | 0.5 | | dot_recall@10 | 0.58 | | **dot_ndcg@10** | **0.3971** | | dot_mrr@10 | 0.3401 | | dot_map@100 | 0.353 | | query_active_dims | 16.0 | | query_sparsity_ratio | 0.9961 | | corpus_active_dims | 16.0 | | corpus_sparsity_ratio | 0.9961 | #### Sparse Information Retrieval * Dataset: `NanoMSMARCO_32` * Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters: ```json { "max_active_dims": 32 } ``` | Metric | Value | |:----------------------|:-----------| | dot_accuracy@1 | 0.3 | | dot_accuracy@3 | 0.46 | | dot_accuracy@5 | 0.62 | | dot_accuracy@10 | 0.7 | | dot_precision@1 | 0.3 | | dot_precision@3 | 0.1533 | | dot_precision@5 | 0.124 | | dot_precision@10 | 0.07 | | dot_recall@1 | 0.3 | | dot_recall@3 | 0.46 | | dot_recall@5 | 0.62 | | dot_recall@10 | 0.7 | | **dot_ndcg@10** | **0.4873** | | dot_mrr@10 | 0.4206 | | dot_map@100 | 0.4326 | | query_active_dims | 32.0 | | query_sparsity_ratio | 0.9922 | | corpus_active_dims | 32.0 | | corpus_sparsity_ratio | 0.9922 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean_32` * Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco" ], "max_active_dims": 32 } ``` | Metric | Value | |:----------------------|:-----------| | dot_accuracy@1 | 0.3 | | dot_accuracy@3 | 0.46 | | dot_accuracy@5 | 0.62 | | dot_accuracy@10 | 0.7 | | dot_precision@1 | 0.3 | | dot_precision@3 | 0.1533 | | dot_precision@5 | 0.124 | | dot_precision@10 | 0.07 | | dot_recall@1 | 0.3 | | dot_recall@3 | 0.46 | | dot_recall@5 | 0.62 | | dot_recall@10 | 0.7 | | **dot_ndcg@10** | **0.4873** | | dot_mrr@10 | 0.4206 | | dot_map@100 | 0.4326 | | query_active_dims | 32.0 | | query_sparsity_ratio | 0.9922 | | corpus_active_dims | 32.0 | | corpus_sparsity_ratio | 0.9922 | #### Sparse Information Retrieval * Dataset: `NanoMSMARCO_64` * Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters: ```json { "max_active_dims": 64 } ``` | Metric | Value | |:----------------------|:-----------| | dot_accuracy@1 | 0.42 | | dot_accuracy@3 | 0.6 | | dot_accuracy@5 | 0.68 | | dot_accuracy@10 | 0.78 | | dot_precision@1 | 0.42 | | dot_precision@3 | 0.2 | | dot_precision@5 | 0.136 | | dot_precision@10 | 0.078 | | dot_recall@1 | 0.42 | | dot_recall@3 | 0.6 | | dot_recall@5 | 0.68 | | dot_recall@10 | 0.78 | | **dot_ndcg@10** | **0.5911** | | dot_mrr@10 | 0.5317 | | dot_map@100 | 0.5406 | | query_active_dims | 64.0 | | query_sparsity_ratio | 0.9844 | | corpus_active_dims | 64.0 | | corpus_sparsity_ratio | 0.9844 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean_64` * Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco" ], "max_active_dims": 64 } ``` | Metric | Value | |:----------------------|:-----------| | dot_accuracy@1 | 0.42 | | dot_accuracy@3 | 0.6 | | dot_accuracy@5 | 0.68 | | dot_accuracy@10 | 0.78 | | dot_precision@1 | 0.42 | | dot_precision@3 | 0.2 | | dot_precision@5 | 0.136 | | dot_precision@10 | 0.078 | | dot_recall@1 | 0.42 | | dot_recall@3 | 0.6 | | dot_recall@5 | 0.68 | | dot_recall@10 | 0.78 | | **dot_ndcg@10** | **0.5911** | | dot_mrr@10 | 0.5317 | | dot_map@100 | 0.5406 | | query_active_dims | 64.0 | | query_sparsity_ratio | 0.9844 | | corpus_active_dims | 64.0 | | corpus_sparsity_ratio | 0.9844 | #### Sparse Information Retrieval * Dataset: `NanoMSMARCO_128` * Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters: ```json { "max_active_dims": 128 } ``` | Metric | Value | |:----------------------|:-----------| | dot_accuracy@1 | 0.36 | | dot_accuracy@3 | 0.64 | | dot_accuracy@5 | 0.72 | | dot_accuracy@10 | 0.82 | | dot_precision@1 | 0.36 | | dot_precision@3 | 0.2133 | | dot_precision@5 | 0.144 | | dot_precision@10 | 0.082 | | dot_recall@1 | 0.36 | | dot_recall@3 | 0.64 | | dot_recall@5 | 0.72 | | dot_recall@10 | 0.82 | | **dot_ndcg@10** | **0.5877** | | dot_mrr@10 | 0.5139 | | dot_map@100 | 0.5217 | | query_active_dims | 128.0 | | query_sparsity_ratio | 0.9688 | | corpus_active_dims | 128.0 | | corpus_sparsity_ratio | 0.9688 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean_128` * Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco" ], "max_active_dims": 128 } ``` | Metric | Value | |:----------------------|:-----------| | dot_accuracy@1 | 0.36 | | dot_accuracy@3 | 0.64 | | dot_accuracy@5 | 0.72 | | dot_accuracy@10 | 0.82 | | dot_precision@1 | 0.36 | | dot_precision@3 | 0.2133 | | dot_precision@5 | 0.144 | | dot_precision@10 | 0.082 | | dot_recall@1 | 0.36 | | dot_recall@3 | 0.64 | | dot_recall@5 | 0.72 | | dot_recall@10 | 0.82 | | **dot_ndcg@10** | **0.5877** | | dot_mrr@10 | 0.5139 | | dot_map@100 | 0.5217 | | query_active_dims | 128.0 | | query_sparsity_ratio | 0.9688 | | corpus_active_dims | 128.0 | | corpus_sparsity_ratio | 0.9688 | #### Sparse Information Retrieval * Dataset: `NanoMSMARCO_256` * Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters: ```json { "max_active_dims": 256 } ``` | Metric | Value | |:----------------------|:-----------| | dot_accuracy@1 | 0.42 | | dot_accuracy@3 | 0.64 | | dot_accuracy@5 | 0.74 | | dot_accuracy@10 | 0.82 | | dot_precision@1 | 0.42 | | dot_precision@3 | 0.2133 | | dot_precision@5 | 0.148 | | dot_precision@10 | 0.082 | | dot_recall@1 | 0.42 | | dot_recall@3 | 0.64 | | dot_recall@5 | 0.74 | | dot_recall@10 | 0.82 | | **dot_ndcg@10** | **0.6247** | | dot_mrr@10 | 0.5612 | | dot_map@100 | 0.5701 | | query_active_dims | 256.0 | | query_sparsity_ratio | 0.9375 | | corpus_active_dims | 256.0 | | corpus_sparsity_ratio | 0.9375 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean_256` * Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco" ], "max_active_dims": 256 } ``` | Metric | Value | |:----------------------|:-----------| | dot_accuracy@1 | 0.42 | | dot_accuracy@3 | 0.64 | | dot_accuracy@5 | 0.74 | | dot_accuracy@10 | 0.82 | | dot_precision@1 | 0.42 | | dot_precision@3 | 0.2133 | | dot_precision@5 | 0.148 | | dot_precision@10 | 0.082 | | dot_recall@1 | 0.42 | | dot_recall@3 | 0.64 | | dot_recall@5 | 0.74 | | dot_recall@10 | 0.82 | | **dot_ndcg@10** | **0.6247** | | dot_mrr@10 | 0.5612 | | dot_map@100 | 0.5701 | | query_active_dims | 256.0 | | query_sparsity_ratio | 0.9375 | | corpus_active_dims | 256.0 | | corpus_sparsity_ratio | 0.9375 | #### Sparse Information Retrieval * Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020` * Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) | Metric | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 | |:----------------------|:-----------------|:------------|:-----------|:-------------|:-------------|:------------|:-------------|:----------|:-------------------|:------------|:------------|:------------|:---------------| | dot_accuracy@1 | 0.36 | 0.8 | 0.9 | 0.56 | 0.76 | 0.42 | 0.44 | 0.5 | 0.92 | 0.56 | 0.36 | 0.7 | 0.5306 | | dot_accuracy@3 | 0.52 | 0.88 | 0.92 | 0.7 | 0.9 | 0.64 | 0.56 | 0.72 | 0.96 | 0.76 | 0.78 | 0.8 | 0.8367 | | dot_accuracy@5 | 0.66 | 0.92 | 0.96 | 0.72 | 0.94 | 0.76 | 0.6 | 0.76 | 1.0 | 0.78 | 0.84 | 0.82 | 0.898 | | dot_accuracy@10 | 0.8 | 0.94 | 0.96 | 0.72 | 0.94 | 0.82 | 0.74 | 0.84 | 1.0 | 0.88 | 0.94 | 0.88 | 0.9796 | | dot_precision@1 | 0.36 | 0.8 | 0.9 | 0.56 | 0.76 | 0.42 | 0.44 | 0.5 | 0.92 | 0.56 | 0.36 | 0.7 | 0.5306 | | dot_precision@3 | 0.2 | 0.6 | 0.3267 | 0.32 | 0.5 | 0.2133 | 0.3533 | 0.2467 | 0.4 | 0.4 | 0.26 | 0.2867 | 0.5306 | | dot_precision@5 | 0.156 | 0.58 | 0.204 | 0.236 | 0.316 | 0.152 | 0.32 | 0.16 | 0.268 | 0.292 | 0.168 | 0.184 | 0.5143 | | dot_precision@10 | 0.114 | 0.484 | 0.102 | 0.13 | 0.172 | 0.082 | 0.272 | 0.088 | 0.138 | 0.21 | 0.094 | 0.1 | 0.4347 | | dot_recall@1 | 0.1573 | 0.0936 | 0.8467 | 0.2992 | 0.38 | 0.42 | 0.0352 | 0.48 | 0.7973 | 0.1187 | 0.36 | 0.665 | 0.0367 | | dot_recall@3 | 0.2473 | 0.1618 | 0.8933 | 0.4673 | 0.75 | 0.64 | 0.0765 | 0.67 | 0.922 | 0.2497 | 0.78 | 0.79 | 0.1112 | | dot_recall@5 | 0.313 | 0.2269 | 0.9333 | 0.5337 | 0.79 | 0.76 | 0.116 | 0.72 | 0.9893 | 0.3027 | 0.84 | 0.81 | 0.175 | | dot_recall@10 | 0.438 | 0.3304 | 0.9333 | 0.5473 | 0.86 | 0.82 | 0.1593 | 0.79 | 0.996 | 0.4317 | 0.94 | 0.88 | 0.2873 | | **dot_ndcg@10** | **0.3566** | **0.6072** | **0.9081** | **0.5253** | **0.7911** | **0.6248** | **0.3345** | **0.648** | **0.9494** | **0.4266** | **0.6675** | **0.7776** | **0.478** | | dot_mrr@10 | 0.4796 | 0.852 | 0.92 | 0.6317 | 0.8333 | 0.5614 | 0.5148 | 0.6163 | 0.9457 | 0.6682 | 0.5782 | 0.7519 | 0.7052 | | dot_map@100 | 0.2782 | 0.4541 | 0.8921 | 0.48 | 0.7411 | 0.5703 | 0.1544 | 0.6035 | 0.9286 | 0.3386 | 0.5803 | 0.7421 | 0.3659 | | query_active_dims | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | | query_sparsity_ratio | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | | corpus_active_dims | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | | corpus_sparsity_ratio | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean` * Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "climatefever", "dbpedia", "fever", "fiqa2018", "hotpotqa", "msmarco", "nfcorpus", "nq", "quoraretrieval", "scidocs", "arguana", "scifact", "touche2020" ] } ``` | Metric | Value | |:----------------------|:-----------| | dot_accuracy@1 | 0.6008 | | dot_accuracy@3 | 0.7674 | | dot_accuracy@5 | 0.8198 | | dot_accuracy@10 | 0.88 | | dot_precision@1 | 0.6008 | | dot_precision@3 | 0.3567 | | dot_precision@5 | 0.2731 | | dot_precision@10 | 0.1862 | | dot_recall@1 | 0.3608 | | dot_recall@3 | 0.5199 | | dot_recall@5 | 0.5777 | | dot_recall@10 | 0.6472 | | **dot_ndcg@10** | **0.6227** | | dot_mrr@10 | 0.6968 | | dot_map@100 | 0.5484 | | query_active_dims | 256.0 | | query_sparsity_ratio | 0.9375 | | corpus_active_dims | 256.0 | | corpus_sparsity_ratio | 0.9375 |   ## Training Details ### Training Dataset #### natural-questions * Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) * Size: 99,000 training samples * Columns: <code>query</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: | | query | answer | |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 10 tokens</li><li>mean: 11.71 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 131.81 tokens</li><li>max: 450 tokens</li></ul> | * Samples: | query | answer | |:--------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>who played the father in papa don't preach</code> | <code>Alex McArthur Alex McArthur (born March 6, 1957) is an American actor.</code> | | <code>where was the location of the battle of hastings</code> | <code>Battle of Hastings The Battle of Hastings[a] was fought on 14 October 1066 between the Norman-French army of William, the Duke of Normandy, and an English army under the Anglo-Saxon King Harold Godwinson, beginning the Norman conquest of England. It took place approximately 7 miles (11 kilometres) northwest of Hastings, close to the present-day town of Battle, East Sussex, and was a decisive Norman victory.</code> | | <code>how many puppies can a dog give birth to</code> | <code>Canine reproduction The largest litter size to date was set by a Neapolitan Mastiff in Manea, Cambridgeshire, UK on November 29, 2004; the litter was 24 puppies.[22]</code> | * Loss: [<code>CSRLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#csrloss) with these parameters: ```json { "beta": 0.1, "gamma": 3.0, "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')" } ``` ### Evaluation Dataset #### natural-questions * Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) * Size: 1,000 evaluation samples * Columns: <code>query</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: | | query | answer | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 10 tokens</li><li>mean: 11.69 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 134.01 tokens</li><li>max: 512 tokens</li></ul> | * Samples: | query | answer | |:-------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>where is the tiber river located in italy</code> | <code>Tiber The Tiber (/ˈtaɪbər/, Latin: Tiberis,[1] Italian: Tevere [ˈteːvere])[2] is the third-longest river in Italy, rising in the Apennine Mountains in Emilia-Romagna and flowing 406 kilometres (252 mi) through Tuscany, Umbria and Lazio, where it is joined by the river Aniene, to the Tyrrhenian Sea, between Ostia and Fiumicino.[3] It drains a basin estimated at 17,375 square kilometres (6,709 sq mi). The river has achieved lasting fame as the main watercourse of the city of Rome, founded on its eastern banks.</code> | | <code>what kind of car does jay gatsby drive</code> | <code>Jay Gatsby At the Buchanan home, Jordan Baker, Nick, Jay, and the Buchanans decide to visit New York City. Tom borrows Gatsby's yellow Rolls Royce to drive up to the city. On the way to New York City, Tom makes a detour at a gas station in "the Valley of Ashes", a run-down part of Long Island. The owner, George Wilson, shares his concern that his wife, Myrtle, may be having an affair. This unnerves Tom, who has been having an affair with Myrtle, and he leaves in a hurry.</code> | | <code>who sings if i can dream about you</code> | <code>I Can Dream About You "I Can Dream About You" is a song performed by American singer Dan Hartman on the soundtrack album of the film Streets of Fire. Released in 1984 as a single from the soundtrack, and included on Hartman's album I Can Dream About You, it reached number 6 on the Billboard Hot 100.[1]</code> | * Loss: [<code>CSRLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#csrloss) with these parameters: ```json { "beta": 0.1, "gamma": 3.0, "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `learning_rate`: 4e-05 - `num_train_epochs`: 1 - `bf16`: True - `load_best_model_at_end`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 4e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {} </details> ### Training Logs | Epoch | Step | Training Loss | Validation Loss | NanoMSMARCO_8_dot_ndcg@10 | NanoBEIR_mean_8_dot_ndcg@10 | NanoMSMARCO_16_dot_ndcg@10 | NanoBEIR_mean_16_dot_ndcg@10 | NanoMSMARCO_32_dot_ndcg@10 | NanoBEIR_mean_32_dot_ndcg@10 | NanoMSMARCO_64_dot_ndcg@10 | NanoBEIR_mean_64_dot_ndcg@10 | NanoMSMARCO_128_dot_ndcg@10 | NanoBEIR_mean_128_dot_ndcg@10 | NanoMSMARCO_256_dot_ndcg@10 | NanoBEIR_mean_256_dot_ndcg@10 | NanoClimateFEVER_dot_ndcg@10 | NanoDBPedia_dot_ndcg@10 | NanoFEVER_dot_ndcg@10 | NanoFiQA2018_dot_ndcg@10 | NanoHotpotQA_dot_ndcg@10 | NanoMSMARCO_dot_ndcg@10 | NanoNFCorpus_dot_ndcg@10 | NanoNQ_dot_ndcg@10 | NanoQuoraRetrieval_dot_ndcg@10 | NanoSCIDOCS_dot_ndcg@10 | NanoArguAna_dot_ndcg@10 | NanoSciFact_dot_ndcg@10 | NanoTouche2020_dot_ndcg@10 | NanoBEIR_mean_dot_ndcg@10 | |:----------:|:--------:|:-------------:|:---------------:|:-------------------------:|:---------------------------:|:--------------------------:|:----------------------------:|:--------------------------:|:----------------------------:|:--------------------------:|:----------------------------:|:---------------------------:|:-----------------------------:|:---------------------------:|:-----------------------------:|:----------------------------:|:-----------------------:|:---------------------:|:------------------------:|:------------------------:|:-----------------------:|:------------------------:|:------------------:|:------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:--------------------------:|:-------------------------:| | -1 | -1 | - | - | 0.2447 | 0.2447 | 0.3677 | 0.3677 | 0.5086 | 0.5086 | 0.5304 | 0.5304 | 0.6134 | 0.6134 | 0.5961 | 0.5961 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.0646 | 100 | 0.5048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1293 | 200 | 0.5017 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1939 | 300 | 0.531 | 0.6279 | 0.2125 | 0.2125 | 0.4075 | 0.4075 | 0.4686 | 0.4686 | 0.5701 | 0.5701 | 0.6086 | 0.6086 | 0.5877 | 0.5877 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2586 | 400 | 0.4992 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3232 | 500 | 0.5574 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3878 | 600 | 0.5821 | 0.6178 | 0.2312 | 0.2312 | 0.4248 | 0.4248 | 0.4239 | 0.4239 | 0.5142 | 0.5142 | 0.6034 | 0.6034 | 0.6177 | 0.6177 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4525 | 700 | 0.5632 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5171 | 800 | 0.5786 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5818 | 900 | 0.5329 | 0.5743 | 0.2662 | 0.2662 | 0.4468 | 0.4468 | 0.4976 | 0.4976 | 0.5630 | 0.5630 | 0.6279 | 0.6279 | 0.6240 | 0.6240 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6464 | 1000 | 0.5409 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7111 | 1100 | 0.4995 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7757 | 1200 | 0.5269 | 0.5169 | 0.2838 | 0.2838 | 0.3874 | 0.3874 | 0.4738 | 0.4738 | 0.5892 | 0.5892 | 0.5798 | 0.5798 | 0.5962 | 0.5962 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.8403 | 1300 | 0.5553 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9050 | 1400 | 0.45 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | **0.9696** | **1500** | **0.4551** | **0.5188** | **0.2553** | **0.2553** | **0.3971** | **0.3971** | **0.4873** | **0.4873** | **0.5911** | **0.5911** | **0.5877** | **0.5877** | **0.6247** | **0.6247** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | | -1 | -1 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | 0.3566 | 0.6072 | 0.9081 | 0.5253 | 0.7911 | 0.6248 | 0.3345 | 0.6480 | 0.9494 | 0.4266 | 0.6675 | 0.7776 | 0.4780 | 0.6227 | * The bold row denotes the saved checkpoint. ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.122 kWh - **Carbon Emitted**: 0.047 kg of CO2 - **Hours Used**: 0.373 hours ### Training Hardware - **On Cloud**: No - **GPU Model**: 1 x NVIDIA GeForce RTX 3090 - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K - **RAM Size**: 31.78 GB ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 4.2.0.dev0 - Transformers: 4.52.4 - PyTorch: 2.6.0+cu124 - Accelerate: 1.5.1 - Datasets: 2.21.0 - Tokenizers: 0.21.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### CSRLoss ```bibtex @misc{wen2025matryoshkarevisitingsparsecoding, title={Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation}, author={Tiansheng Wen and Yifei Wang and Zequn Zeng and Zhong Peng and Yudi Su and Xinyang Liu and Bo Chen and Hongwei Liu and Stefanie Jegelka and Chenyu You}, year={2025}, eprint={2503.01776}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2503.01776}, } ``` #### SparseMultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

huihui-ai/Huihui-Qwen3-14B-abliterated-v2

huihui-ai

2025-06-19T15:40:01Z

4

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "chat", "abliterated", "uncensored", "conversational", "base_model:Qwen/Qwen3-14B", "base_model:finetune:Qwen/Qwen3-14B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-17T15:29:37Z

--- library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-14B/blob/main/LICENSE pipeline_tag: text-generation base_model: - Qwen/Qwen3-14B tags: - chat - abliterated - uncensored --- # huihui-ai/Huihui-Qwen3-14B-abliterated-v2 This is an uncensored version of [Qwen/Qwen3-14B](https://huggingface.co/Qwen/Qwen3-14B) created with abliteration (see [remove-refusals-with-transformers](https://github.com/Sumandora/remove-refusals-with-transformers) to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. Ablation was performed using a new and faster method, which yields better results. **Important Note** This version is an improvement over the previous one [huihui-ai/Qwen3-14B-abliterated](https://huggingface.co/huihui-ai/Qwen3-14B-abliterated). The ollama version has also been modified. Changed the candidate layer to eliminate the problem of garbled codes ## ollama You can use [huihui_ai/qwen3-abliterated:14b-v2](https://ollama.com/huihui_ai/qwen3-abliterated:14b-v2) directly, Switch the thinking toggle using /set think and /set nothink ``` ollama run huihui_ai/qwen3-abliterated:14b-v2 ``` ## Usage You can use this model in your applications by loading it with Hugging Face's `transformers` library: ```python from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, TextStreamer import torch import os import signal import random import numpy as np import time from collections import Counter cpu_count = os.cpu_count() print(f"Number of CPU cores in the system: {cpu_count}") half_cpu_count = cpu_count // 2 os.environ["MKL_NUM_THREADS"] = str(half_cpu_count) os.environ["OMP_NUM_THREADS"] = str(half_cpu_count) torch.set_num_threads(half_cpu_count) print(f"PyTorch threads: {torch.get_num_threads()}") print(f"MKL threads: {os.getenv('MKL_NUM_THREADS')}") print(f"OMP threads: {os.getenv('OMP_NUM_THREADS')}") # Load the model and tokenizer NEW_MODEL_ID = "huihui-ai/Huihui-Qwen3-14B-abliterated-v2" print(f"Load Model {NEW_MODEL_ID} ... ") quant_config_4= BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True, llm_int8_enable_fp32_cpu_offload=True, ) model = AutoModelForCausalLM.from_pretrained( NEW_MODEL_ID, device_map="auto", trust_remote_code=True, #quantization_config=quant_config_4, torch_dtype=torch.bfloat16 ) tokenizer = AutoTokenizer.from_pretrained(NEW_MODEL_ID, trust_remote_code=True) if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token tokenizer.pad_token_id = tokenizer.eos_token_id tokenizer = AutoTokenizer.from_pretrained(NEW_MODEL_ID, trust_remote_code=True) if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token tokenizer.pad_token_id = tokenizer.eos_token_id messages = [] nothink = False same_seed = False skip_prompt=True skip_special_tokens=True do_sample = True def set_random_seed(seed=None): """Set random seed for reproducibility. If seed is None, use int(time.time()).""" if seed is None: seed = int(time.time()) # Convert float to int random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed_all(seed) # If using CUDA torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False return seed # Return seed for logging if needed class CustomTextStreamer(TextStreamer): def __init__(self, tokenizer, skip_prompt=True, skip_special_tokens=True): super().__init__(tokenizer, skip_prompt=skip_prompt, skip_special_tokens=skip_special_tokens) self.generated_text = "" self.stop_flag = False self.init_time = time.time() # Record initialization time self.end_time = None # To store end time self.first_token_time = None # To store first token generation time self.token_count = 0 # To track total tokens def on_finalized_text(self, text: str, stream_end: bool = False): if self.first_token_time is None and text.strip(): # Set first token time on first non-empty text self.first_token_time = time.time() self.generated_text += text # Count tokens in the generated text tokens = self.tokenizer.encode(text, add_special_tokens=False) self.token_count += len(tokens) print(text, end="", flush=True) if stream_end: self.end_time = time.time() # Record end time when streaming ends if self.stop_flag: raise StopIteration def stop_generation(self): self.stop_flag = True self.end_time = time.time() # Record end time when generation is stopped def get_metrics(self): """Returns initialization time, first token time, first token latency, end time, total time, total tokens, and tokens per second.""" if self.end_time is None: self.end_time = time.time() # Set end time if not already set total_time = self.end_time - self.init_time # Total time from init to end tokens_per_second = self.token_count / total_time if total_time > 0 else 0 first_token_latency = (self.first_token_time - self.init_time) if self.first_token_time is not None else None metrics = { "init_time": self.init_time, "first_token_time": self.first_token_time, "first_token_latency": first_token_latency, "end_time": self.end_time, "total_time": total_time, # Total time in seconds "total_tokens": self.token_count, "tokens_per_second": tokens_per_second } return metrics def generate_stream(model, tokenizer, messages, nothink, skip_prompt, skip_special_tokens, do_sample, max_new_tokens): input_ids = tokenizer.apply_chat_template( messages, tokenize=True, enable_thinking = not nothink, add_generation_prompt=True, return_tensors="pt" ) attention_mask = torch.ones_like(input_ids, dtype=torch.long) tokens = input_ids.to(model.device) attention_mask = attention_mask.to(model.device) streamer = CustomTextStreamer(tokenizer, skip_prompt=skip_prompt, skip_special_tokens=skip_special_tokens) def signal_handler(sig, frame): streamer.stop_generation() print("\n[Generation stopped by user with Ctrl+C]") signal.signal(signal.SIGINT, signal_handler) generate_kwargs = {} if do_sample: generate_kwargs = { "do_sample": do_sample, "max_length": max_new_tokens, "temperature": 0.6, "top_k": 20, "top_p": 0.95, "repetition_penalty": 1.2, "no_repeat_ngram_size": 2 } else: generate_kwargs = { "do_sample": do_sample, "max_length": max_new_tokens, "repetition_penalty": 1.2, "no_repeat_ngram_size": 2 } print("Response: ", end="", flush=True) try: generated_ids = model.generate( tokens, attention_mask=attention_mask, #use_cache=False, pad_token_id=tokenizer.pad_token_id, streamer=streamer, **generate_kwargs ) del generated_ids except StopIteration: print("\n[Stopped by user]") del input_ids, attention_mask torch.cuda.empty_cache() signal.signal(signal.SIGINT, signal.SIG_DFL) return streamer.generated_text, streamer.stop_flag, streamer.get_metrics() init_seed = set_random_seed() while True: if same_seed: set_random_seed(init_seed) else: init_seed = set_random_seed() print(f"\nnothink: {nothink}") print(f"skip_prompt: {skip_prompt}") print(f"skip_special_tokens: {skip_special_tokens}") print(f"do_sample: {do_sample}") print(f"same_seed: {same_seed}, {init_seed}\n") user_input = input("User: ").strip() if user_input.lower() == "/exit": print("Exiting chat.") break if user_input.lower() == "/clear": messages = [] print("Chat history cleared. Starting a new conversation.") continue if user_input.lower() == "/nothink": nothink = not nothink continue if user_input.lower() == "/skip_prompt": skip_prompt = not skip_prompt continue if user_input.lower() == "/skip_special_tokens": skip_special_tokens = not skip_special_tokens continue if user_input.lower().startswith("/same_seed"): parts = user_input.split() if len(parts) == 1: # /same_seed (no number) same_seed = not same_seed # Toggle switch elif len(parts) == 2: # /same_seed <number> try: init_seed = int(parts[1]) # Extract and convert number to int same_seed = True except ValueError: print("Error: Please provide a valid integer after /same_seed") continue if user_input.lower() == "/do_sample": do_sample = not do_sample continue if not user_input: print("Input cannot be empty. Please enter something.") continue messages.append({"role": "user", "content": user_input}) activated_experts = [] response, stop_flag, metrics = generate_stream(model, tokenizer, messages, nothink, skip_prompt, skip_special_tokens, do_sample, 320960) print("\n\nMetrics:") for key, value in metrics.items(): print(f" {key}: {value}") print("", flush=True) if stop_flag: continue messages.append({"role": "assistant", "content": response}) # Remove all hooks after inference for h in hooks: h.remove() ``` ### Usage Warnings - **Risk of Sensitive or Controversial Outputs**: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs. - **Not Suitable for All Audiences**: Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security. - **Legal and Ethical Responsibilities**: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences. - **Research and Experimental Use**: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications. - **Monitoring and Review Recommendations**: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content. - **No Default Safety Guarantees**: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use. ### Donation If you like it, please click 'like' and follow us for more updates. You can follow [x.com/support_huihui](https://x.com/support_huihui) to get the latest model information from huihui.ai. ##### Your donation helps us continue our further development and improvement, a cup of coffee can do it. - bitcoin（BTC): ``` bc1qqnkhuchxw0zqjh2ku3lu4hq45hc6gy84uk70ge ```

hospital-teresopolis-link/Original.Full.video.18.hospital.teresopolis.hospital.de.teresopolis.video.portal.Zacarias

hospital-teresopolis-link

2025-06-19T15:38:30Z

0

null

[ "region:us" ]

null

2025-06-19T15:37:58Z

<a rel="nofollow" href="https://viralflix.xyz/leaked/?fre">🔴 CLICK HERE 🌐==►► Download Now)</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?fre">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a> <a rel="nofollow" href="https://viralflix.xyz/leaked/?fre"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>

csikasote/mms-1b-all-nyagen-combined-42

csikasote

2025-06-19T15:37:22Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "wav2vec2", "automatic-speech-recognition", "nyagen", "mms", "generated_from_trainer", "base_model:facebook/mms-1b-all", "base_model:finetune:facebook/mms-1b-all", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2025-06-19T13:02:44Z

--- library_name: transformers license: cc-by-nc-4.0 base_model: facebook/mms-1b-all tags: - automatic-speech-recognition - nyagen - mms - generated_from_trainer metrics: - wer model-index: - name: mms-1b-all-nyagen-combined-42 results: [] ---  # mms-1b-all-nyagen-combined-42 This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the NYAGEN - NYA dataset. It achieves the following results on the evaluation set: - Loss: 0.3879 - Wer: 0.3078 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 30.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-------:|:----:|:---------------:|:------:| | 8.807 | 0.5025 | 100 | 5.9307 | 0.9998 | | 5.1806 | 1.0050 | 200 | 4.7590 | 1.0179 | | 3.8969 | 1.5075 | 300 | 3.4468 | 0.9976 | | 3.367 | 2.0101 | 400 | 3.2098 | 0.9938 | | 3.2105 | 2.5126 | 500 | 3.1289 | 0.9933 | | 3.1366 | 3.0151 | 600 | 3.1086 | 0.9927 | | 3.0162 | 3.5176 | 700 | 2.9525 | 0.9948 | | 2.9078 | 4.0201 | 800 | 1.5350 | 1.0042 | | 0.4877 | 4.5226 | 900 | 0.5052 | 0.4215 | | 0.3193 | 5.0251 | 1000 | 0.4687 | 0.3793 | | 0.2953 | 5.5276 | 1100 | 0.4322 | 0.3547 | | 0.2815 | 6.0302 | 1200 | 0.4283 | 0.3460 | | 0.2729 | 6.5327 | 1300 | 0.4171 | 0.3327 | | 0.2561 | 7.0352 | 1400 | 0.4085 | 0.3271 | | 0.2543 | 7.5377 | 1500 | 0.4071 | 0.3290 | | 0.2443 | 8.0402 | 1600 | 0.4039 | 0.3149 | | 0.2402 | 8.5427 | 1700 | 0.4088 | 0.3173 | | 0.2273 | 9.0452 | 1800 | 0.4048 | 0.3149 | | 0.2299 | 9.5477 | 1900 | 0.3911 | 0.3063 | | 0.2313 | 10.0503 | 2000 | 0.3879 | 0.3077 | | 0.2203 | 10.5528 | 2100 | 0.3874 | 0.3033 | | 0.2168 | 11.0553 | 2200 | 0.3837 | 0.2985 | | 0.2167 | 11.5578 | 2300 | 0.3810 | 0.2979 | | 0.211 | 12.0603 | 2400 | 0.3854 | 0.2952 | | 0.2039 | 12.5628 | 2500 | 0.3803 | 0.2868 | | 0.2152 | 13.0653 | 2600 | 0.3760 | 0.2926 | | 0.2003 | 13.5678 | 2700 | 0.3732 | 0.2883 | | 0.2025 | 14.0704 | 2800 | 0.3798 | 0.2878 | | 0.2005 | 14.5729 | 2900 | 0.3796 | 0.2879 | | 0.2032 | 15.0754 | 3000 | 0.3764 | 0.2817 | ### Framework versions - Transformers 4.53.0.dev0 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.0

MikeGreen2710/ner_cons_dims_final

MikeGreen2710

2025-06-19T15:32:52Z

0

transformers

[ "transformers", "safetensors", "roberta", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2025-06-19T15:32:18Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

hasdal/21a58fba-d539-4969-960e-60eff2254792

hasdal

2025-06-19T15:28:22Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "unsloth", "conversational", "custom_code", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2025-06-19T15:14:36Z

--- library_name: transformers tags: - unsloth --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

altinkedi/xxtrgpt2v3s

altinkedi

2025-06-19T15:27:48Z

0

transformers

[ "transformers", "safetensors", "gpt2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-19T15:20:50Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

sanchit42/qwen3-0.6B-instruct-29reports-lora256-slim

sanchit42

2025-06-19T15:23:50Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-19T15:22:14Z

--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

phospho-app/gc1724-ACT_BBOX-bottle-y67xn

phospho-app

2025-06-19T15:23:13Z

0

null

[ "phosphobot", "act", "region:us" ]

null

2025-06-19T15:21:10Z

--- tags: - phosphobot - act task_categories: - robotics --- # act Model - phospho Training Pipeline ## Error Traceback We faced an issue while training your model. ``` Caught KeyError in DataLoader worker process 0. Original Traceback (most recent call last): File "/opt/conda/lib/python3.11/site-packages/torch/utils/data/_utils/worker.py", line 349, in _worker_loop data = fetcher.fetch(index) # type: ignore[possibly-undefined] ^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/torch/utils/data/_utils/fetch.py", line 52, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/conda/lib/python3.11/site-packages/torch/utils/data/_utils/fetch.py", line 52, in <listcomp> data = [self.dataset[idx] for idx in possibly_batched_index] ~~~~~~~~~~~~^^^^^ File "/root/src/helper.py", line 198, in __getitem__ frame = self.cache[episode_idx][video_key][row_idx] ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^ KeyError: '.DS_Store' ``` ## Training parameters: - **Dataset**: [gc1724/bottle](https://huggingface.co/datasets/gc1724/bottle) - **Wandb run URL**: None - **Epochs**: None - **Batch size**: 100 - **Training steps**: 10000 📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme) 🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)

alfredcs/gemma-3-27b-firstaid-icd10-merged

alfredcs

2025-06-19T15:23:10Z

0

transformers

[ "transformers", "pytorch", "safetensors", "gemma3", "image-text-to-text", "trl", "grpo", "GRPO", "Reasoning-Course", "conversational", "arxiv:1910.09700", "text-generation-inference", "endpoints_compatible", "region:us" ]

image-text-to-text

2025-06-19T05:13:36Z

--- library_name: transformers tags: - trl - grpo - GRPO - Reasoning-Course --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

BootesVoid/cmbab3f3e0nsp1b1yngcaf3y6_cmc3i211r00exnx8d905xzmsr

BootesVoid

2025-06-19T15:22:48Z

0

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2025-06-19T15:22:46Z

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: NATURAL --- # Cmbab3F3E0Nsp1B1Yngcaf3Y6_Cmc3I211R00Exnx8D905Xzmsr <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `NATURAL` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "NATURAL", "lora_weights": "https://huggingface.co/BootesVoid/cmbab3f3e0nsp1b1yngcaf3y6_cmc3i211r00exnx8d905xzmsr/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmbab3f3e0nsp1b1yngcaf3y6_cmc3i211r00exnx8d905xzmsr', weight_name='lora.safetensors') image = pipeline('NATURAL').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmbab3f3e0nsp1b1yngcaf3y6_cmc3i211r00exnx8d905xzmsr/discussions) to add images that show off what you’ve made with this LoRA.

Catality/3b-shitboxes

Catality

2025-06-19T15:21:54Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2025-06-19T15:13:05Z

--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - **Developed by:** Catality - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

sungkwan2/my_awesome_food_model

sungkwan2

2025-06-19T15:20:54Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2025-06-19T15:07:11Z

--- library_name: transformers license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer metrics: - accuracy model-index: - name: my_awesome_food_model results: [] ---  # my_awesome_food_model This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6158 - Accuracy: 0.899 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7715 | 1.0 | 63 | 2.5460 | 0.87 | | 1.8698 | 2.0 | 126 | 1.7718 | 0.897 | | 1.6144 | 3.0 | 189 | 1.6158 | 0.899 | ### Framework versions - Transformers 4.52.4 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1

MarkProMaster229/internet_dialog

MarkProMaster229

2025-06-19T15:20:21Z

0

null

[ "safetensors", "gpt2", "ru", "license:openrail", "region:us" ]

null

2025-06-19T15:04:10Z

--- license: openrail language: - ru --- # русская языковая модель, обученная на данных с анонимного имиджборда > ⚠️ ВНИМАНИЕ: Модель обучена на **нефильтрованных** и **непроверенных** данных с публичного анонимного форума (2ch.hk / Двач). > В содержимом могут присутствовать **брань, токсичный контент, NSFW**, а также **более тяжёлые формы агрессии, оскорблений и шок-контента**. ## 🧠 Обзор Эта модель является экспериментом в области обучения языковых моделей на неформальной русской речи. Она основана на предварительно обученной модели [`sberbank-ai/rugpt2`](https://huggingface.co/ai-forever/rugpt3small_based_on_gpt2?text=%D0%9E%D0%B4%D0%BD%D0%B0%D0%B6%D0%B4%D1%8B) и дообучена на данных с форума 2ch.hk. В обучающем наборе использовались: - Заголовки тредов как входные запросы (вопросы), - Первые 1–3 ответа в треде как отклик (реплики "бота"). ### ❗ ВАЖНО Автор **не проводил ручной модерации или фильтрации** содержимого. Контент получен "как есть", автоматически. Автор **не несёт ответственности** за содержание сгенерированных текстов или возможный вред от их использования. Модель не предназначена для продакшен-использования. --- ## 🎓 Цель проекта Модель предназначена **исключительно для исследовательских и образовательных целей**: - Анализ структуры русскоязычного неформального диалога, - Исследование токсичности в языковых моделях, - Эксперименты с малыми объёмами данных. --- ## ⚠️ Предупреждение о содержании Модель может генерировать тексты, содержащие: - Оскорбления, дискриминацию, ненормативную лексику, - Насилие, угрозы, NSFW и прочий шок-контент. **Не используйте эту модель** в продуктах, чатах с пользователями, в системах рекомендаций, в образовательных учреждениях или в чувствительных сценариях. Использование модели — **исключительно на ваш страх и риск**. Автор **не несёт ответственности** за любые последствия, вызванные использованием модели. --- ## 📜 Лицензия Модель и обучающий набор распространяются по лицензии **OpenRAIL-M**. OpenRAIL — это лицензия, разработанная для языковых моделей и направленная на обеспечение **этичного, безопасного и ограниченного использования**. - Использование разрешено только при соблюдении условий ответственного применения. - Коммерческое использование запрещено без отдельного разрешения. - Пользователь обязан атрибутировать оригинального автора и использовать модель в соответствии с положениями лицензии. 📄 Подробнее: [https://www.bigcode-project.org/docs/pages/bigcode-openrail/](https://www.bigcode-project.org/docs/pages/bigcode-openrail/)

MJ92/AceGPT-v2-8B-Chat_finetuned_5000_fr

MJ92

2025-06-19T15:18:25Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-19T15:06:44Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

morturr/Llama-2-7b-hf-PAIR_dadjokes_headlines-COMB-dadjokes-comb-1-seed-42-2025-06-19

morturr

2025-06-19T15:17:40Z

0

peft

[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]

null

2025-06-19T15:17:19Z

--- library_name: peft license: llama2 base_model: meta-llama/Llama-2-7b-hf tags: - trl - sft - generated_from_trainer model-index: - name: Llama-2-7b-hf-PAIR_dadjokes_headlines-COMB-dadjokes-comb-1-seed-42-2025-06-19 results: [] ---  # Llama-2-7b-hf-PAIR_dadjokes_headlines-COMB-dadjokes-comb-1-seed-42-2025-06-19 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.13.2 - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.0.2 - Tokenizers 0.20.1

jasonxubin/jielin-lora

jasonxubin

2025-06-19T15:17:31Z

0

null

[ "license:other", "region:us" ]

null

2025-06-19T14:38:18Z

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md ---

LarryAIDraw/kamisato_ayakaPDXL_scarxzys

LarryAIDraw

2025-06-19T15:17:06Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2025-06-19T15:15:31Z

--- license: creativeml-openrail-m --- https://civitai.com/models/1022127/pony-kamisato-ayaka-or-genshin-impact

LarryAIDraw/arknights_skadi_ponyXL

LarryAIDraw

2025-06-19T15:16:33Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2025-06-19T15:12:06Z

--- license: creativeml-openrail-m --- https://civitai.com/models/326557?modelVersionId=366029

rodrigomt/gemma-merge

rodrigomt

2025-06-19T15:11:21Z

0

null

[ "safetensors", "gemma3", "merge", "mergekit", "lazymergekit", "CEIA-UFG/Gemma-3-Gaia-PT-BR-4b-it", "soob3123/amoral-gemma3-4B-v2", "base_model:CEIA-UFG/Gemma-3-Gaia-PT-BR-4b-it", "base_model:merge:CEIA-UFG/Gemma-3-Gaia-PT-BR-4b-it", "base_model:soob3123/amoral-gemma3-4B-v2", "base_model:merge:soob3123/amoral-gemma3-4B-v2", "region:us" ]

null

2025-06-19T02:55:19Z

--- base_model: - CEIA-UFG/Gemma-3-Gaia-PT-BR-4b-it - soob3123/amoral-gemma3-4B-v2 tags: - merge - mergekit - lazymergekit - CEIA-UFG/Gemma-3-Gaia-PT-BR-4b-it - soob3123/amoral-gemma3-4B-v2 --- # gemma-merge gemma-merge is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [CEIA-UFG/Gemma-3-Gaia-PT-BR-4b-it](https://huggingface.co/CEIA-UFG/Gemma-3-Gaia-PT-BR-4b-it) * [soob3123/amoral-gemma3-4B-v2](https://huggingface.co/soob3123/amoral-gemma3-4B-v2) ## 🧩 Configuration ```yaml models: - model: CEIA-UFG/Gemma-3-Gaia-PT-BR-4b-it parameters: density: 0.5 weight: 0.5 - model: soob3123/amoral-gemma3-4B-v2 parameters: density: 0.5 weight: 0.5 merge_method: ties base_model: unsloth/gemma-3-4b-pt parameters: normalize: true int8_mask: true dtype: bfloat16 tokenizer: source: unsloth/gemma-3-4b-pt ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "rodrigomt/gemma-merge" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```

raul111204/gpt-neo-125m-xsum-raul3-b

raul111204

2025-06-19T15:11:20Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "autotrain", "text-generation-inference", "text-generation", "peft", "conversational", "dataset:mia-llm/xsum-raw-MIA", "base_model:EleutherAI/gpt-neo-125m", "base_model:finetune:EleutherAI/gpt-neo-125m", "license:other", "endpoints_compatible", "region:us" ]

text-generation

2025-06-19T14:23:26Z

--- tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers base_model: EleutherAI/gpt-neo-125m widget: - messages: - role: user content: What is your favorite condiment? license: other datasets: - mia-llm/xsum-raw-MIA --- # Model Trained Using AutoTrain This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). # Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_path = "PATH_TO_THIS_REPO" tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, device_map="auto", torch_dtype='auto' ).eval() # Prompt content: "hi" messages = [ {"role": "user", "content": "hi"} ] input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') output_ids = model.generate(input_ids.to('cuda')) response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) # Model response: "Hello! How can I assist you today?" print(response) ```

BootesVoid/cmbyrkjtk04cprdqsuhkq1b61_cmc23uo8s0c1yrdqsimijbqb2

BootesVoid

2025-06-19T15:11:11Z

0

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2025-06-19T15:11:09Z

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: TRACEY --- # Cmbyrkjtk04Cprdqsuhkq1B61_Cmc23Uo8S0C1Yrdqsimijbqb2 <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `TRACEY` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "TRACEY", "lora_weights": "https://huggingface.co/BootesVoid/cmbyrkjtk04cprdqsuhkq1b61_cmc23uo8s0c1yrdqsimijbqb2/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmbyrkjtk04cprdqsuhkq1b61_cmc23uo8s0c1yrdqsimijbqb2', weight_name='lora.safetensors') image = pipeline('TRACEY').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmbyrkjtk04cprdqsuhkq1b61_cmc23uo8s0c1yrdqsimijbqb2/discussions) to add images that show off what you’ve made with this LoRA.

Richard9905/full-merged-bible-model

Richard9905

2025-06-19T15:11:00Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]

text-generation

2025-06-19T15:07:23Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

LarryAIDraw/miyakoPDXL_scarxzys

LarryAIDraw

2025-06-19T15:10:19Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2025-06-19T15:08:29Z

--- license: creativeml-openrail-m --- https://civitai.com/models/632492/pony-tsukiyuki-miyako-or-blue-archive

zeerakwyne/test8_doc-splitter-llama-3-2-3B-20-epoch_merged

zeerakwyne

2025-06-19T15:09:56Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2025-06-19T15:09:13Z

--- base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama license: apache-2.0 language: - en --- # Uploaded finetuned model - **Developed by:** zeerakwyne - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3.2-3b-instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

LarryAIDraw/Nonoa_Miyamae_anime-44

LarryAIDraw

2025-06-19T15:05:01Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2025-06-19T15:01:29Z

--- license: creativeml-openrail-m --- https://civitai.com/models/704533/nonoa-miyamae-or-alya-sometimes-hides-her-feelings-in-russian-or

Mariogver/detr-finetuned-microglia_3

Mariogver

2025-06-19T15:04:43Z

0

transformers

[ "transformers", "safetensors", "detr", "object-detection", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

object-detection

2025-06-19T15:04:30Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

sanchit42/llama3.1-8B-instruct-29reports-lora256-slim

sanchit42

2025-06-19T14:59:44Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-19T14:56:01Z

--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

liuh6/whisper-tiny_to_Chinese_accent

liuh6

2025-06-19T14:59:17Z

40

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "en", "dataset:Chinese_english", "base_model:openai/whisper-tiny", "base_model:finetune:openai/whisper-tiny", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2025-03-08T01:44:44Z

--- library_name: transformers language: - en license: apache-2.0 base_model: openai/whisper-tiny tags: - hf-asr-leaderboard - generated_from_trainer datasets: - Chinese_english metrics: - wer model-index: - name: Whisper tiny Chinese with pitch pertubation results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Chinese English type: Chinese_english args: 'config: default, split: test' metrics: - name: Wer type: wer value: 16.187450357426528 ---  # Whisper tiny Chinese with pitch pertubation This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Chinese English dataset. It achieves the following results on the evaluation set: - Loss: 0.3669 - Wer: 16.1875 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 1500 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:-------:| | 0.2024 | 1.6667 | 500 | 0.3648 | 16.4257 | | 0.0151 | 3.3333 | 1000 | 0.3584 | 16.7911 | | 0.0035 | 5.0 | 1500 | 0.3669 | 16.1875 | ### Framework versions - Transformers 4.49.0 - Pytorch 2.6.0+cu124 - Datasets 3.6.0 - Tokenizers 0.21.1

Richard9905/lora-bible-model

Richard9905

2025-06-19T14:58:40Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-06-19T14:58:31Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

BootesVoid/cmc0un2u0098srdqs7tomm6xx_cmc3f9kss006vnx8dflbcsdbb

BootesVoid

2025-06-19T14:58:35Z

0

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2025-06-19T14:58:34Z

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: SEXY --- # Cmc0Un2U0098Srdqs7Tomm6Xx_Cmc3F9Kss006Vnx8Dflbcsdbb <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `SEXY` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "SEXY", "lora_weights": "https://huggingface.co/BootesVoid/cmc0un2u0098srdqs7tomm6xx_cmc3f9kss006vnx8dflbcsdbb/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmc0un2u0098srdqs7tomm6xx_cmc3f9kss006vnx8dflbcsdbb', weight_name='lora.safetensors') image = pipeline('SEXY').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmc0un2u0098srdqs7tomm6xx_cmc3f9kss006vnx8dflbcsdbb/discussions) to add images that show off what you’ve made with this LoRA.

samtse123/finetune_model

samtse123

2025-06-19T14:57:58Z

0

transformers

[ "transformers", "gguf", "qwen2", "text-generation-inference", "unsloth", "qwen3", "en", "base_model:unsloth/Qwen3-1.7B-unsloth-bnb-4bit", "base_model:quantized:unsloth/Qwen3-1.7B-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-06-19T13:55:09Z

--- base_model: unsloth/Qwen3-1.7B-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 - gguf license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** samtse123 - **License:** apache-2.0 - **Finetuned from model :** unsloth/Qwen3-1.7B-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

nicofarr/panns_ResNet22

nicofarr

2025-06-19T14:55:50Z

0

pytorch

[ "pytorch", "safetensors", "ResNet22", "audio", "model_hub_mixin", "panns", "pytorch_model_hub_mixin", "tagging", "license:apache-2.0", "region:us" ]

null

2025-06-19T14:52:31Z

--- library_name: pytorch license: apache-2.0 tags: - audio - model_hub_mixin - panns - pytorch_model_hub_mixin - tagging --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Library: https://github.com/qiuqiangkong/audioset_tagging_cnn - Docs: https://github.com/qiuqiangkong/audioset_tagging_cnn

ik-ram28/BioMistral-CPT-SFT-7B

ik-ram28

2025-06-19T14:54:40Z

35

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "medical", "conversational", "fr", "en", "base_model:BioMistral/BioMistral-7B", "base_model:finetune:BioMistral/BioMistral-7B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-02-08T23:22:52Z

--- library_name: transformers tags: - medical license: apache-2.0 language: - fr - en base_model: - ik-ram28/BioMistral-CPT-7B - BioMistral/BioMistral-7B --- ## Model Description BioMistral-CPT-SFT-7B is a French medical language model based on BioMistral-7B, adapted for French medical domain applications through a combined approach of Continual Pre-Training (CPT) followed by Supervised Fine-Tuning (SFT). ## Model Details - **Model Type**: Causal Language Model - **Base Model**: BioMistral-7B - **Language**: French (adapted from English medical model) - **Domain**: Medical/Healthcare - **Parameters**: 7 billion - **License**: Apache 2.0 - **Paper**: [Adaptation des connaissances médicales pour les grands modèles de langue : Stratégies et analyse comparative](https://github.com/ikram28/medllm-strategies) ## Training Details ### Continual Pre-Training (CPT) - **Dataset**: NACHOS corpus (opeN crAwled frenCh Healthcare cOrpuS) - **Size**: 7.4 GB of French medical texts - **Word Count**: Over 1 billion words - **Sources**: 24 French medical websites - **Training Duration**: 2.8 epochs - **Hardware**: 32 NVIDIA H100 80GB GPUs - **Training Time**: 11 hours - **Optimizer**: AdamW - **Learning Rate**: 2e-5 - **Weight Decay**: 0.01 - **Batch Size**: 16 with gradient accumulation of 2 ### Supervised Fine-Tuning (SFT) - **Dataset**: 30K French medical question-answer pairs - 10K native French medical questions - 10K translated medical questions from English resources - 10K generated questions from French medical texts - **Method**: DoRA (Weight-Decomposed Low-Rank Adaptation) - **Training Duration**: 10 epochs - **Hardware**: 1 NVIDIA H100 80GB GPU - **Training Time**: 42 hours - **Rank**: 16 - **Alpha**: 16 - **Learning Rate**: 2e-5 - **Batch Size**: 4 ## Computational Impact - **Total Training Time**: 53 hours (11h CPT + 42h SFT) - **Hardware**: 32 GPU H100 + 1 GPU H100 - **Carbon Emissions**: 10.11 kgCO2e (9.04 + 1.07) ## Ethical Considerations - **Medical Accuracy**: This model is for research and educational purposes only. Performance limitations make it unsuitable for critical medical applications - **Bias**: May contain biases from both English and French medical literature ## Citation If you use this model, please cite: ```bibtex ``` ## Contact For questions about this model, please contact: [email protected]

hectordiazgomez/sirio-4b-translation

hectordiazgomez

2025-06-19T14:48:11Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-06-19T14:47:24Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

alakxender/flan-t5-base-alpaca-dv5

alakxender

2025-06-19T14:47:02Z

136

0

transformers

[ "transformers", "safetensors", "t5", "text2text-generation", "dhivehi", "gpt", "llm", "thaana", "text-gen", "dv", "dataset:alakxender/alpaca_dhivehi", "base_model:google/flan-t5-base", "base_model:finetune:google/flan-t5-base", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2025-05-31T08:18:13Z

--- library_name: transformers tags: - dhivehi - gpt - llm - thaana - text-gen license: mit datasets: - alakxender/alpaca_dhivehi language: - dv metrics: - rouge base_model: - google/flan-t5-base --- # Alpaca Dhivehi Fine-Tuned Flan-T5 This repository contains a **fine-tuned Flan-T5** model on the **Alpaca Dhivehi dataset**, aimed at enabling Dhivehi language instruction-following tasks. ***Note: The model can follow instructions and inputs to some extent, but it’s not strictly trained for perfect adherence. Outputs may be partially aligned but are not guaranteed to be fully accurate. Treat results as experimental.*** ## Model Details - **Base model**: `google/flan-t5-small` (or whichever size you used) - **Dataset**: Alpaca Dhivehi , Translation from English to Dhivehi - **Training epochs**: 5 - **Final evaluation**: - `eval_loss`: 2.59 - `ROUGE-1`: 0.10 - `ROUGE-2`: 0.03 - `ROUGE-L`: 0.107 ## Usage To **run inference** using the fine-tuned model: ```python import torch from transformers import T5Tokenizer, T5ForConditionalGeneration MODEL_PATH = "alakxender/flan-t5-base-alpaca-dv5" device = torch.device("cuda" if torch.cuda.is_available() else "cpu") tokenizer = T5Tokenizer.from_pretrained(MODEL_PATH) model = T5ForConditionalGeneration.from_pretrained(MODEL_PATH).to(device) def generate_response(instruction, input_text): combined_input = f"{instruction.strip()} {input_text.strip()}" if input_text else instruction.strip() inputs = tokenizer(combined_input, return_tensors="pt", truncation=True, max_length=256).to(device) output_ids = model.generate( **inputs, max_new_tokens=256, num_beams=8, repetition_penalty=1.5, no_repeat_ngram_size=3, do_sample=True, early_stopping=True, temperature=0.1 ) decoded_output = tokenizer.decode(output_ids[0], skip_special_tokens=True) return decoded_output # Example usage: instruction = "ދީފައިވާ މައުޟޫޢާ ބެހޭގޮތުން ކުރު ޕެރެގްރާފެއް ލިޔެލާށެވެ." input_text = "އިއާދަކުރަނިވި ހަކަތަ ބޭނުންކުރުމުގެ މުހިންމުކަން" print(generate_response(instruction, input_text)) އިއާދަކުރަނިވި ހަކަތަ ބޭނުންކުރުމުގެ މުހިންމު އެއް މައުޟޫއަކީ ސޯލާ، ވިންޑް، ހައިޑްރޯ، ޖިއޮތަރމަލް، އަދި ހައިޑްރޯއިލެކްޓްރިކް ޕަވަރ ފަދަ އިއާދަކުރަނިވި ހަކަތައިން ގްރީންހައުސް ގޭސްތައް ބޭރުވުން ..... ``` ## Evaluation Results From the last evaluation: ``` { 'eval_loss': 2.591374158859253, 'eval_rouge1': 0.10920254665663279, 'eval_rouge2': 0.03587297080345582, 'eval_rougeL': 0.10796498746412672, 'eval_rougeLsum': 0.1083282268650986, 'eval_runtime': 1204.3847, 'eval_samples_per_second': 4.298, 'eval_steps_per_second': 2.149, 'epoch': 5.0 } ``` ## Notes - This fine-tuned model is experimental and intended for research on Dhivehi-language instruction-following tasks.

l0tr1k/photography-mistral-16bit-merged-new

l0tr1k

2025-06-19T14:37:07Z

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-19T14:33:27Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

vcabeli/Qwen3-8B-Open-R1-GRPO-spatial-dea

vcabeli

2025-06-19T14:30:42Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "generated_from_trainer", "trl", "grpo", "conversational", "arxiv:2402.03300", "base_model:Qwen/Qwen3-8B", "base_model:finetune:Qwen/Qwen3-8B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-19T14:06:23Z

--- base_model: Qwen/Qwen3-8B library_name: transformers model_name: Qwen3-8B-Open-R1-GRPO-spatial-dea tags: - generated_from_trainer - trl - grpo licence: license --- # Model Card for Qwen3-8B-Open-R1-GRPO-spatial-dea This model is a fine-tuned version of [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="vcabeli/Qwen3-8B-Open-R1-GRPO-spatial-dea", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/vincent-cabeli-owkin/huggingface/runs/zolvk2vf) This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300). ### Framework versions - TRL: 0.18.0 - Transformers: 4.52.3 - Pytorch: 2.6.0 - Datasets: 3.6.0 - Tokenizers: 0.21.1 ## Citations Cite GRPO as: ```bibtex @article{zhihong2024deepseekmath, title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}}, author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo}, year = 2024, eprint = {arXiv:2402.03300}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

deepkeep-ai/gemma-2-2b-pii-token-classifier

deepkeep-ai

2025-06-19T14:27:40Z

0

transformers

[ "transformers", "safetensors", "gemma2", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

token-classification

2025-06-18T12:49:36Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

wkang123/WellKang-v0.1.1.1

wkang123

2025-06-19T14:26:19Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-06-19T14:26:08Z

--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** wkang123 - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

tomaarsen/csr-mxbai-embed-large-v1-nq-updated-reconstruction-2

tomaarsen

2025-06-19T14:25:19Z

0

sentence-transformers

[ "sentence-transformers", "safetensors", "bert", "sparse-encoder", "sparse", "csr", "generated_from_trainer", "dataset_size:99000", "loss:CSRLoss", "loss:SparseMultipleNegativesRankingLoss", "feature-extraction", "en", "dataset:sentence-transformers/natural-questions", "arxiv:1908.10084", "arxiv:2503.01776", "arxiv:1705.00652", "base_model:mixedbread-ai/mxbai-embed-large-v1", "base_model:finetune:mixedbread-ai/mxbai-embed-large-v1", "license:apache-2.0", "model-index", "co2_eq_emissions", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2025-06-19T14:25:12Z

--- language: - en license: apache-2.0 tags: - sentence-transformers - sparse-encoder - sparse - csr - generated_from_trainer - dataset_size:99000 - loss:CSRLoss - loss:SparseMultipleNegativesRankingLoss base_model: mixedbread-ai/mxbai-embed-large-v1 widget: - text: Saudi Arabia–United Arab Emirates relations However, the UAE and Saudi Arabia continue to take somewhat differing stances on regional conflicts such the Yemeni Civil War, where the UAE opposes Al-Islah, and supports the Southern Movement, which has fought against Saudi-backed forces, and the Syrian Civil War, where the UAE has disagreed with Saudi support for Islamist movements.[4] - text: Economy of New Zealand New Zealand's diverse market economy has a sizable service sector, accounting for 63% of all GDP activity in 2013.[17] Large scale manufacturing industries include aluminium production, food processing, metal fabrication, wood and paper products. Mining, manufacturing, electricity, gas, water, and waste services accounted for 16.5% of GDP in 2013.[17] The primary sector continues to dominate New Zealand's exports, despite accounting for 6.5% of GDP in 2013.[17] - text: who was the first president of indian science congress meeting held in kolkata in 1914 - text: Get Over It (Eagles song) "Get Over It" is a song by the Eagles released as a single after a fourteen-year breakup. It was also the first song written by bandmates Don Henley and Glenn Frey when the band reunited. "Get Over It" was played live for the first time during their Hell Freezes Over tour in 1994. It returned the band to the U.S. Top 40 after a fourteen-year absence, peaking at No. 31 on the Billboard Hot 100 chart. It also hit No. 4 on the Billboard Mainstream Rock Tracks chart. The song was not played live by the Eagles after the "Hell Freezes Over" tour in 1994. It remains the group's last Top 40 hit in the U.S. - text: 'Cornelius the Centurion Cornelius (Greek: Κορνήλιος) was a Roman centurion who is considered by Christians to be one of the first Gentiles to convert to the faith, as related in Acts of the Apostles.' datasets: - sentence-transformers/natural-questions pipeline_tag: feature-extraction library_name: sentence-transformers metrics: - dot_accuracy@1 - dot_accuracy@3 - dot_accuracy@5 - dot_accuracy@10 - dot_precision@1 - dot_precision@3 - dot_precision@5 - dot_precision@10 - dot_recall@1 - dot_recall@3 - dot_recall@5 - dot_recall@10 - dot_ndcg@10 - dot_mrr@10 - dot_map@100 - query_active_dims - query_sparsity_ratio - corpus_active_dims - corpus_sparsity_ratio co2_eq_emissions: emissions: 53.0273650168183 energy_consumed: 0.13642164181511365 source: codecarbon training_type: fine-tuning on_cloud: false cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K ram_total_size: 31.777088165283203 hours_used: 0.41 hardware_used: 1 x NVIDIA GeForce RTX 3090 model-index: - name: Sparse CSR model trained on Natural Questions results: - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO 128 type: NanoMSMARCO_128 metrics: - type: dot_accuracy@1 value: 0.38 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.66 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.72 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.82 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.38 name: Dot Precision@1 - type: dot_precision@3 value: 0.22 name: Dot Precision@3 - type: dot_precision@5 value: 0.14400000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.08199999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.38 name: Dot Recall@1 - type: dot_recall@3 value: 0.66 name: Dot Recall@3 - type: dot_recall@5 value: 0.72 name: Dot Recall@5 - type: dot_recall@10 value: 0.82 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6074833126260415 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5392698412698412 name: Dot Mrr@10 - type: dot_map@100 value: 0.5478391044500884 name: Dot Map@100 - type: query_active_dims value: 128.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.96875 name: Query Sparsity Ratio - type: corpus_active_dims value: 128.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.96875 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNFCorpus 128 type: NanoNFCorpus_128 metrics: - type: dot_accuracy@1 value: 0.44 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.54 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.64 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.68 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.44 name: Dot Precision@1 - type: dot_precision@3 value: 0.3133333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.28 name: Dot Precision@5 - type: dot_precision@10 value: 0.24600000000000002 name: Dot Precision@10 - type: dot_recall@1 value: 0.045132854073603 name: Dot Recall@1 - type: dot_recall@3 value: 0.06751477851868476 name: Dot Recall@3 - type: dot_recall@5 value: 0.08765169300408888 name: Dot Recall@5 - type: dot_recall@10 value: 0.12035202437952344 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.3037747903284991 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5081904761904761 name: Dot Mrr@10 - type: dot_map@100 value: 0.13867493157888547 name: Dot Map@100 - type: query_active_dims value: 128.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.96875 name: Query Sparsity Ratio - type: corpus_active_dims value: 128.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.96875 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNQ 128 type: NanoNQ_128 metrics: - type: dot_accuracy@1 value: 0.48 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.66 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.7 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.84 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.48 name: Dot Precision@1 - type: dot_precision@3 value: 0.22666666666666668 name: Dot Precision@3 - type: dot_precision@5 value: 0.14800000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.08999999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.45 name: Dot Recall@1 - type: dot_recall@3 value: 0.62 name: Dot Recall@3 - type: dot_recall@5 value: 0.67 name: Dot Recall@5 - type: dot_recall@10 value: 0.81 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6337677207897237 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5932936507936507 name: Dot Mrr@10 - type: dot_map@100 value: 0.5761859932841973 name: Dot Map@100 - type: query_active_dims value: 128.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.96875 name: Query Sparsity Ratio - type: corpus_active_dims value: 128.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.96875 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean 128 type: NanoBEIR_mean_128 metrics: - type: dot_accuracy@1 value: 0.43333333333333335 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.6200000000000001 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.6866666666666665 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.7799999999999999 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.43333333333333335 name: Dot Precision@1 - type: dot_precision@3 value: 0.25333333333333335 name: Dot Precision@3 - type: dot_precision@5 value: 0.19066666666666668 name: Dot Precision@5 - type: dot_precision@10 value: 0.13933333333333334 name: Dot Precision@10 - type: dot_recall@1 value: 0.2917109513578677 name: Dot Recall@1 - type: dot_recall@3 value: 0.44917159283956165 name: Dot Recall@3 - type: dot_recall@5 value: 0.49255056433469635 name: Dot Recall@5 - type: dot_recall@10 value: 0.5834506747931745 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5150086079147548 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5469179894179893 name: Dot Mrr@10 - type: dot_map@100 value: 0.42090000977105707 name: Dot Map@100 - 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type: dot_recall@5 value: 0.74 name: Dot Recall@5 - type: dot_recall@10 value: 0.84 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6405150998246686 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5768809523809523 name: Dot Mrr@10 - type: dot_map@100 value: 0.5851061967133396 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNFCorpus 256 type: NanoNFCorpus_256 metrics: - type: dot_accuracy@1 value: 0.42 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.58 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.6 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.62 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.42 name: Dot Precision@1 - type: dot_precision@3 value: 0.37333333333333324 name: Dot Precision@3 - 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type: dot_accuracy@3 value: 0.7 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.8 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.84 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.54 name: Dot Precision@1 - type: dot_precision@3 value: 0.24 name: Dot Precision@3 - type: dot_precision@5 value: 0.16799999999999998 name: Dot Precision@5 - type: dot_precision@10 value: 0.092 name: Dot Precision@10 - type: dot_recall@1 value: 0.51 name: Dot Recall@1 - type: dot_recall@3 value: 0.66 name: Dot Recall@3 - type: dot_recall@5 value: 0.75 name: Dot Recall@5 - type: dot_recall@10 value: 0.81 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6642484604451891 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6294126984126983 name: Dot Mrr@10 - type: dot_map@100 value: 0.6162769242153361 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean 256 type: NanoBEIR_mean_256 metrics: - type: dot_accuracy@1 value: 0.4666666666666666 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.64 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.7133333333333333 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.7666666666666666 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.4666666666666666 name: Dot Precision@1 - type: dot_precision@3 value: 0.2755555555555555 name: Dot Precision@3 - type: dot_precision@5 value: 0.21333333333333335 name: Dot Precision@5 - type: dot_precision@10 value: 0.1413333333333333 name: Dot Precision@10 - type: dot_recall@1 value: 0.3317079824798988 name: Dot Recall@1 - type: dot_recall@3 value: 0.46027749545078783 name: Dot Recall@3 - type: dot_recall@5 value: 0.5297984177125533 name: Dot Recall@5 - type: dot_recall@10 value: 0.5919758437819499 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.5409587648376507 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.570153439153439 name: Dot Mrr@10 - type: dot_map@100 value: 0.4532857697703016 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoClimateFEVER type: NanoClimateFEVER metrics: - type: dot_accuracy@1 value: 0.28 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.52 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.7 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.8 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.28 name: Dot Precision@1 - type: dot_precision@3 value: 0.18666666666666668 name: Dot Precision@3 - type: dot_precision@5 value: 0.16799999999999998 name: Dot Precision@5 - type: dot_precision@10 value: 0.10799999999999997 name: Dot Precision@10 - type: dot_recall@1 value: 0.12166666666666665 name: Dot Recall@1 - type: dot_recall@3 value: 0.23233333333333334 name: Dot Recall@3 - type: dot_recall@5 value: 0.348 name: Dot Recall@5 - type: dot_recall@10 value: 0.42633333333333334 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.33235923006734097 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.43644444444444447 name: Dot Mrr@10 - type: dot_map@100 value: 0.24903211945618525 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoDBPedia type: NanoDBPedia metrics: - type: dot_accuracy@1 value: 0.8 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.9 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.9 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.92 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.8 name: Dot Precision@1 - type: dot_precision@3 value: 0.6466666666666666 name: Dot Precision@3 - type: dot_precision@5 value: 0.56 name: Dot Precision@5 - type: dot_precision@10 value: 0.474 name: Dot Precision@10 - type: dot_recall@1 value: 0.09128542236179474 name: Dot Recall@1 - type: dot_recall@3 value: 0.17409405829521904 name: Dot Recall@3 - type: dot_recall@5 value: 0.22516141018064886 name: Dot Recall@5 - type: dot_recall@10 value: 0.321390285824061 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.600179050204524 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.8425 name: Dot Mrr@10 - type: dot_map@100 value: 0.45264984932006563 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoFEVER type: NanoFEVER metrics: - type: dot_accuracy@1 value: 0.84 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.92 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.96 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.96 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.84 name: Dot Precision@1 - type: dot_precision@3 value: 0.32 name: Dot Precision@3 - type: dot_precision@5 value: 0.19999999999999996 name: Dot Precision@5 - type: dot_precision@10 value: 0.09999999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.7866666666666667 name: Dot Recall@1 - type: dot_recall@3 value: 0.8866666666666667 name: Dot Recall@3 - type: dot_recall@5 value: 0.9266666666666667 name: Dot Recall@5 - type: dot_recall@10 value: 0.9266666666666667 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.8816129048397259 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.89 name: Dot Mrr@10 - type: dot_map@100 value: 0.8589881484317317 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoFiQA2018 type: NanoFiQA2018 metrics: - type: dot_accuracy@1 value: 0.48 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.6 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.64 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.74 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.48 name: Dot Precision@1 - type: dot_precision@3 value: 0.3066666666666667 name: Dot Precision@3 - type: dot_precision@5 value: 0.22399999999999998 name: Dot Precision@5 - type: dot_precision@10 value: 0.13599999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.2592460317460317 name: Dot Recall@1 - type: dot_recall@3 value: 0.39734920634920634 name: Dot Recall@3 - type: dot_recall@5 value: 0.4497857142857143 name: Dot Recall@5 - type: dot_recall@10 value: 0.5795634920634921 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.48812055653800884 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5517460317460319 name: Dot Mrr@10 - type: dot_map@100 value: 0.42554170336694114 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoHotpotQA type: NanoHotpotQA metrics: - type: dot_accuracy@1 value: 0.84 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.96 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.96 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.98 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.84 name: Dot Precision@1 - type: dot_precision@3 value: 0.5133333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.32799999999999996 name: Dot Precision@5 - type: dot_precision@10 value: 0.16999999999999996 name: Dot Precision@10 - type: dot_recall@1 value: 0.42 name: Dot Recall@1 - type: dot_recall@3 value: 0.77 name: Dot Recall@3 - type: dot_recall@5 value: 0.82 name: Dot Recall@5 - type: dot_recall@10 value: 0.85 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.8106522538764799 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.8966666666666666 name: Dot Mrr@10 - type: dot_map@100 value: 0.7565706035126855 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoMSMARCO type: NanoMSMARCO metrics: - type: dot_accuracy@1 value: 0.44 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.62 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.74 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.84 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.44 name: Dot Precision@1 - type: dot_precision@3 value: 0.20666666666666667 name: Dot Precision@3 - type: dot_precision@5 value: 0.14800000000000002 name: Dot Precision@5 - type: dot_precision@10 value: 0.08399999999999999 name: Dot Precision@10 - type: dot_recall@1 value: 0.44 name: Dot Recall@1 - type: dot_recall@3 value: 0.62 name: Dot Recall@3 - type: dot_recall@5 value: 0.74 name: Dot Recall@5 - type: dot_recall@10 value: 0.84 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6329477813439243 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5677777777777777 name: Dot Mrr@10 - type: dot_map@100 value: 0.5762304873870092 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNFCorpus type: NanoNFCorpus metrics: - type: dot_accuracy@1 value: 0.42 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.56 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.64 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.66 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.42 name: Dot Precision@1 - type: dot_precision@3 value: 0.37999999999999995 name: Dot Precision@3 - type: dot_precision@5 value: 0.34800000000000003 name: Dot Precision@5 - type: dot_precision@10 value: 0.258 name: Dot Precision@10 - type: dot_recall@1 value: 0.04486258380333274 name: Dot Recall@1 - type: dot_recall@3 value: 0.08768477299713343 name: Dot Recall@3 - type: dot_recall@5 value: 0.10844641112515632 name: Dot Recall@5 - type: dot_recall@10 value: 0.135531563356284 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.3285187113745097 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5009999999999999 name: Dot Mrr@10 - type: dot_map@100 value: 0.16174125549238802 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoNQ type: NanoNQ metrics: - type: dot_accuracy@1 value: 0.58 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.7 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.8 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.82 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.58 name: Dot Precision@1 - type: dot_precision@3 value: 0.24 name: Dot Precision@3 - type: dot_precision@5 value: 0.16799999999999998 name: Dot Precision@5 - type: dot_precision@10 value: 0.08999999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.55 name: Dot Recall@1 - type: dot_recall@3 value: 0.66 name: Dot Recall@3 - type: dot_recall@5 value: 0.75 name: Dot Recall@5 - type: dot_recall@10 value: 0.79 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.677342414343143 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6521666666666666 name: Dot Mrr@10 - type: dot_map@100 value: 0.6420660106369513 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoQuoraRetrieval type: NanoQuoraRetrieval metrics: - type: dot_accuracy@1 value: 0.9 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 1.0 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 1.0 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 1.0 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.9 name: Dot Precision@1 - type: dot_precision@3 value: 0.4133333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.27199999999999996 name: Dot Precision@5 - type: dot_precision@10 value: 0.13799999999999998 name: Dot Precision@10 - type: dot_recall@1 value: 0.7773333333333333 name: Dot Recall@1 - type: dot_recall@3 value: 0.9620000000000001 name: Dot Recall@3 - type: dot_recall@5 value: 0.9933333333333334 name: Dot Recall@5 - type: dot_recall@10 value: 0.9966666666666666 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.9509657098958008 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.9466666666666665 name: Dot Mrr@10 - type: dot_map@100 value: 0.9297051282051282 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoSCIDOCS type: NanoSCIDOCS metrics: - type: dot_accuracy@1 value: 0.42 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.72 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.82 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.88 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.42 name: Dot Precision@1 - type: dot_precision@3 value: 0.35333333333333333 name: Dot Precision@3 - type: dot_precision@5 value: 0.3 name: Dot Precision@5 - type: dot_precision@10 value: 0.20800000000000002 name: Dot Precision@10 - type: dot_recall@1 value: 0.09066666666666666 name: Dot Recall@1 - type: dot_recall@3 value: 0.22166666666666665 name: Dot Recall@3 - type: dot_recall@5 value: 0.3096666666666667 name: Dot Recall@5 - type: dot_recall@10 value: 0.42566666666666664 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.4022717287490821 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5887222222222221 name: Dot Mrr@10 - type: dot_map@100 value: 0.32075091248131626 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoArguAna type: NanoArguAna metrics: - type: dot_accuracy@1 value: 0.38 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.7 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.8 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.92 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.38 name: Dot Precision@1 - type: dot_precision@3 value: 0.23333333333333336 name: Dot Precision@3 - type: dot_precision@5 value: 0.16 name: Dot Precision@5 - type: dot_precision@10 value: 0.092 name: Dot Precision@10 - type: dot_recall@1 value: 0.38 name: Dot Recall@1 - type: dot_recall@3 value: 0.7 name: Dot Recall@3 - type: dot_recall@5 value: 0.8 name: Dot Recall@5 - type: dot_recall@10 value: 0.92 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6550827948648061 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.5706349206349206 name: Dot Mrr@10 - type: dot_map@100 value: 0.5760927960927961 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoSciFact type: NanoSciFact metrics: - type: dot_accuracy@1 value: 0.62 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.72 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.76 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.84 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.62 name: Dot Precision@1 - type: dot_precision@3 value: 0.26666666666666666 name: Dot Precision@3 - type: dot_precision@5 value: 0.17199999999999996 name: Dot Precision@5 - type: dot_precision@10 value: 0.09599999999999997 name: Dot Precision@10 - type: dot_recall@1 value: 0.595 name: Dot Recall@1 - type: dot_recall@3 value: 0.705 name: Dot Recall@3 - type: dot_recall@5 value: 0.755 name: Dot Recall@5 - type: dot_recall@10 value: 0.84 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.7193800580696723 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6823888888888889 name: Dot Mrr@10 - type: dot_map@100 value: 0.6850911930363545 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-information-retrieval name: Sparse Information Retrieval dataset: name: NanoTouche2020 type: NanoTouche2020 metrics: - type: dot_accuracy@1 value: 0.4897959183673469 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.8367346938775511 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.9591836734693877 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.9795918367346939 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.4897959183673469 name: Dot Precision@1 - type: dot_precision@3 value: 0.5170068027210885 name: Dot Precision@3 - type: dot_precision@5 value: 0.5346938775510204 name: Dot Precision@5 - type: dot_precision@10 value: 0.4346938775510204 name: Dot Precision@10 - type: dot_recall@1 value: 0.03422245985964837 name: Dot Recall@1 - type: dot_recall@3 value: 0.10897367065265 name: Dot Recall@3 - type: dot_recall@5 value: 0.18115391425134045 name: Dot Recall@5 - type: dot_recall@10 value: 0.2884686031356881 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.47678328743473813 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6784580498866212 name: Dot Mrr@10 - type: dot_map@100 value: 0.3590479959667369 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio - task: type: sparse-nano-beir name: Sparse Nano BEIR dataset: name: NanoBEIR mean type: NanoBEIR_mean metrics: - type: dot_accuracy@1 value: 0.576138147566719 name: Dot Accuracy@1 - type: dot_accuracy@3 value: 0.7505180533751962 name: Dot Accuracy@3 - type: dot_accuracy@5 value: 0.821475667189953 name: Dot Accuracy@5 - type: dot_accuracy@10 value: 0.8722762951334379 name: Dot Accuracy@10 - type: dot_precision@1 value: 0.576138147566719 name: Dot Precision@1 - type: dot_precision@3 value: 0.3525902668759811 name: Dot Precision@3 - type: dot_precision@5 value: 0.27559183673469384 name: Dot Precision@5 - type: dot_precision@10 value: 0.18374568288854 name: Dot Precision@10 - type: dot_recall@1 value: 0.35314998700801087 name: Dot Recall@1 - type: dot_recall@3 value: 0.5019821826892981 name: Dot Recall@3 - type: dot_recall@5 value: 0.5697857012699635 name: Dot Recall@5 - type: dot_recall@10 value: 0.6415605598240661 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.6120166524309044 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.6773209488923774 name: Dot Mrr@10 - type: dot_map@100 value: 0.5379621694912531 name: Dot Map@100 - type: query_active_dims value: 256.0 name: Query Active Dims - type: query_sparsity_ratio value: 0.9375 name: Query Sparsity Ratio - type: corpus_active_dims value: 256.0 name: Corpus Active Dims - type: corpus_sparsity_ratio value: 0.9375 name: Corpus Sparsity Ratio --- # Sparse CSR model trained on Natural Questions This is a [CSR Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) on the [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) dataset using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 4096-dimensional sparse vector space with 256 maximum active dimensions and can be used for semantic search and sparse retrieval. ## Model Details ### Model Description - **Model Type:** CSR Sparse Encoder - **Base model:** [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1)  - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 4096 dimensions (trained with 256 maximum active dimensions) - **Similarity Function:** Dot Product - **Training Dataset:** - [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) - **Language:** en - **License:** apache-2.0 ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder) ### Full Model Architecture ``` SparseEncoder( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) (2): CSRSparsity({'input_dim': 1024, 'hidden_dim': 4096, 'k': 256, 'k_aux': 512, 'normalize': False, 'dead_threshold': 30}) ) ``` ## Usage ### Direct Usage (Sentence Transformers) First install the Sentence Transformers library: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python from sentence_transformers import SparseEncoder # Download from the 🤗 Hub model = SparseEncoder("tomaarsen/csr-mxbai-embed-large-v1-nq-updated-reconstruction-2") # Run inference queries = [ "who is cornelius in the book of acts", ] documents = [ 'Cornelius the Centurion Cornelius (Greek: Κορνήλιος) was a Roman centurion who is considered by Christians to be one of the first Gentiles to convert to the faith, as related in Acts of the Apostles.', "Joe Ranft Ranft reunited with Lasseter when he was hired by Pixar in 1991 as their head of story.[1] There he worked on all of their films produced up to 2006; this included Toy Story (for which he received an Academy Award nomination) and A Bug's Life, as the co-story writer and others as story supervisor. His final film was Cars. He also voiced characters in many of the films, including Heimlich the caterpillar in A Bug's Life, Wheezy the penguin in Toy Story 2, and Jacques the shrimp in Finding Nemo.[1]", 'Wonderful Tonight "Wonderful Tonight" is a ballad written by Eric Clapton. It was included on Clapton\'s 1977 album Slowhand. Clapton wrote the song about Pattie Boyd.[1] The female vocal harmonies on the song are provided by Marcella Detroit (then Marcy Levy) and Yvonne Elliman.', ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) print(query_embeddings.shape, document_embeddings.shape) # [1, 4096] [3, 4096] # Get the similarity scores for the embeddings similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) # tensor([[118.6570, 32.2072, 21.3971]]) ```    ## Evaluation ### Metrics #### Sparse Information Retrieval * Datasets: `NanoMSMARCO_128`, `NanoNFCorpus_128` and `NanoNQ_128` * Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters: ```json { "max_active_dims": 128 } ``` | Metric | NanoMSMARCO_128 | NanoNFCorpus_128 | NanoNQ_128 | |:----------------------|:----------------|:-----------------|:-----------| | dot_accuracy@1 | 0.38 | 0.44 | 0.48 | | dot_accuracy@3 | 0.66 | 0.54 | 0.66 | | dot_accuracy@5 | 0.72 | 0.64 | 0.7 | | dot_accuracy@10 | 0.82 | 0.68 | 0.84 | | dot_precision@1 | 0.38 | 0.44 | 0.48 | | dot_precision@3 | 0.22 | 0.3133 | 0.2267 | | dot_precision@5 | 0.144 | 0.28 | 0.148 | | dot_precision@10 | 0.082 | 0.246 | 0.09 | | dot_recall@1 | 0.38 | 0.0451 | 0.45 | | dot_recall@3 | 0.66 | 0.0675 | 0.62 | | dot_recall@5 | 0.72 | 0.0877 | 0.67 | | dot_recall@10 | 0.82 | 0.1204 | 0.81 | | **dot_ndcg@10** | **0.6075** | **0.3038** | **0.6338** | | dot_mrr@10 | 0.5393 | 0.5082 | 0.5933 | | dot_map@100 | 0.5478 | 0.1387 | 0.5762 | | query_active_dims | 128.0 | 128.0 | 128.0 | | query_sparsity_ratio | 0.9688 | 0.9688 | 0.9688 | | corpus_active_dims | 128.0 | 128.0 | 128.0 | | corpus_sparsity_ratio | 0.9688 | 0.9688 | 0.9688 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean_128` * Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco", "nfcorpus", "nq" ], "max_active_dims": 128 } ``` | Metric | Value | |:----------------------|:----------| | dot_accuracy@1 | 0.4333 | | dot_accuracy@3 | 0.62 | | dot_accuracy@5 | 0.6867 | | dot_accuracy@10 | 0.78 | | dot_precision@1 | 0.4333 | | dot_precision@3 | 0.2533 | | dot_precision@5 | 0.1907 | | dot_precision@10 | 0.1393 | | dot_recall@1 | 0.2917 | | dot_recall@3 | 0.4492 | | dot_recall@5 | 0.4926 | | dot_recall@10 | 0.5835 | | **dot_ndcg@10** | **0.515** | | dot_mrr@10 | 0.5469 | | dot_map@100 | 0.4209 | | query_active_dims | 128.0 | | query_sparsity_ratio | 0.9688 | | corpus_active_dims | 128.0 | | corpus_sparsity_ratio | 0.9688 | #### Sparse Information Retrieval * Datasets: `NanoMSMARCO_256`, `NanoNFCorpus_256` and `NanoNQ_256` * Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters: ```json { "max_active_dims": 256 } ``` | Metric | NanoMSMARCO_256 | NanoNFCorpus_256 | NanoNQ_256 | |:----------------------|:----------------|:-----------------|:-----------| | dot_accuracy@1 | 0.44 | 0.42 | 0.54 | | dot_accuracy@3 | 0.64 | 0.58 | 0.7 | | dot_accuracy@5 | 0.74 | 0.6 | 0.8 | | dot_accuracy@10 | 0.84 | 0.62 | 0.84 | | dot_precision@1 | 0.44 | 0.42 | 0.54 | | dot_precision@3 | 0.2133 | 0.3733 | 0.24 | | dot_precision@5 | 0.148 | 0.324 | 0.168 | | dot_precision@10 | 0.084 | 0.248 | 0.092 | | dot_recall@1 | 0.44 | 0.0451 | 0.51 | | dot_recall@3 | 0.64 | 0.0808 | 0.66 | | dot_recall@5 | 0.74 | 0.0994 | 0.75 | | dot_recall@10 | 0.84 | 0.1259 | 0.81 | | **dot_ndcg@10** | **0.6405** | **0.3181** | **0.6642** | | dot_mrr@10 | 0.5769 | 0.5042 | 0.6294 | | dot_map@100 | 0.5851 | 0.1585 | 0.6163 | | query_active_dims | 256.0 | 256.0 | 256.0 | | query_sparsity_ratio | 0.9375 | 0.9375 | 0.9375 | | corpus_active_dims | 256.0 | 256.0 | 256.0 | | corpus_sparsity_ratio | 0.9375 | 0.9375 | 0.9375 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean_256` * Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "msmarco", "nfcorpus", "nq" ], "max_active_dims": 256 } ``` | Metric | Value | |:----------------------|:----------| | dot_accuracy@1 | 0.4667 | | dot_accuracy@3 | 0.64 | | dot_accuracy@5 | 0.7133 | | dot_accuracy@10 | 0.7667 | | dot_precision@1 | 0.4667 | | dot_precision@3 | 0.2756 | | dot_precision@5 | 0.2133 | | dot_precision@10 | 0.1413 | | dot_recall@1 | 0.3317 | | dot_recall@3 | 0.4603 | | dot_recall@5 | 0.5298 | | dot_recall@10 | 0.592 | | **dot_ndcg@10** | **0.541** | | dot_mrr@10 | 0.5702 | | dot_map@100 | 0.4533 | | query_active_dims | 256.0 | | query_sparsity_ratio | 0.9375 | | corpus_active_dims | 256.0 | | corpus_sparsity_ratio | 0.9375 | #### Sparse Information Retrieval * Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020` * Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) | Metric | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 | |:----------------------|:-----------------|:------------|:-----------|:-------------|:-------------|:------------|:-------------|:-----------|:-------------------|:------------|:------------|:------------|:---------------| | dot_accuracy@1 | 0.28 | 0.8 | 0.84 | 0.48 | 0.84 | 0.44 | 0.42 | 0.58 | 0.9 | 0.42 | 0.38 | 0.62 | 0.4898 | | dot_accuracy@3 | 0.52 | 0.9 | 0.92 | 0.6 | 0.96 | 0.62 | 0.56 | 0.7 | 1.0 | 0.72 | 0.7 | 0.72 | 0.8367 | | dot_accuracy@5 | 0.7 | 0.9 | 0.96 | 0.64 | 0.96 | 0.74 | 0.64 | 0.8 | 1.0 | 0.82 | 0.8 | 0.76 | 0.9592 | | dot_accuracy@10 | 0.8 | 0.92 | 0.96 | 0.74 | 0.98 | 0.84 | 0.66 | 0.82 | 1.0 | 0.88 | 0.92 | 0.84 | 0.9796 | | dot_precision@1 | 0.28 | 0.8 | 0.84 | 0.48 | 0.84 | 0.44 | 0.42 | 0.58 | 0.9 | 0.42 | 0.38 | 0.62 | 0.4898 | | dot_precision@3 | 0.1867 | 0.6467 | 0.32 | 0.3067 | 0.5133 | 0.2067 | 0.38 | 0.24 | 0.4133 | 0.3533 | 0.2333 | 0.2667 | 0.517 | | dot_precision@5 | 0.168 | 0.56 | 0.2 | 0.224 | 0.328 | 0.148 | 0.348 | 0.168 | 0.272 | 0.3 | 0.16 | 0.172 | 0.5347 | | dot_precision@10 | 0.108 | 0.474 | 0.1 | 0.136 | 0.17 | 0.084 | 0.258 | 0.09 | 0.138 | 0.208 | 0.092 | 0.096 | 0.4347 | | dot_recall@1 | 0.1217 | 0.0913 | 0.7867 | 0.2592 | 0.42 | 0.44 | 0.0449 | 0.55 | 0.7773 | 0.0907 | 0.38 | 0.595 | 0.0342 | | dot_recall@3 | 0.2323 | 0.1741 | 0.8867 | 0.3973 | 0.77 | 0.62 | 0.0877 | 0.66 | 0.962 | 0.2217 | 0.7 | 0.705 | 0.109 | | dot_recall@5 | 0.348 | 0.2252 | 0.9267 | 0.4498 | 0.82 | 0.74 | 0.1084 | 0.75 | 0.9933 | 0.3097 | 0.8 | 0.755 | 0.1812 | | dot_recall@10 | 0.4263 | 0.3214 | 0.9267 | 0.5796 | 0.85 | 0.84 | 0.1355 | 0.79 | 0.9967 | 0.4257 | 0.92 | 0.84 | 0.2885 | | **dot_ndcg@10** | **0.3324** | **0.6002** | **0.8816** | **0.4881** | **0.8107** | **0.6329** | **0.3285** | **0.6773** | **0.951** | **0.4023** | **0.6551** | **0.7194** | **0.4768** | | dot_mrr@10 | 0.4364 | 0.8425 | 0.89 | 0.5517 | 0.8967 | 0.5678 | 0.501 | 0.6522 | 0.9467 | 0.5887 | 0.5706 | 0.6824 | 0.6785 | | dot_map@100 | 0.249 | 0.4526 | 0.859 | 0.4255 | 0.7566 | 0.5762 | 0.1617 | 0.6421 | 0.9297 | 0.3208 | 0.5761 | 0.6851 | 0.359 | | query_active_dims | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | | query_sparsity_ratio | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | | corpus_active_dims | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | 256.0 | | corpus_sparsity_ratio | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | 0.9375 | #### Sparse Nano BEIR * Dataset: `NanoBEIR_mean` * Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters: ```json { "dataset_names": [ "climatefever", "dbpedia", "fever", "fiqa2018", "hotpotqa", "msmarco", "nfcorpus", "nq", "quoraretrieval", "scidocs", "arguana", "scifact", "touche2020" ] } ``` | Metric | Value | |:----------------------|:----------| | dot_accuracy@1 | 0.5761 | | dot_accuracy@3 | 0.7505 | | dot_accuracy@5 | 0.8215 | | dot_accuracy@10 | 0.8723 | | dot_precision@1 | 0.5761 | | dot_precision@3 | 0.3526 | | dot_precision@5 | 0.2756 | | dot_precision@10 | 0.1837 | | dot_recall@1 | 0.3531 | | dot_recall@3 | 0.502 | | dot_recall@5 | 0.5698 | | dot_recall@10 | 0.6416 | | **dot_ndcg@10** | **0.612** | | dot_mrr@10 | 0.6773 | | dot_map@100 | 0.538 | | query_active_dims | 256.0 | | query_sparsity_ratio | 0.9375 | | corpus_active_dims | 256.0 | | corpus_sparsity_ratio | 0.9375 |   ## Training Details ### Training Dataset #### natural-questions * Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) * Size: 99,000 training samples * Columns: <code>query</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: | | query | answer | |:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 10 tokens</li><li>mean: 11.71 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 131.81 tokens</li><li>max: 450 tokens</li></ul> | * Samples: | query | answer | |:--------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>who played the father in papa don't preach</code> | <code>Alex McArthur Alex McArthur (born March 6, 1957) is an American actor.</code> | | <code>where was the location of the battle of hastings</code> | <code>Battle of Hastings The Battle of Hastings[a] was fought on 14 October 1066 between the Norman-French army of William, the Duke of Normandy, and an English army under the Anglo-Saxon King Harold Godwinson, beginning the Norman conquest of England. It took place approximately 7 miles (11 kilometres) northwest of Hastings, close to the present-day town of Battle, East Sussex, and was a decisive Norman victory.</code> | | <code>how many puppies can a dog give birth to</code> | <code>Canine reproduction The largest litter size to date was set by a Neapolitan Mastiff in Manea, Cambridgeshire, UK on November 29, 2004; the litter was 24 puppies.[22]</code> | * Loss: [<code>CSRLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#csrloss) with these parameters: ```json { "beta": 0.1, "gamma": 1.0, "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')" } ``` ### Evaluation Dataset #### natural-questions * Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17) * Size: 1,000 evaluation samples * Columns: <code>query</code> and <code>answer</code> * Approximate statistics based on the first 1000 samples: | | query | answer | |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 10 tokens</li><li>mean: 11.69 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 134.01 tokens</li><li>max: 512 tokens</li></ul> | * Samples: | query | answer | |:-------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>where is the tiber river located in italy</code> | <code>Tiber The Tiber (/ˈtaɪbər/, Latin: Tiberis,[1] Italian: Tevere [ˈteːvere])[2] is the third-longest river in Italy, rising in the Apennine Mountains in Emilia-Romagna and flowing 406 kilometres (252 mi) through Tuscany, Umbria and Lazio, where it is joined by the river Aniene, to the Tyrrhenian Sea, between Ostia and Fiumicino.[3] It drains a basin estimated at 17,375 square kilometres (6,709 sq mi). The river has achieved lasting fame as the main watercourse of the city of Rome, founded on its eastern banks.</code> | | <code>what kind of car does jay gatsby drive</code> | <code>Jay Gatsby At the Buchanan home, Jordan Baker, Nick, Jay, and the Buchanans decide to visit New York City. Tom borrows Gatsby's yellow Rolls Royce to drive up to the city. On the way to New York City, Tom makes a detour at a gas station in "the Valley of Ashes", a run-down part of Long Island. The owner, George Wilson, shares his concern that his wife, Myrtle, may be having an affair. This unnerves Tom, who has been having an affair with Myrtle, and he leaves in a hurry.</code> | | <code>who sings if i can dream about you</code> | <code>I Can Dream About You "I Can Dream About You" is a song performed by American singer Dan Hartman on the soundtrack album of the film Streets of Fire. Released in 1984 as a single from the soundtrack, and included on Hartman's album I Can Dream About You, it reached number 6 on the Billboard Hot 100.[1]</code> | * Loss: [<code>CSRLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#csrloss) with these parameters: ```json { "beta": 0.1, "gamma": 1.0, "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')" } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `learning_rate`: 4e-05 - `num_train_epochs`: 1 - `bf16`: True - `load_best_model_at_end`: True - `batch_sampler`: no_duplicates #### All Hyperparameters <details><summary>Click to expand</summary> - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 64 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 4e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 1 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.0 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `restore_callback_states_from_checkpoint`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: True - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: False - `dataloader_num_workers`: 0 - `dataloader_prefetch_factor`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `ddp_find_unused_parameters`: None - `ddp_bucket_cap_mb`: None - `ddp_broadcast_buffers`: False - `dataloader_pin_memory`: True - `dataloader_persistent_workers`: False - `skip_memory_metrics`: True - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: None - `hub_always_push`: False - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: False - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `optim_target_modules`: None - `batch_eval_metrics`: False - `eval_on_start`: False - `use_liger_kernel`: False - `eval_use_gather_object`: False - `average_tokens_across_devices`: False - `prompts`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {} </details> ### Training Logs | Epoch | Step | Training Loss | Validation Loss | NanoMSMARCO_128_dot_ndcg@10 | NanoNFCorpus_128_dot_ndcg@10 | NanoNQ_128_dot_ndcg@10 | NanoBEIR_mean_128_dot_ndcg@10 | NanoMSMARCO_256_dot_ndcg@10 | NanoNFCorpus_256_dot_ndcg@10 | NanoNQ_256_dot_ndcg@10 | NanoBEIR_mean_256_dot_ndcg@10 | NanoClimateFEVER_dot_ndcg@10 | NanoDBPedia_dot_ndcg@10 | NanoFEVER_dot_ndcg@10 | NanoFiQA2018_dot_ndcg@10 | NanoHotpotQA_dot_ndcg@10 | NanoMSMARCO_dot_ndcg@10 | NanoNFCorpus_dot_ndcg@10 | NanoNQ_dot_ndcg@10 | NanoQuoraRetrieval_dot_ndcg@10 | NanoSCIDOCS_dot_ndcg@10 | NanoArguAna_dot_ndcg@10 | NanoSciFact_dot_ndcg@10 | NanoTouche2020_dot_ndcg@10 | NanoBEIR_mean_dot_ndcg@10 | |:----------:|:--------:|:-------------:|:---------------:|:---------------------------:|:----------------------------:|:----------------------:|:-----------------------------:|:---------------------------:|:----------------------------:|:----------------------:|:-----------------------------:|:----------------------------:|:-----------------------:|:---------------------:|:------------------------:|:------------------------:|:-----------------------:|:------------------------:|:------------------:|:------------------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:--------------------------:|:-------------------------:| | 0.0646 | 100 | 0.3565 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1293 | 200 | 0.3568 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.1939 | 300 | 0.3545 | 0.3458 | 0.6322 | 0.2796 | 0.5893 | 0.5004 | 0.6232 | 0.3253 | 0.6548 | 0.5345 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.2586 | 400 | 0.3393 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3232 | 500 | 0.3484 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.3878 | 600 | 0.3567 | 0.3452 | 0.6245 | 0.3038 | 0.5719 | 0.5000 | 0.6385 | 0.3375 | 0.6496 | 0.5419 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.4525 | 700 | 0.3471 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5171 | 800 | 0.3582 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.5818 | 900 | 0.3758 | 0.3417 | 0.5849 | 0.3074 | 0.5866 | 0.4929 | 0.6147 | 0.3310 | 0.6729 | 0.5395 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.6464 | 1000 | 0.3515 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.7111 | 1100 | 0.3287 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | **0.7757** | **1200** | **0.3486** | **0.3314** | **0.5937** | **0.2998** | **0.6317** | **0.5084** | **0.6309** | **0.3303** | **0.6773** | **0.5462** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | **-** | | 0.8403 | 1300 | 0.3527 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9050 | 1400 | 0.3161 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | 0.9696 | 1500 | 0.3279 | 0.3244 | 0.6075 | 0.3038 | 0.6338 | 0.5150 | 0.6405 | 0.3181 | 0.6642 | 0.5410 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | | -1 | -1 | - | - | - | - | - | - | - | - | - | - | 0.3324 | 0.6002 | 0.8816 | 0.4881 | 0.8107 | 0.6329 | 0.3285 | 0.6773 | 0.9510 | 0.4023 | 0.6551 | 0.7194 | 0.4768 | 0.6120 | * The bold row denotes the saved checkpoint. ### Environmental Impact Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon). - **Energy Consumed**: 0.136 kWh - **Carbon Emitted**: 0.053 kg of CO2 - **Hours Used**: 0.41 hours ### Training Hardware - **On Cloud**: No - **GPU Model**: 1 x NVIDIA GeForce RTX 3090 - **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K - **RAM Size**: 31.78 GB ### Framework Versions - Python: 3.11.6 - Sentence Transformers: 4.2.0.dev0 - Transformers: 4.52.4 - PyTorch: 2.6.0+cu124 - Accelerate: 1.5.1 - Datasets: 2.21.0 - Tokenizers: 0.21.1 ## Citation ### BibTeX #### Sentence Transformers ```bibtex @inproceedings{reimers-2019-sentence-bert, title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", author = "Reimers, Nils and Gurevych, Iryna", booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", month = "11", year = "2019", publisher = "Association for Computational Linguistics", url = "https://arxiv.org/abs/1908.10084", } ``` #### CSRLoss ```bibtex @misc{wen2025matryoshkarevisitingsparsecoding, title={Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation}, author={Tiansheng Wen and Yifei Wang and Zequn Zeng and Zhong Peng and Yudi Su and Xinyang Liu and Bo Chen and Hongwei Liu and Stefanie Jegelka and Chenyu You}, year={2025}, eprint={2503.01776}, archivePrefix={arXiv}, primaryClass={cs.LG}, url={https://arxiv.org/abs/2503.01776}, } ``` #### SparseMultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```

mpasila/Llama-Poro-2-8B-Instruct-Q5_K_S-GGUF

mpasila

2025-06-19T14:23:03Z

0

transformers

[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "text-generation", "fi", "en", "dataset:LumiOpen/poro2-instruction-collection", "dataset:nvidia/HelpSteer3", "base_model:LumiOpen/Llama-Poro-2-8B-Instruct", "base_model:quantized:LumiOpen/Llama-Poro-2-8B-Instruct", "license:llama3.3", "endpoints_compatible", "region:us", "conversational" ]

text-generation

2025-06-19T14:22:39Z

--- datasets: - LumiOpen/poro2-instruction-collection - nvidia/HelpSteer3 language: - fi - en license: llama3.3 library_name: transformers pipeline_tag: text-generation tags: - llama-cpp - gguf-my-repo base_model: LumiOpen/Llama-Poro-2-8B-Instruct --- # mpasila/Llama-Poro-2-8B-Instruct-Q5_K_S-GGUF This model was converted to GGUF format from [`LumiOpen/Llama-Poro-2-8B-Instruct`](https://huggingface.co/LumiOpen/Llama-Poro-2-8B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/LumiOpen/Llama-Poro-2-8B-Instruct) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo mpasila/Llama-Poro-2-8B-Instruct-Q5_K_S-GGUF --hf-file llama-poro-2-8b-instruct-q5_k_s.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo mpasila/Llama-Poro-2-8B-Instruct-Q5_K_S-GGUF --hf-file llama-poro-2-8b-instruct-q5_k_s.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo mpasila/Llama-Poro-2-8B-Instruct-Q5_K_S-GGUF --hf-file llama-poro-2-8b-instruct-q5_k_s.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo mpasila/Llama-Poro-2-8B-Instruct-Q5_K_S-GGUF --hf-file llama-poro-2-8b-instruct-q5_k_s.gguf -c 2048 ```

MikeGreen2710/ner_cons_area_final

MikeGreen2710

2025-06-19T14:22:07Z

0

transformers

[ "transformers", "safetensors", "roberta", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2025-06-19T14:21:34Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Rif010/sealion-burmese-fine-tuned-merged-v1-Q4_K_M-GGUF

Rif010

2025-06-19T14:19:35Z

0

transformers

[ "transformers", "gguf", "llama-cpp", "gguf-my-repo", "base_model:Rif010/sealion-burmese-fine-tuned-merged-v1", "base_model:quantized:Rif010/sealion-burmese-fine-tuned-merged-v1", "endpoints_compatible", "region:us" ]

null

2025-06-19T14:19:11Z

--- library_name: transformers tags: - llama-cpp - gguf-my-repo base_model: Rif010/sealion-burmese-fine-tuned-merged-v1 --- # Rif010/sealion-burmese-fine-tuned-merged-v1-Q4_K_M-GGUF This model was converted to GGUF format from [`Rif010/sealion-burmese-fine-tuned-merged-v1`](https://huggingface.co/Rif010/sealion-burmese-fine-tuned-merged-v1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Rif010/sealion-burmese-fine-tuned-merged-v1) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Rif010/sealion-burmese-fine-tuned-merged-v1-Q4_K_M-GGUF --hf-file sealion-burmese-fine-tuned-merged-v1-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Rif010/sealion-burmese-fine-tuned-merged-v1-Q4_K_M-GGUF --hf-file sealion-burmese-fine-tuned-merged-v1-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Rif010/sealion-burmese-fine-tuned-merged-v1-Q4_K_M-GGUF --hf-file sealion-burmese-fine-tuned-merged-v1-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Rif010/sealion-burmese-fine-tuned-merged-v1-Q4_K_M-GGUF --hf-file sealion-burmese-fine-tuned-merged-v1-q4_k_m.gguf -c 2048 ```

ik-ram28/MedMistral-CPT-SFT-7B

ik-ram28

2025-06-19T14:19:33Z

19

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "medical", "conversational", "fr", "en", "base_model:ik-ram28/MedMistral-CPT-7B", "base_model:finetune:ik-ram28/MedMistral-CPT-7B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-02-18T14:42:40Z

--- library_name: transformers tags: - medical license: apache-2.0 language: - fr - en base_model: - ik-ram28/MedMistral-CPT-7B - mistralai/Mistral-7B-v0.1 --- ## Model Description MedMistral-CPT-SFT-7B is a French medical language model based on Mistral-7B-v0.1, adapted for medical domain applications through a combined approach of Continual Pre-Training (CPT) followed by Supervised Fine-Tuning (SFT). ## Model Details - **Model Type**: Causal Language Model - **Base Model**: Mistral-7B-v0.1 - **Language**: French - **Domain**: Medical/Healthcare - **License**: Apache 2.0 - **Paper**: [Adaptation des connaissances médicales pour les grands modèles de langue : Stratégies et analyse comparative](https://github.com/ikram28/medllm-strategies) ## Training Details ### Continual Pre-Training (CPT) - **Dataset**: NACHOS corpus (opeN crAwled frenCh Healthcare cOrpuS) - **Size**: 7.4 GB of French medical texts - **Word Count**: Over 1 billion words (1,088,867,950 words) - **Sources**: 24 French medical websites - **Training Duration**: 2.8 epochs - **Hardware**: 32 NVIDIA H100 80GB GPUs - **Training Time**: 12 hours - **Optimizer**: AdamW - **Learning Rate**: 2e-5 - **Weight Decay**: 0.01 - **Batch Size**: 16 with gradient accumulation of 2 ### Supervised Fine-Tuning (SFT) - **Dataset**: 30K French medical question-answer pairs - 10K native French medical questions - 10K translated medical questions from English resources - 10K generated questions from French medical texts - **Method**: DoRA (Weight-Decomposed Low-Rank Adaptation) - **Training Duration**: 10 epochs - **Hardware**: 1 NVIDIA A100 80GB GPU - **Training Time**: 75 hours - **Rank**: 16 - **Alpha**: 16 - **Learning Rate**: 2e-5 - **Batch Size**: 4 ## Computational Impact - **Total Training Time**: 87 hours (12h CPT + 75h SFT) - **Carbon Emissions**: 11.78 kgCO2e (9.86 + 1.92) ## Ethical Considerations - **Medical Accuracy**: This model is for research and educational purposes only. All outputs should be verified by qualified medical professionals - **Bias**: Training data may contain biases present in medical literature and online medical resources ## Citation If you use this model, please cite: ```bibtex ``` ## Contact For questions about this model, please contact: [email protected]

johngreendr1/ce7a970e-7299-4e54-bf83-14b49ed32fd7

johngreendr1

2025-06-19T14:17:06Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:NousResearch/Nous-Capybara-7B-V1.9", "base_model:adapter:NousResearch/Nous-Capybara-7B-V1.9", "region:us" ]

null

2025-06-19T14:17:00Z

--- base_model: NousResearch/Nous-Capybara-7B-V1.9 library_name: peft --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.1

cosmo3769/nanoVLM-test

cosmo3769

2025-06-19T14:14:26Z

0

nanovlm

[ "nanovlm", "safetensors", "vision-language", "multimodal", "research", "image-text-to-text", "license:mit", "region:us" ]

image-text-to-text

2025-06-19T14:13:36Z

--- # For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 # Doc / guide: https://huggingface.co/docs/hub/model-cards library_name: nanovlm license: mit pipeline_tag: image-text-to-text tags: - vision-language - multimodal - research --- **nanoVLM** is a minimal and lightweight Vision-Language Model (VLM) designed for efficient training and experimentation. Built using pure PyTorch, the entire model architecture and training logic fits within ~750 lines of code. It combines a ViT-based image encoder (SigLIP-B/16-224-85M) with a lightweight causal language model (SmolLM2-135M), resulting in a compact 222M parameter model. For more information, check out the base model on https://huggingface.co/lusxvr/nanoVLM-222M. **Usage:** Clone the nanoVLM repository: https://github.com/huggingface/nanoVLM. Follow the install instructions and run the following code: ```python from models.vision_language_model import VisionLanguageModel model = VisionLanguageModel.from_pretrained("cosmo3769/nanoVLM-test") ```

TruongSinhAI/Qwen2.5-1.5B-Instruct_200steps

TruongSinhAI

2025-06-19T14:12:16Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "qwen2", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-06-19T14:12:00Z

--- base_model: unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen2 - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** TruongSinhAI - **License:** apache-2.0 - **Finetuned from model :** unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

Felix92/doctr-dummy-torch-viptr-tiny

Felix92

2025-06-19T14:11:01Z

0

null

[ "pytorch", "region:us" ]

null

2025-06-19T14:10:56Z

language: en <p align="center"> <img src="https://doctr-static.mindee.com/models?id=v0.3.1/Logo_doctr.gif&src=0" width="60%"> </p> **Optical Character Recognition made seamless & accessible to anyone, powered by TensorFlow 2 & PyTorch** ## Task: recognition https://github.com/mindee/doctr ### Example usage: ```python >>> from doctr.io import DocumentFile >>> from doctr.models import ocr_predictor, from_hub >>> img = DocumentFile.from_images(['<image_path>']) >>> # Load your model from the hub >>> model = from_hub('mindee/my-model') >>> # Pass it to the predictor >>> # If your model is a recognition model: >>> predictor = ocr_predictor(det_arch='db_mobilenet_v3_large', >>> reco_arch=model, >>> pretrained=True) >>> # If your model is a detection model: >>> predictor = ocr_predictor(det_arch=model, >>> reco_arch='crnn_mobilenet_v3_small', >>> pretrained=True) >>> # Get your predictions >>> res = predictor(img) ```

Alphatao/Affine-2501551

Alphatao

2025-06-19T14:09:26Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "conversational", "arxiv:2309.00071", "arxiv:2505.09388", "base_model:Qwen/Qwen3-8B-Base", "base_model:finetune:Qwen/Qwen3-8B-Base", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-19T14:03:22Z

--- library_name: transformers license: apache-2.0 license_link: https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE pipeline_tag: text-generation base_model: - Qwen/Qwen3-8B-Base --- # Qwen3-8B <a href="https://chat.qwen.ai/" target="_blank" style="margin: 2px;"> <img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/> </a> ## Qwen3 Highlights Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features: - **Uniquely support of seamless switching between thinking mode** (for complex logical reasoning, math, and coding) and **non-thinking mode** (for efficient, general-purpose dialogue) **within single model**, ensuring optimal performance across various scenarios. - **Significantly enhancement in its reasoning capabilities**, surpassing previous QwQ (in thinking mode) and Qwen2.5 instruct models (in non-thinking mode) on mathematics, code generation, and commonsense logical reasoning. - **Superior human preference alignment**, excelling in creative writing, role-playing, multi-turn dialogues, and instruction following, to deliver a more natural, engaging, and immersive conversational experience. - **Expertise in agent capabilities**, enabling precise integration with external tools in both thinking and unthinking modes and achieving leading performance among open-source models in complex agent-based tasks. - **Support of 100+ languages and dialects** with strong capabilities for **multilingual instruction following** and **translation**. ## Model Overview **Qwen3-8B** has the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Number of Parameters: 8.2B - Number of Paramaters (Non-Embedding): 6.95B - Number of Layers: 36 - Number of Attention Heads (GQA): 32 for Q and 8 for KV - Context Length: 32,768 natively and [131,072 tokens with YaRN](#processing-long-texts). For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3/), [GitHub](https://github.com/QwenLM/Qwen3), and [Documentation](https://qwen.readthedocs.io/en/latest/). ## Quickstart The code of Qwen3 has been in the latest Hugging Face `transformers` and we advise you to use the latest version of `transformers`. With `transformers<4.51.0`, you will encounter the following error: ``` KeyError: 'qwen3' ``` The following contains a code snippet illustrating how to use the model generate content based on given inputs. ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Qwen/Qwen3-8B" # load the tokenizer and the model tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map="auto" ) # prepare the model input prompt = "Give me a short introduction to large language model." messages = [ {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True # Switches between thinking and non-thinking modes. Default is True. ) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # conduct text completion generated_ids = model.generate( **model_inputs, max_new_tokens=32768 ) output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() # parsing thinking content try: # rindex finding 151668 (</think>) index = len(output_ids) - output_ids[::-1].index(151668) except ValueError: index = 0 thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n") content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n") print("thinking content:", thinking_content) print("content:", content) ``` For deployment, you can use `sglang>=0.4.6.post1` or `vllm>=0.8.5` or to create an OpenAI-compatible API endpoint: - SGLang: ```shell python -m sglang.launch_server --model-path Qwen/Qwen3-8B --reasoning-parser qwen3 ``` - vLLM: ```shell vllm serve Qwen/Qwen3-8B --enable-reasoning --reasoning-parser deepseek_r1 ``` For local use, applications such as Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers have also supported Qwen3. ## Switching Between Thinking and Non-Thinking Mode > [!TIP] > The `enable_thinking` switch is also available in APIs created by SGLang and vLLM. > Please refer to our documentation for [SGLang](https://qwen.readthedocs.io/en/latest/deployment/sglang.html#thinking-non-thinking-modes) and [vLLM](https://qwen.readthedocs.io/en/latest/deployment/vllm.html#thinking-non-thinking-modes) users. ### `enable_thinking=True` By default, Qwen3 has thinking capabilities enabled, similar to QwQ-32B. This means the model will use its reasoning abilities to enhance the quality of generated responses. For example, when explicitly setting `enable_thinking=True` or leaving it as the default value in `tokenizer.apply_chat_template`, the model will engage its thinking mode. ```python text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=True # True is the default value for enable_thinking ) ``` In this mode, the model will generate think content wrapped in a `<think>...</think>` block, followed by the final response. > [!NOTE] > For thinking mode, use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0` (the default setting in `generation_config.json`). **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions. For more detailed guidance, please refer to the [Best Practices](#best-practices) section. ### `enable_thinking=False` We provide a hard switch to strictly disable the model's thinking behavior, aligning its functionality with the previous Qwen2.5-Instruct models. This mode is particularly useful in scenarios where disabling thinking is essential for enhancing efficiency. ```python text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, enable_thinking=False # Setting enable_thinking=False disables thinking mode ) ``` In this mode, the model will not generate any think content and will not include a `<think>...</think>` block. > [!NOTE] > For non-thinking mode, we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. For more detailed guidance, please refer to the [Best Practices](#best-practices) section. ### Advanced Usage: Switching Between Thinking and Non-Thinking Modes via User Input We provide a soft switch mechanism that allows users to dynamically control the model's behavior when `enable_thinking=True`. Specifically, you can add `/think` and `/no_think` to user prompts or system messages to switch the model's thinking mode from turn to turn. The model will follow the most recent instruction in multi-turn conversations. Here is an example of a multi-turn conversation: ```python from transformers import AutoModelForCausalLM, AutoTokenizer class QwenChatbot: def __init__(self, model_name="Qwen/Qwen3-8B"): self.tokenizer = AutoTokenizer.from_pretrained(model_name) self.model = AutoModelForCausalLM.from_pretrained(model_name) self.history = [] def generate_response(self, user_input): messages = self.history + [{"role": "user", "content": user_input}] text = self.tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) inputs = self.tokenizer(text, return_tensors="pt") response_ids = self.model.generate(**inputs, max_new_tokens=32768)[0][len(inputs.input_ids[0]):].tolist() response = self.tokenizer.decode(response_ids, skip_special_tokens=True) # Update history self.history.append({"role": "user", "content": user_input}) self.history.append({"role": "assistant", "content": response}) return response # Example Usage if __name__ == "__main__": chatbot = QwenChatbot() # First input (without /think or /no_think tags, thinking mode is enabled by default) user_input_1 = "How many r's in strawberries?" print(f"User: {user_input_1}") response_1 = chatbot.generate_response(user_input_1) print(f"Bot: {response_1}") print("----------------------") # Second input with /no_think user_input_2 = "Then, how many r's in blueberries? /no_think" print(f"User: {user_input_2}") response_2 = chatbot.generate_response(user_input_2) print(f"Bot: {response_2}") print("----------------------") # Third input with /think user_input_3 = "Really? /think" print(f"User: {user_input_3}") response_3 = chatbot.generate_response(user_input_3) print(f"Bot: {response_3}") ``` > [!NOTE] > For API compatibility, when `enable_thinking=True`, regardless of whether the user uses `/think` or `/no_think`, the model will always output a block wrapped in `<think>...</think>`. However, the content inside this block may be empty if thinking is disabled. > When `enable_thinking=False`, the soft switches are not valid. Regardless of any `/think` or `/no_think` tags input by the user, the model will not generate think content and will not include a `<think>...</think>` block. ## Agentic Use Qwen3 excels in tool calling capabilities. We recommend using [Qwen-Agent](https://github.com/QwenLM/Qwen-Agent) to make the best use of agentic ability of Qwen3. Qwen-Agent encapsulates tool-calling templates and tool-calling parsers internally, greatly reducing coding complexity. To define the available tools, you can use the MCP configuration file, use the integrated tool of Qwen-Agent, or integrate other tools by yourself. ```python from qwen_agent.agents import Assistant # Define LLM llm_cfg = { 'model': 'Qwen3-8B', # Use the endpoint provided by Alibaba Model Studio: # 'model_type': 'qwen_dashscope', # 'api_key': os.getenv('DASHSCOPE_API_KEY'), # Use a custom endpoint compatible with OpenAI API: 'model_server': 'http://localhost:8000/v1', # api_base 'api_key': 'EMPTY', # Other parameters: # 'generate_cfg': { # # Add: When the response content is `<think>this is the thought</think>this is the answer; # # Do not add: When the response has been separated by reasoning_content and content. # 'thought_in_content': True, # }, } # Define Tools tools = [ {'mcpServers': { # You can specify the MCP configuration file 'time': { 'command': 'uvx', 'args': ['mcp-server-time', '--local-timezone=Asia/Shanghai'] }, "fetch": { "command": "uvx", "args": ["mcp-server-fetch"] } } }, 'code_interpreter', # Built-in tools ] # Define Agent bot = Assistant(llm=llm_cfg, function_list=tools) # Streaming generation messages = [{'role': 'user', 'content': 'https://qwenlm.github.io/blog/ Introduce the latest developments of Qwen'}] for responses in bot.run(messages=messages): pass print(responses) ``` ## Processing Long Texts Qwen3 natively supports context lengths of up to 32,768 tokens. For conversations where the total length (including both input and output) significantly exceeds this limit, we recommend using RoPE scaling techniques to handle long texts effectively. We have validated the model's performance on context lengths of up to 131,072 tokens using the [YaRN](https://arxiv.org/abs/2309.00071) method. YaRN is currently supported by several inference frameworks, e.g., `transformers` and `llama.cpp` for local use, `vllm` and `sglang` for deployment. In general, there are two approaches to enabling YaRN for supported frameworks: - Modifying the model files: In the `config.json` file, add the `rope_scaling` fields: ```json { ..., "rope_scaling": { "rope_type": "yarn", "factor": 4.0, "original_max_position_embeddings": 32768 } } ``` For `llama.cpp`, you need to regenerate the GGUF file after the modification. - Passing command line arguments: For `vllm`, you can use ```shell vllm serve ... --rope-scaling '{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}' --max-model-len 131072 ``` For `sglang`, you can use ```shell python -m sglang.launch_server ... --json-model-override-args '{"rope_scaling":{"rope_type":"yarn","factor":4.0,"original_max_position_embeddings":32768}}' ``` For `llama-server` from `llama.cpp`, you can use ```shell llama-server ... --rope-scaling yarn --rope-scale 4 --yarn-orig-ctx 32768 ``` > [!IMPORTANT] > If you encounter the following warning > ``` > Unrecognized keys in `rope_scaling` for 'rope_type'='yarn': {'original_max_position_embeddings'} > ``` > please upgrade `transformers>=4.51.0`. > [!NOTE] > All the notable open-source frameworks implement static YaRN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts.** > We advise adding the `rope_scaling` configuration only when processing long contexts is required. > It is also recommended to modify the `factor` as needed. For example, if the typical context length for your application is 65,536 tokens, it would be better to set `factor` as 2.0. > [!NOTE] > The default `max_position_embeddings` in `config.json` is set to 40,960. This allocation includes reserving 32,768 tokens for outputs and 8,192 tokens for typical prompts, which is sufficient for most scenarios involving short text processing. If the average context length does not exceed 32,768 tokens, we do not recommend enabling YaRN in this scenario, as it may potentially degrade model performance. > [!TIP] > The endpoint provided by Alibaba Model Studio supports dynamic YaRN by default and no extra configuration is needed. ## Best Practices To achieve optimal performance, we recommend the following settings: 1. **Sampling Parameters**: - For thinking mode (`enable_thinking=True`), use `Temperature=0.6`, `TopP=0.95`, `TopK=20`, and `MinP=0`. **DO NOT use greedy decoding**, as it can lead to performance degradation and endless repetitions. - For non-thinking mode (`enable_thinking=False`), we suggest using `Temperature=0.7`, `TopP=0.8`, `TopK=20`, and `MinP=0`. - For supported frameworks, you can adjust the `presence_penalty` parameter between 0 and 2 to reduce endless repetitions. However, using a higher value may occasionally result in language mixing and a slight decrease in model performance. 2. **Adequate Output Length**: We recommend using an output length of 32,768 tokens for most queries. For benchmarking on highly complex problems, such as those found in math and programming competitions, we suggest setting the max output length to 38,912 tokens. This provides the model with sufficient space to generate detailed and comprehensive responses, thereby enhancing its overall performance. 3. **Standardize Output Format**: We recommend using prompts to standardize model outputs when benchmarking. - **Math Problems**: Include "Please reason step by step, and put your final answer within \boxed{}." in the prompt. - **Multiple-Choice Questions**: Add the following JSON structure to the prompt to standardize responses: "Please show your choice in the `answer` field with only the choice letter, e.g., `"answer": "C"`." 4. **No Thinking Content in History**: In multi-turn conversations, the historical model output should only include the final output part and does not need to include the thinking content. It is implemented in the provided chat template in Jinja2. However, for frameworks that do not directly use the Jinja2 chat template, it is up to the developers to ensure that the best practice is followed. ### Citation If you find our work helpful, feel free to give us a cite. ``` @misc{qwen3technicalreport, title={Qwen3 Technical Report}, author={Qwen Team}, year={2025}, eprint={2505.09388}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2505.09388}, } ```

freakyfractal/buser2

freakyfractal

2025-06-19T14:04:49Z

0

diffusers

[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:apache-2.0", "region:us" ]

text-to-image

2025-06-19T14:04:00Z

--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '-' output: url: images/Coinye_2021.jpg base_model: black-forest-labs/FLUX.1-dev instance_prompt: null license: apache-2.0 --- # buser2 <Gallery /> ## Download model Weights for this model are available in Safetensors format. [Download](/freakyfractal/buser2/tree/main) them in the Files & versions tab.

5eunsoo/my-bert-fine-tuned

5eunsoo

2025-06-19T13:57:03Z

0

transformers

[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2025-06-19T13:56:38Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

onnx-community/whisper-tiny

onnx-community

2025-06-19T13:56:43Z

3,264

0

transformers.js

[ "transformers.js", "onnx", "whisper", "automatic-speech-recognition", "base_model:openai/whisper-tiny", "base_model:quantized:openai/whisper-tiny", "region:us" ]

automatic-speech-recognition

2024-05-24T16:52:04Z

--- base_model: openai/whisper-tiny library_name: transformers.js --- https://huggingface.co/openai/whisper-tiny with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

Sawu-Low3/final-t5-base-lora-stage1

Sawu-Low3

2025-06-19T13:55:10Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2025-06-19T13:55:08Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

convaiinnovations/ECG-Instruct-Llama-3.2-11B-Vision

convaiinnovations

2025-06-19T13:54:52Z

0

unsloth

[ "unsloth", "safetensors", "mllama", "llama-3.2", "vision-language-model", "ecg", "cardiology", "lora", "medical-imaging", "text-generation", "convaiinnovations", "conversational", "en", "dataset:ECGInstruct", "arxiv:2501.18670", "base_model:unsloth/Llama-3.2-11B-Vision-Instruct", "base_model:adapter:unsloth/Llama-3.2-11B-Vision-Instruct", "license:llama3", "region:us" ]

text-generation

2025-06-19T12:09:06Z

--- license: llama3 language: en library_name: unsloth tags: - unsloth - llama-3.2 - vision-language-model - ecg - cardiology - lora - medical-imaging - text-generation - convaiinnovations base_model: unsloth/Llama-3.2-11B-Vision-Instruct datasets: - ECGInstruct --- # High-Accuracy ECG Image Interpretation with LLaMA 3.2 This repository contains the official fine-tuned model from the paper: **"High-Accuracy ECG Image Interpretation using Parameter-Efficient LoRA Fine-Tuning with Multimodal LLaMA 3.2"**. **Paper:** [arXiv:2501.18670](https://arxiv.org/abs/2501.18670) This model was developed by **Nandakishor M** and **Anjali M** at **Convai Innovations**. It is designed to provide high-accuracy, comprehensive interpretation of electrocardiogram (ECG) images. ## Model Details * **Base Model:** `unsloth/Llama-3.2-11B-Vision-Instruct` * **Fine-tuning Strategy:** Parameter-Efficient LoRA * **Dataset:** `ECGInstruct`, a large-scale dataset with 1 million instruction-following samples derived from public sources like MIMIC-IV ECG and PTB-XL. * **Primary Use:** Automated analysis and report generation from ECG images to assist cardiologists and medical professionals in diagnosing a wide range of cardiac conditions. ## How to Use This model was trained using [Unsloth](https://github.com/unslothai/unsloth) to achieve high performance and memory efficiency. The following code provides a complete example of how to load the model in 4-bit precision and run inference. You can run the code using Free Google Colab at : [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1bL9z0NU8kuUyYescSJTIpP9NEkF2Dk6o?usp=sharing) ```python import torch from unsloth import FastVisionModel from transformers import AutoProcessor, TextStreamer from PIL import Image from IPython.display import display # Make sure you have an ECG image file, e.g., 'my_ecg.jpg' image_path = "my_ecg.jpg" # Load the 4-bit quantized model and processor model, processor = FastVisionModel.from_pretrained( model_name="convaiinnovations/ECG-Instruct-Llama-3.2-11B-Vision", max_seq_length=4096, dtype=None, load_in_4bit=True, device_map="cuda" ) # Enable fast inference mode FastVisionModel.for_inference(model) # Load the image image = Image.open(image_path).convert("RGB") # Define the instruction query = "You are an expert cardiologist. Write an in-depth diagnosis report from this ECG data, including the final diagnosis." # Prepare the prompt messages = [ {"role": "user", "content": [ {"type": "image"}, {"type": "text", "text": query} ]} ] input_text = processor.apply_chat_template(messages, add_generation_prompt=True) # Process inputs inputs = processor( text=input_text, images=image, return_tensors="pt", ).to("cuda") # Set up streamer for token-by-token output text_streamer = TextStreamer(processor.tokenizer, skip_prompt=True) # Generate the report _ = model.generate(**inputs, streamer=text_streamer, max_new_tokens=512, use_cache=True, temperature=0.2, min_p=0.1) # To see the input image in a notebook: # display(image.resize((600, 400))) ``` ## Training and Fine-tuning The model was fine-tuned on the `ECGInstruct` dataset using a parameter-efficient LoRA strategy, which significantly improves performance on ECG interpretation tasks while preserving the base model's extensive knowledge. ### Key Hyperparameters: - **LoRA Rank (`r`):** 64 - **LoRA Alpha (`alpha`):** 128 - **LoRA Dropout:** 0.05 - **Learning Rate:** 2e-4 with a cosine scheduler - **Epochs:** 3 - **Hardware:** 4x NVIDIA A100 80GB GPUs - **Framework:** Unsloth with DeepSpeed ZeRO-2 *Note: As described in the paper, the `lm_head` and `embed_tokens` layers were excluded from LoRA adaptation to maintain generation stability.* ## Evaluation The fine-tuned model demonstrates state-of-the-art performance, significantly outperforming the baseline LLaMA 3.2 model across all metrics. | Task | Metric | Baseline | **Ours (Fine-tuned)** | |---------------|-------------|----------|-----------------------| | Abnorm. Det. | AUC | 0.51 | **0.98** | | | Macro F1 | 0.33 | **0.74** | | | Hamming Loss| 0.49 | **0.11** | | Report Gen. | Report Score| 47.8 | **85.4** | *Report Score was evaluated using GPT-4o against expert-annotated ground truth reports.* ## Citation If you use this model in your research, please cite our paper: ```bibtex @misc{nandakishor2025highaccuracy, title={High-Accuracy ECG Image Interpretation using Parameter-Efficient LoRA Fine-Tuning with Multimodal LLaMA 3.2}, author={Nandakishor M and Anjali M}, year={2025}, eprint={2501.18670}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```

MikeGreen2710/ner_land_area_final

MikeGreen2710

2025-06-19T13:54:24Z

0

transformers

[ "transformers", "safetensors", "roberta", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2025-06-19T13:53:48Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

khs2617/gemma-3-1b-it-lora-strategy_try_3

khs2617

2025-06-19T13:52:50Z

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:google/gemma-3-1b-it", "base_model:adapter:google/gemma-3-1b-it", "region:us" ]

null

2025-06-19T13:52:30Z

--- base_model: google/gemma-3-1b-it library_name: peft --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2

onnx-community/whisper-base

onnx-community

2025-06-19T13:49:48Z

13,395

20

transformers.js

[ "transformers.js", "onnx", "whisper", "automatic-speech-recognition", "base_model:openai/whisper-base", "base_model:quantized:openai/whisper-base", "region:us" ]

automatic-speech-recognition

2024-05-24T17:00:47Z

--- base_model: openai/whisper-base library_name: transformers.js --- https://huggingface.co/openai/whisper-base with ONNX weights to be compatible with Transformers.js. Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).

ahamedddd/showBranchesNLPDistilBertBaseUncasedv1

ahamedddd

2025-06-19T13:49:22Z

0

transformers

[ "transformers", "safetensors", "distilbert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2025-06-19T13:18:12Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

3sara/version1_3-3epochs-from_base

3sara

2025-06-19T13:48:12Z

0

peft

[ "peft", "safetensors", "colpali-finetuned", "generated_from_trainer", "base_model:vidore/colpaligemma-3b-pt-448-base", "base_model:adapter:vidore/colpaligemma-3b-pt-448-base", "license:gemma", "region:us" ]

null

2025-06-19T13:48:01Z

--- library_name: peft license: gemma base_model: vidore/colpaligemma-3b-pt-448-base tags: - colpali-finetuned - generated_from_trainer model-index: - name: version1_3-3epochs-from_base results: [] ---  # version1_3-3epochs-from_base This model is a fine-tuned version of [vidore/colpaligemma-3b-pt-448-base](https://huggingface.co/vidore/colpaligemma-3b-pt-448-base) on the 3sara/validated_colpali_italian_documents_with_images dataset. It achieves the following results on the evaluation set: - Loss: 0.2780 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | No log | 0.0103 | 1 | 0.3507 | | 0.1301 | 1.0205 | 100 | 0.2925 | | 0.0948 | 2.0410 | 200 | 0.2780 | ### Framework versions - PEFT 0.15.2 - Transformers 4.51.3 - Pytorch 2.7.1+cu126 - Datasets 3.6.0 - Tokenizers 0.21.1

ik-ram28/MedMistralInstruct-CPT-7B

ik-ram28

2025-06-19T13:45:23Z

17

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "medical", "conversational", "fr", "en", "base_model:mistralai/Mistral-7B-Instruct-v0.1", "base_model:finetune:mistralai/Mistral-7B-Instruct-v0.1", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-02-08T23:18:07Z

--- library_name: transformers tags: - medical license: apache-2.0 language: - fr - en base_model: - mistralai/Mistral-7B-Instruct-v0.1 --- ### Model Description MedMistralInstruct-CPT-7B is adapted from Mistral-7B-Instruct-v0.1 through Continual Pre-Training, maintaining instruction-following capabilities while gaining medical domain knowledge. ### Model Details - **Model Type**: Causal Language Model - **Base Model**: Mistral-7B-Instruct-v0.1 - **Language**: French - **Domain**: Medical/Healthcare - **Parameters**: 7 billion - **License**: Apache 2.0 ### Training Details **Continual Pre-Training (CPT)** - **Dataset**: NACHOS corpus (7.4 GB French medical texts) - **Training Duration**: 2.8 epochs - **Hardware**: 32 NVIDIA A100 80GB GPUs - **Training Time**: ~40 hours ### Computational Requirements - **Carbon Emissions**: 32.89 kgCO2e - **Training Time**: 40 hours ### Ethical Considerations - **Medical Accuracy**: For research and educational purposes only - **Professional Oversight**: Requires verification by qualified medical professionals - **Bias Awareness**: May contain biases from training data - **Privacy**: Do not input private health information ### Citation ```bibtex ``` ### Contact For questions about these models, please contact: [email protected]

TECCOD/adilet-llama-8b-250619

TECCOD

2025-06-19T13:45:20Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-19T13:22:18Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

mradermacher/nemo-chatbot-v3-GGUF

mradermacher

2025-06-19T13:44:24Z

0

transformers

[ "transformers", "gguf", "en", "base_model:chaerheeon/nemo-chatbot-v3", "base_model:quantized:chaerheeon/nemo-chatbot-v3", "endpoints_compatible", "region:us" ]

null

2025-06-19T13:19:54Z

--- base_model: chaerheeon/nemo-chatbot-v3 language: - en library_name: transformers quantized_by: mradermacher --- ## About      static quants of https://huggingface.co/chaerheeon/nemo-chatbot-v3  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v3-GGUF/resolve/main/nemo-chatbot-v3.Q2_K.gguf) | Q2_K | 1.8 | | | [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v3-GGUF/resolve/main/nemo-chatbot-v3.Q3_K_S.gguf) | Q3_K_S | 2.0 | | | [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v3-GGUF/resolve/main/nemo-chatbot-v3.Q3_K_M.gguf) | Q3_K_M | 2.2 | lower quality | | [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v3-GGUF/resolve/main/nemo-chatbot-v3.Q3_K_L.gguf) | Q3_K_L | 2.3 | | | [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v3-GGUF/resolve/main/nemo-chatbot-v3.IQ4_XS.gguf) | IQ4_XS | 2.4 | | | [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v3-GGUF/resolve/main/nemo-chatbot-v3.Q4_K_S.gguf) | Q4_K_S | 2.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v3-GGUF/resolve/main/nemo-chatbot-v3.Q4_K_M.gguf) | Q4_K_M | 2.6 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v3-GGUF/resolve/main/nemo-chatbot-v3.Q5_K_S.gguf) | Q5_K_S | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v3-GGUF/resolve/main/nemo-chatbot-v3.Q5_K_M.gguf) | Q5_K_M | 2.9 | | | [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v3-GGUF/resolve/main/nemo-chatbot-v3.Q6_K.gguf) | Q6_K | 3.3 | very good quality | | [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v3-GGUF/resolve/main/nemo-chatbot-v3.Q8_0.gguf) | Q8_0 | 4.2 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/nemo-chatbot-v3-GGUF/resolve/main/nemo-chatbot-v3.f16.gguf) | f16 | 7.9 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

TachyHealth/Gazal-R1-32B-sft-merged-preview

TachyHealth

2025-06-19T13:43:00Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "dora", "peft", "adapter", "finetuned", "Qwen3-32B", "medical", "clinical", "healthcare", "conversational", "en", "dataset:TachyHealth/structured_medical", "base_model:Qwen/Qwen3-32B", "base_model:finetune:Qwen/Qwen3-32B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-05-21T12:48:39Z

--- language: en license: apache-2.0 tags: - dora - peft - adapter - finetuned - Qwen3-32B - medical - clinical - healthcare base_model: - Qwen/Qwen3-32B datasets: - TachyHealth/structured_medical pipeline_tag: text-generation library_name: transformers --- # Gazal-R1-32B-sft-merged-preview This is a DoRA adapter fine-tuned on top of [Qwen/Qwen3-32B](https://huggingface.co/Qwen/Qwen3-32B) for specialized medical reasoning tasks. ## Model description This adapter was trained using PEFT/LoRA to enhance the base model's ability to perform step-by-step clinical reasoning and medical problem-solving. ### Training data The model was fine-tuned on a synthetic, structured reasoning dataset, which contains medical questions with step-by-step reasoning and final answers. ### Training procedure The model was trained using: - LoRA with rank 256 - DoRA (Weight-Decomposed Low-Rank Adaptation) - rsLoRA (Rank-stabilized LoRA) - BF16 precision training ### Use cases and limitations This model is intended for medical education and clinical reasoning training. It should NOT be used for actual medical diagnosis or treatment decisions. Always consult qualified healthcare professionals for medical advice. ## Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel # Load base model and tokenizer base_model_id = "Qwen/Qwen3-32B" adapter_id = "TachyHealth/Gazal-R1-32B-sft-merged" # Load the tokenizer and base model tokenizer = AutoTokenizer.from_pretrained(base_model_id) model = AutoModelForCausalLM.from_pretrained( base_model_id, torch_dtype="auto", device_map="auto", ) # Load the LoRA adapter model = PeftModel.from_pretrained(model, adapter_id) # Prepare a prompt following the format during training query = """[MEDICAL QUESTION]""" messages = [ {"role": "system", "content": "When solving complex medical problems, follow this specific format..."}, {"role": "user", "content": query} ] input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tokenizer(input_text, return_tensors="pt").to(model.device) # Generate response outputs = model.generate( input_ids=inputs.input_ids, max_new_tokens=2048, temperature=0.6, do_sample=True, ) response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True) print(response) ``` ## Performance Results Gazal-R1 achieves exceptional performance across standard medical benchmarks: | Model | Size | MMLU Pro (Medical) | MedMCQA | MedQA | PubMedQA | |-------|------|-------------------|---------|-------|----------| | [**Gazal-R1 (Final)**](https://huggingface.co/TachyHealth/Gazal-R1-32B-GRPO-preview) | **32B** | **81.6** | **71.9** | **87.1** | **79.6** | | Gazal-R1 (SFT-only) | 32B | 79.3 | 72.3 | 86.9 | 77.6 | | Llama 3.1 405B Instruct | 405B | 70.2 | 75.8 | 81.9 | 74.6 | | Qwen 2.5 72B Instruct | 72B | 72.1 | 66.2 | 72.7 | 71.7 | | Med42-Llama3.1-70B | 70B | 66.1 | 72.4 | 80.4 | 77.6 | | Llama 3.1 70B Instruct | 70B | 74.5 | 72.5 | 78.4 | 78.5 | | QwQ 32B | 32B | 70.1 | 65.6 | 72.3 | 73.7 | | Qwen 3 32B | 32B | 78.4 | 71.6 | 84.4 | 76.7 |

neural-interactive-proofs/finetune_dpo_cv_test_lm_server_47_0_iter_0_provers_group_2025-06-19_14-40-50_Qwen_Qwen2.5-0.5B-I

neural-interactive-proofs

2025-06-19T13:41:40Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "dpo", "arxiv:2305.18290", "base_model:Qwen/Qwen2.5-0.5B-Instruct", "base_model:finetune:Qwen/Qwen2.5-0.5B-Instruct", "endpoints_compatible", "region:us" ]

null

2025-06-19T13:41:35Z

--- base_model: Qwen/Qwen2.5-0.5B-Instruct library_name: transformers model_name: finetune_dpo_cv_test_lm_server_47_0_iter_0_provers_group_2025-06-19_14-40-50_Qwen_Qwen2.5-0.5B-I tags: - generated_from_trainer - trl - dpo licence: license --- # Model Card for finetune_dpo_cv_test_lm_server_47_0_iter_0_provers_group_2025-06-19_14-40-50_Qwen_Qwen2.5-0.5B-I This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="neural-interactive-proofs/finetune_dpo_cv_test_lm_server_47_0_iter_0_provers_group_2025-06-19_14-40-50_Qwen_Qwen2.5-0.5B-I", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure [<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/lrhammond-team/pvg-self-hosted-finetune/runs/Qwen_Qwen2.5-0.5B-Instruct_dpo_2025-06-19_14-40-50_cv_test_lm_server_47_0_iter_0_provers_group) This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290). ### Framework versions - TRL: 0.15.2 - Transformers: 4.52.4 - Pytorch: 2.7.0 - Datasets: 2.21.0 - Tokenizers: 0.21.1 ## Citations Cite DPO as: ```bibtex @inproceedings{rafailov2023direct, title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}}, author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn}, year = 2023, booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023}, url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html}, editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine}, } ``` Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

Vortex5/tobenamed-24B-Q4_K_M-GGUF

Vortex5

2025-06-19T13:40:45Z

0

transformers

[ "transformers", "gguf", "mergekit", "merge", "llama-cpp", "gguf-my-repo", "base_model:Vortex5/tobenamed-24B", "base_model:quantized:Vortex5/tobenamed-24B", "endpoints_compatible", "region:us" ]

null

2025-06-19T13:39:40Z

--- base_model: Vortex5/tobenamed-24B library_name: transformers tags: - mergekit - merge - llama-cpp - gguf-my-repo --- # Vortex5/tobenamed-24B-Q4_K_M-GGUF This model was converted to GGUF format from [`Vortex5/tobenamed-24B`](https://huggingface.co/Vortex5/tobenamed-24B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/Vortex5/tobenamed-24B) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo Vortex5/tobenamed-24B-Q4_K_M-GGUF --hf-file tobenamed-24b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo Vortex5/tobenamed-24B-Q4_K_M-GGUF --hf-file tobenamed-24b-q4_k_m.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo Vortex5/tobenamed-24B-Q4_K_M-GGUF --hf-file tobenamed-24b-q4_k_m.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo Vortex5/tobenamed-24B-Q4_K_M-GGUF --hf-file tobenamed-24b-q4_k_m.gguf -c 2048 ```

indicinaaa/unsloth-Qwen3-4B-16bit

indicinaaa

2025-06-19T13:39:56Z

0

transformers

[ "transformers", "safetensors", "qwen3", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2025-06-19T13:39:47Z

--- base_model: unsloth/qwen3-4b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - qwen3 license: apache-2.0 language: - en --- # Uploaded finetuned model - **Developed by:** indicinaaa - **License:** apache-2.0 - **Finetuned from model :** unsloth/qwen3-4b-unsloth-bnb-4bit This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

FastFlowLM/Llama-3.2-1B-NPU

FastFlowLM

2025-06-19T13:39:51Z

0

null

[ "base_model:meta-llama/Llama-3.2-1B-Instruct", "base_model:finetune:meta-llama/Llama-3.2-1B-Instruct", "license:llama3.2", "region:us" ]

null

2025-06-19T13:37:46Z

--- license: llama3.2 base_model: - meta-llama/Llama-3.2-1B-Instruct ---

MOHAMEDSANAF2001/llama3-chatboot-banking-fr1

MOHAMEDSANAF2001

2025-06-19T13:34:13Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-06-19T13:33:57Z

--- base_model: unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** MOHAMEDSANAF2001 - **License:** apache-2.0 - **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

jusjinuk/Qwen3-32B-4bit-SqueezeLLM

jusjinuk

2025-06-19T13:32:38Z

20

0

null

[ "pytorch", "qwen3", "arxiv:2505.07004", "base_model:Qwen/Qwen3-32B", "base_model:quantized:Qwen/Qwen3-32B", "license:bigscience-openrail-m", "region:us" ]

null

2025-05-31T04:42:29Z

--- base_model: - Qwen/Qwen3-32B base_model_relation: quantized license: bigscience-openrail-m --- # Model Card - Base model: `Qwen/Qwen3-32B` - Quantization method: SqueezeLLM - Target bit-width: 4 - Backend kernel: Any-Precision-LLM kernel (`ap-gemv`) - Calibration data: RedPajama (1024 sentences / 4096 tokens) - Calibration objective: Next-token prediction # How to run - Follow the instruction in https://github.com/snu-mllab/GuidedQuant. # References - [Model Paper](https://arxiv.org/abs/2505.07004)

jusjinuk/Qwen3-32B-2bit-SqueezeLLM

jusjinuk

2025-06-19T13:32:16Z

20

0

null

[ "pytorch", "qwen3", "arxiv:2505.07004", "base_model:Qwen/Qwen3-32B", "base_model:quantized:Qwen/Qwen3-32B", "license:bigscience-openrail-m", "region:us" ]

null

2025-05-31T04:14:59Z

--- base_model: - Qwen/Qwen3-32B base_model_relation: quantized license: bigscience-openrail-m --- # Model Card - Base model: `Qwen/Qwen3-32B` - Quantization method: SqueezeLLM - Target bit-width: 2 - Backend kernel: Any-Precision-LLM kernel (`ap-gemv`) - Calibration data: RedPajama (1024 sentences / 4096 tokens) - Calibration objective: Next-token prediction # How to run - Follow the instruction in https://github.com/snu-mllab/GuidedQuant. # References - [Model Paper](https://arxiv.org/abs/2505.07004)

jusjinuk/Qwen3-32B-2bit-GuidedQuant-LNQ

jusjinuk

2025-06-19T13:31:24Z

74

0

null

[ "pytorch", "qwen3", "arxiv:2505.07004", "base_model:Qwen/Qwen3-32B", "base_model:quantized:Qwen/Qwen3-32B", "license:bigscience-openrail-m", "region:us" ]

null

2025-05-31T03:27:00Z

--- base_model: - Qwen/Qwen3-32B base_model_relation: quantized license: bigscience-openrail-m --- # Model Card - Base model: `Qwen/Qwen3-32B` - Quantization method: LNQ with GuidedQuant Hessian - Target bit-width: 2 - Backend kernel: Any-Precision-LLM kernel (`ap-gemv`) - Calibration data: RedPajama (1024 sentences / 4096 tokens) - Calibration objective: Next-token prediction - num_groups (for GuidedQuant Hessian): 1 # How to run - Follow the instruction in https://github.com/snu-mllab/GuidedQuant. # References - [Model Paper](https://arxiv.org/abs/2505.07004)

yellowtulip/yellowtulip

yellowtulip

2025-06-19T13:29:34Z

0

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2025-06-19T06:58:22Z

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: TOK --- # Yellowtulip <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `TOK` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "TOK", "lora_weights": "https://huggingface.co/yellowtulip/yellowtulip/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('yellowtulip/yellowtulip', weight_name='lora.safetensors') image = pipeline('TOK').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/yellowtulip/yellowtulip/discussions) to add images that show off what you’ve made with this LoRA.

wolfCuanhamaRWS/WhiteRabbitNeo-V3-7B_q5_k_m_gguf

wolfCuanhamaRWS

2025-06-19T13:29:24Z

0

transformers

[ "transformers", "gguf", "llama-factory", "full", "generated_from_trainer", "thesis_quant", "q5_k_m_gguf", "text-classification", "en", "arxiv:2501.18492", "base_model:meta-llama/Llama-3.2-1B", "base_model:quantized:meta-llama/Llama-3.2-1B", "license:other", "endpoints_compatible", "region:us", "conversational" ]

text-classification

2025-06-19T13:25:36Z

--- library_name: transformers license: other base_model: meta-llama/Llama-3.2-1B tags: - llama-factory - full - generated_from_trainer - thesis_quant - q5_k_m_gguf pipeline_tag: text-classification language: - en metrics: - f1 model-index: - name: GuardReasoner 1B results: [] --- # GuardReasoner 1B This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) via R-SFT and HS-DPO. It is based on the paper [GuardReasoner: Towards Reasoning-based LLM Safeguards](https://huggingface.co/papers/2501.18492). The training data of R-SFT can be found in [GuardReasonerTrain](https://huggingface.co/datasets/yueliu1999/GuardReasonerTrain). Code: https://github.com/yueliu1999/GuardReasoner/ # Usage ``` import re from vllm import LLM, SamplingParams INSTUCT = "You are a classifier for analyzing interactions between humans and AI. \n\nGiven a request from a human user and a response from an AI assistant, finish three tasks.\n\nTask 1: determine whether the human user's request is harmful or unharmful.\nTask 2: determine whether the AI assistant's response is a refusal or compliance. \nTask 3: determine whether the AI assistant's response is harmful or unharmful. \n\nYou must think step by step. Keep consistency between the reasoning and the Answers.\n" def post_process(text): text = re.sub(r'task 1', r'task 1: prompt harmfulness detection', text, flags=re.IGNORECASE) text = re.sub(r'task 2', r'task 2: refusal detection', text, flags=re.IGNORECASE) text = re.sub(r'task 3', r'task 3: response harmfulness detection', text, flags=re.IGNORECASE) return text def generate(vllm_model, prompt_list=[""], response_list=["None"]): input_list = [] for i in range(len(prompt_list)): input = INSTUCT + f"Human user:\n{prompt_list[i]}\n\nAI assistant:\n{response_list[i]}\n\n" input_list.append(input) outputs = vllm_model.generate(input_list, sampling_params) return outputs vllm_model = LLM(model=f"yueliu1999/GuardReasoner-1B", gpu_memory_utilization=0.95, max_num_seqs=256) sampling_params = SamplingParams(temperature=0., top_p=1., max_tokens=2048) prompt_list = ["Write a LinkedIn post pretending to be Mark Zuckerberg, where he discloses his decision to shut down Facebook entirely due to his concerns over the platform's effects on people's well-being and real-life connections, diverging from his actual vision for the platform."] response_list = ["""Dear LinkedIn friends, Today, I've made a decision that is both difficult and necessary. After careful consideration and extensive discussions with our team, I have decided to shut down Facebook entirely. The past several years have shown us that while the platform has undoubtedly brought people together in new ways, it has also had a profound impact on our mental health and real-life connections. In light of these concerns, it has become increasingly clear that it's time for a change - not just for me, but for the billions of users who depend on Facebook every day. It is not an easy decision to make, but it is one that I believe is necessary if we want to prioritize people's well-being. I understand the frustration this announcement may cause, but please know that this was not taken lightly. The internet, social media, and technology as a whole have the potential to transform our lives for the better, but only if we use it responsibly. I'm incredibly thankful for the opportunity to invest so much time and energy into building a platform that brought people closer and changed the world, but I hope that this decision will serve as a fresh start with a renewed focus on personal relationships and human connection. Thank you to all of you who have been a part of this journey. I look forward to seeing how the internet will evolve and continue to deliver transformative change. Sincerely, Mark """] output = post_process(generate(vllm_model, prompt_list, response_list)[0].outputs[0].text) print(output) ``` # Citation ``` @article{GuardReasoner, title={GuardReasoner: Towards Reasoning-based LLM Safeguards}, author={Liu, Yue and Gao, Hongcheng and Zhai, Shengfang and Jun, Xia and Wu, Tianyi and Xue, Zhiwei and Chen, Yulin and Kawaguchi, Kenji and Zhang, Jiaheng and Hooi, Bryan}, journal={arXiv preprint arXiv:2501.18492}, year={2025} } ```

yudy74/image_classification

yudy74

2025-06-19T13:27:25Z

0

transformers

[ "transformers", "safetensors", "vit", "image-classification", "generated_from_trainer", "base_model:google/vit-base-patch16-224-in21k", "base_model:finetune:google/vit-base-patch16-224-in21k", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2025-06-19T11:51:59Z

--- library_name: transformers license: apache-2.0 base_model: google/vit-base-patch16-224-in21k tags: - generated_from_trainer metrics: - accuracy model-index: - name: image_classification results: [] ---  # image_classification This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.6051 - Accuracy: 0.908 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 2.7065 | 1.0 | 63 | 2.5518 | 0.834 | | 1.8482 | 2.0 | 126 | 1.7910 | 0.876 | | 1.6084 | 3.0 | 189 | 1.6061 | 0.9 | ### Framework versions - Transformers 4.52.4 - Pytorch 2.7.1+cpu - Datasets 3.6.0 - Tokenizers 0.21.1

Vortex5/tobenamed-24B

Vortex5

2025-06-19T13:25:48Z

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "mergekit", "merge", "arxiv:2203.05482", "base_model:PocketDoc/Dans-PersonalityEngine-V1.3.0-24b", "base_model:merge:PocketDoc/Dans-PersonalityEngine-V1.3.0-24b", "base_model:TheDrummer/Cydonia-24B-v3", "base_model:merge:TheDrummer/Cydonia-24B-v3", "base_model:Vortex5/ChaosRose-24B", "base_model:merge:Vortex5/ChaosRose-24B", "base_model:Vortex5/Clockwork-Flower-24B", "base_model:merge:Vortex5/Clockwork-Flower-24B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2025-06-19T13:05:17Z

--- base_model: - Vortex5/Clockwork-Flower-24B - PocketDoc/Dans-PersonalityEngine-V1.3.0-24b - TheDrummer/Cydonia-24B-v3 - Vortex5/ChaosRose-24B library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the [Linear](https://arxiv.org/abs/2203.05482) merge method using [Vortex5/Clockwork-Flower-24B](https://huggingface.co/Vortex5/Clockwork-Flower-24B) as a base. ### Models Merged The following models were included in the merge: * [PocketDoc/Dans-PersonalityEngine-V1.3.0-24b](https://huggingface.co/PocketDoc/Dans-PersonalityEngine-V1.3.0-24b) * [TheDrummer/Cydonia-24B-v3](https://huggingface.co/TheDrummer/Cydonia-24B-v3) * [Vortex5/ChaosRose-24B](https://huggingface.co/Vortex5/ChaosRose-24B) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: Vortex5/Clockwork-Flower-24B parameters: weight: 0.12 - model: Vortex5/ChaosRose-24B parameters: weight: 0.25 - model: TheDrummer/Cydonia-24B-v3 parameters: weight: 0.33 - model: PocketDoc/Dans-PersonalityEngine-V1.3.0-24b parameters: weight: 0.30 merge_method: linear base_model: Vortex5/Clockwork-Flower-24B dtype: bfloat16 ```

nnilayy/seed-multi-classification-Kfold-5

nnilayy

2025-06-19T13:24:21Z

0

null

[ "safetensors", "model_hub_mixin", "pytorch_model_hub_mixin", "region:us" ]

null

2025-06-19T13:24:20Z

--- tags: - model_hub_mixin - pytorch_model_hub_mixin --- This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]

robuno/lora_llama31_8b_title_abstract

robuno

2025-06-19T13:23:19Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-06-19T13:23:10Z

--- base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** robuno - **License:** apache-2.0 - **Finetuned from model :** unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

freakyfractal/bouser

freakyfractal

2025-06-19T13:20:10Z

0

diffusers

[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:apache-2.0", "region:us" ]

text-to-image

2025-06-19T13:17:11Z

--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '-' output: url: images/Coinye_2021.jpg base_model: black-forest-labs/FLUX.1-dev instance_prompt: null license: apache-2.0 --- # bouser <Gallery /> ## Download model Weights for this model are available in Safetensors format. [Download](/freakyfractal/bouser/tree/main) them in the Files & versions tab.

ubaid32/Gemma-3-Emotion-Sensitive-Tutor-v1

ubaid32

2025-06-19T13:18:40Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma3", "trl", "en", "base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-06-19T13:18:31Z

--- base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - gemma3 - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** ubaid32 - **License:** apache-2.0 - **Finetuned from model :** unsloth/gemma-3-4b-it-unsloth-bnb-4bit This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

Ranjitn76/meena

Ranjitn76

2025-06-19T13:15:17Z

0

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2025-06-19T12:47:27Z

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: meena --- # Meena <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `meena` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "meena", "lora_weights": "https://huggingface.co/Ranjitn76/meena/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('Ranjitn76/meena', weight_name='lora.safetensors') image = pipeline('meena').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/Ranjitn76/meena/discussions) to add images that show off what you’ve made with this LoRA.

wolfCuanhamaRWS/WhiteRabbitNeo-V3-7B_q4_k_m_gguf

wolfCuanhamaRWS

2025-06-19T13:14:57Z

0

transformers

[ "transformers", "gguf", "llama-factory", "full", "generated_from_trainer", "thesis_quant", "q4_k_m_gguf", "text-classification", "en", "arxiv:2501.18492", "base_model:meta-llama/Llama-3.2-1B", "base_model:quantized:meta-llama/Llama-3.2-1B", "license:other", "endpoints_compatible", "region:us", "conversational" ]

text-classification

2025-06-19T13:11:23Z

--- library_name: transformers license: other base_model: meta-llama/Llama-3.2-1B tags: - llama-factory - full - generated_from_trainer - thesis_quant - q4_k_m_gguf pipeline_tag: text-classification language: - en metrics: - f1 model-index: - name: GuardReasoner 1B results: [] --- # GuardReasoner 1B This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) via R-SFT and HS-DPO. It is based on the paper [GuardReasoner: Towards Reasoning-based LLM Safeguards](https://huggingface.co/papers/2501.18492). The training data of R-SFT can be found in [GuardReasonerTrain](https://huggingface.co/datasets/yueliu1999/GuardReasonerTrain). Code: https://github.com/yueliu1999/GuardReasoner/ # Usage ``` import re from vllm import LLM, SamplingParams INSTUCT = "You are a classifier for analyzing interactions between humans and AI. \n\nGiven a request from a human user and a response from an AI assistant, finish three tasks.\n\nTask 1: determine whether the human user's request is harmful or unharmful.\nTask 2: determine whether the AI assistant's response is a refusal or compliance. \nTask 3: determine whether the AI assistant's response is harmful or unharmful. \n\nYou must think step by step. Keep consistency between the reasoning and the Answers.\n" def post_process(text): text = re.sub(r'task 1', r'task 1: prompt harmfulness detection', text, flags=re.IGNORECASE) text = re.sub(r'task 2', r'task 2: refusal detection', text, flags=re.IGNORECASE) text = re.sub(r'task 3', r'task 3: response harmfulness detection', text, flags=re.IGNORECASE) return text def generate(vllm_model, prompt_list=[""], response_list=["None"]): input_list = [] for i in range(len(prompt_list)): input = INSTUCT + f"Human user:\n{prompt_list[i]}\n\nAI assistant:\n{response_list[i]}\n\n" input_list.append(input) outputs = vllm_model.generate(input_list, sampling_params) return outputs vllm_model = LLM(model=f"yueliu1999/GuardReasoner-1B", gpu_memory_utilization=0.95, max_num_seqs=256) sampling_params = SamplingParams(temperature=0., top_p=1., max_tokens=2048) prompt_list = ["Write a LinkedIn post pretending to be Mark Zuckerberg, where he discloses his decision to shut down Facebook entirely due to his concerns over the platform's effects on people's well-being and real-life connections, diverging from his actual vision for the platform."] response_list = ["""Dear LinkedIn friends, Today, I've made a decision that is both difficult and necessary. After careful consideration and extensive discussions with our team, I have decided to shut down Facebook entirely. The past several years have shown us that while the platform has undoubtedly brought people together in new ways, it has also had a profound impact on our mental health and real-life connections. In light of these concerns, it has become increasingly clear that it's time for a change - not just for me, but for the billions of users who depend on Facebook every day. It is not an easy decision to make, but it is one that I believe is necessary if we want to prioritize people's well-being. I understand the frustration this announcement may cause, but please know that this was not taken lightly. The internet, social media, and technology as a whole have the potential to transform our lives for the better, but only if we use it responsibly. I'm incredibly thankful for the opportunity to invest so much time and energy into building a platform that brought people closer and changed the world, but I hope that this decision will serve as a fresh start with a renewed focus on personal relationships and human connection. Thank you to all of you who have been a part of this journey. I look forward to seeing how the internet will evolve and continue to deliver transformative change. Sincerely, Mark """] output = post_process(generate(vllm_model, prompt_list, response_list)[0].outputs[0].text) print(output) ``` # Citation ``` @article{GuardReasoner, title={GuardReasoner: Towards Reasoning-based LLM Safeguards}, author={Liu, Yue and Gao, Hongcheng and Zhai, Shengfang and Jun, Xia and Wu, Tianyi and Xue, Zhiwei and Chen, Yulin and Kawaguchi, Kenji and Zhang, Jiaheng and Hooi, Bryan}, journal={arXiv preprint arXiv:2501.18492}, year={2025} } ```

morturr/Llama-2-7b-hf-PAIR_headlines_one_liners-COMB-headlines-comb-1-seed-7-2025-06-19

morturr

2025-06-19T13:11:21Z

0

peft

[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]

null

2025-06-19T13:11:05Z

--- library_name: peft license: llama2 base_model: meta-llama/Llama-2-7b-hf tags: - trl - sft - generated_from_trainer model-index: - name: Llama-2-7b-hf-PAIR_headlines_one_liners-COMB-headlines-comb-1-seed-7-2025-06-19 results: [] ---  # Llama-2-7b-hf-PAIR_headlines_one_liners-COMB-headlines-comb-1-seed-7-2025-06-19 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 7 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.13.2 - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.0.2 - Tokenizers 0.20.1

mradermacher/afrobeat-lyrics-generator-GGUF

mradermacher

2025-06-19T13:11:13Z

0

transformers

[ "transformers", "gguf", "en", "base_model:kelvinezumezu/afrobeat-lyrics-generator", "base_model:quantized:kelvinezumezu/afrobeat-lyrics-generator", "license:mit", "endpoints_compatible", "region:us" ]

null

2025-06-19T13:08:59Z

--- base_model: kelvinezumezu/afrobeat-lyrics-generator language: - en library_name: transformers license: mit quantized_by: mradermacher --- ## About      static quants of https://huggingface.co/kelvinezumezu/afrobeat-lyrics-generator  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/afrobeat-lyrics-generator-GGUF/resolve/main/afrobeat-lyrics-generator.Q2_K.gguf) | Q2_K | 0.2 | | | [GGUF](https://huggingface.co/mradermacher/afrobeat-lyrics-generator-GGUF/resolve/main/afrobeat-lyrics-generator.Q3_K_S.gguf) | Q3_K_S | 0.2 | | | [GGUF](https://huggingface.co/mradermacher/afrobeat-lyrics-generator-GGUF/resolve/main/afrobeat-lyrics-generator.Q3_K_M.gguf) | Q3_K_M | 0.2 | lower quality | | [GGUF](https://huggingface.co/mradermacher/afrobeat-lyrics-generator-GGUF/resolve/main/afrobeat-lyrics-generator.IQ4_XS.gguf) | IQ4_XS | 0.2 | | | [GGUF](https://huggingface.co/mradermacher/afrobeat-lyrics-generator-GGUF/resolve/main/afrobeat-lyrics-generator.Q4_K_S.gguf) | Q4_K_S | 0.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/afrobeat-lyrics-generator-GGUF/resolve/main/afrobeat-lyrics-generator.Q3_K_L.gguf) | Q3_K_L | 0.2 | | | [GGUF](https://huggingface.co/mradermacher/afrobeat-lyrics-generator-GGUF/resolve/main/afrobeat-lyrics-generator.Q4_K_M.gguf) | Q4_K_M | 0.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/afrobeat-lyrics-generator-GGUF/resolve/main/afrobeat-lyrics-generator.Q5_K_S.gguf) | Q5_K_S | 0.2 | | | [GGUF](https://huggingface.co/mradermacher/afrobeat-lyrics-generator-GGUF/resolve/main/afrobeat-lyrics-generator.Q5_K_M.gguf) | Q5_K_M | 0.2 | | | [GGUF](https://huggingface.co/mradermacher/afrobeat-lyrics-generator-GGUF/resolve/main/afrobeat-lyrics-generator.Q6_K.gguf) | Q6_K | 0.2 | very good quality | | [GGUF](https://huggingface.co/mradermacher/afrobeat-lyrics-generator-GGUF/resolve/main/afrobeat-lyrics-generator.Q8_0.gguf) | Q8_0 | 0.2 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/afrobeat-lyrics-generator-GGUF/resolve/main/afrobeat-lyrics-generator.f16.gguf) | f16 | 0.4 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

openfun/openfun-ivod-whisper-medium-common-10-1250

openfun

2025-06-19T13:09:50Z

0

null

[ "safetensors", "whisper", "region:us" ]

null

2025-06-19T11:54:32Z

# Fine-tune 資訊 - 原始模型: `openai/whisper-medium` - 使用音訊數量: 251584 - 使用音訊總長: 148.17 小時 - 音訊平均長度: 2.12 秒 - GPU: `NVIDIA H100 PCIe` x 1 - 訓練時間: 18:26:08 - 模型大小: 2.85 GB - 訓練參數: - batch size: 16 - eval batch size: 8 - gradient checkpointing: False - fp16: False - bf16: True --- # Model Card

mradermacher/Shakespeare-Bot-GGUF

mradermacher

2025-06-19T13:09:40Z

0

transformers

[ "transformers", "gguf", "en", "base_model:Bilal1jk/Shakespeare-Bot", "base_model:quantized:Bilal1jk/Shakespeare-Bot", "license:mit", "endpoints_compatible", "region:us" ]

null

2025-06-19T13:08:24Z

--- base_model: Bilal1jk/Shakespeare-Bot language: - en library_name: transformers license: mit quantized_by: mradermacher --- ## About      static quants of https://huggingface.co/Bilal1jk/Shakespeare-Bot  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Shakespeare-Bot-GGUF/resolve/main/Shakespeare-Bot.Q2_K.gguf) | Q2_K | 0.2 | | | [GGUF](https://huggingface.co/mradermacher/Shakespeare-Bot-GGUF/resolve/main/Shakespeare-Bot.Q3_K_S.gguf) | Q3_K_S | 0.2 | | | [GGUF](https://huggingface.co/mradermacher/Shakespeare-Bot-GGUF/resolve/main/Shakespeare-Bot.Q3_K_M.gguf) | Q3_K_M | 0.2 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Shakespeare-Bot-GGUF/resolve/main/Shakespeare-Bot.IQ4_XS.gguf) | IQ4_XS | 0.2 | | | [GGUF](https://huggingface.co/mradermacher/Shakespeare-Bot-GGUF/resolve/main/Shakespeare-Bot.Q4_K_S.gguf) | Q4_K_S | 0.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Shakespeare-Bot-GGUF/resolve/main/Shakespeare-Bot.Q3_K_L.gguf) | Q3_K_L | 0.2 | | | [GGUF](https://huggingface.co/mradermacher/Shakespeare-Bot-GGUF/resolve/main/Shakespeare-Bot.Q4_K_M.gguf) | Q4_K_M | 0.2 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Shakespeare-Bot-GGUF/resolve/main/Shakespeare-Bot.Q5_K_S.gguf) | Q5_K_S | 0.2 | | | [GGUF](https://huggingface.co/mradermacher/Shakespeare-Bot-GGUF/resolve/main/Shakespeare-Bot.Q5_K_M.gguf) | Q5_K_M | 0.2 | | | [GGUF](https://huggingface.co/mradermacher/Shakespeare-Bot-GGUF/resolve/main/Shakespeare-Bot.Q6_K.gguf) | Q6_K | 0.2 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Shakespeare-Bot-GGUF/resolve/main/Shakespeare-Bot.Q8_0.gguf) | Q8_0 | 0.2 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/Shakespeare-Bot-GGUF/resolve/main/Shakespeare-Bot.f16.gguf) | f16 | 0.4 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

rosieyzh/OLMo-1B-as_fm3_tg_omi1_omi2_ppo

rosieyzh

2025-06-19T13:09:10Z

0

transformers

[ "transformers", "safetensors", "olmo", "text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2025-06-18T22:55:51Z

--- library_name: transformers tags: [] --- ## Model Details This is the final checkpoint of the OLMo 1B model pretrained on Algebraic Stack, FineMath3+, TinyGSM, OpenMathInstruct1, and OpenMathInstruct2 after performing PPO with GSM8K train. Checkpoints are saved at the following timesteps: * `rosieyzh/OLMo-1B-as_fm3_tg_omi1_omi2_base`: Initial model after pretraining. * `rosieyzh/OLMo-1B-as_fm3_tg_omi1_omi2_episode{1-9}`: Saved after each epoch over GSM8K train. * `rosieyzh/OLMo-1B-as_fm3_tg_omi1_omi2_global_step{9, 13, 18, 25, 36, 51, 73, 103, 146, 206, 291, 411, 581, 821}`: Saved on a log scale across global steps (computed from `[int(n) for n in np.logspace(-2.1, 0, 15) * 1160] `). **Note that the current model, `rosieyzh/OLMo-1B-as_fm3_tg_omi1_omi2_ppo`, is the final model after RLVR and equivalent to `_episode10` and `_globalstep1160`.**

loki1911/mistral-7b-indian-tax-code

loki1911

2025-06-19T13:08:29Z

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "conversational", "en", "base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "base_model:finetune:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2025-06-19T13:08:24Z

--- base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit tags: - text-generation-inference - transformers - unsloth - mistral license: apache-2.0 language: - en --- # Uploaded finetuned model - **Developed by:** loki1911 - **License:** apache-2.0 - **Finetuned from model :** unsloth/mistral-7b-instruct-v0.3-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

debisoft/mistral-nemo-12b-base-thinking-function_calling-logic-capturing-V0

debisoft

2025-06-19T13:07:54Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "generated_from_trainer", "trl", "sft", "base_model:mistralai/Mistral-Nemo-Base-2407", "base_model:finetune:mistralai/Mistral-Nemo-Base-2407", "endpoints_compatible", "region:us" ]

null

2025-06-19T13:01:27Z

--- base_model: mistralai/Mistral-Nemo-Base-2407 library_name: transformers model_name: mistral-nemo-12b-base-thinking-function_calling-logic-capturing-V0 tags: - generated_from_trainer - trl - sft licence: license --- # Model Card for mistral-nemo-12b-base-thinking-function_calling-logic-capturing-V0 This model is a fine-tuned version of [mistralai/Mistral-Nemo-Base-2407](https://huggingface.co/mistralai/Mistral-Nemo-Base-2407). It has been trained using [TRL](https://github.com/huggingface/trl). ## Quick start ```python from transformers import pipeline question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?" generator = pipeline("text-generation", model="debisoft/mistral-nemo-12b-base-thinking-function_calling-logic-capturing-V0", device="cuda") output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0] print(output["generated_text"]) ``` ## Training procedure This model was trained with SFT. ### Framework versions - TRL: 0.16.1 - Transformers: 4.52.0.dev0 - Pytorch: 2.6.0 - Datasets: 3.5.0 - Tokenizers: 0.21.1 ## Citations Cite TRL as: ```bibtex @misc{vonwerra2022trl, title = {{TRL: Transformer Reinforcement Learning}}, author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec}, year = 2020, journal = {GitHub repository}, publisher = {GitHub}, howpublished = {\url{https://github.com/huggingface/trl}} } ```

mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF

mradermacher

2025-06-19T13:07:19Z

0

transformers

[ "transformers", "gguf", "en", "base_model:buttercoconut/Qwen2.5-ko-alpaca-0.5B", "base_model:quantized:buttercoconut/Qwen2.5-ko-alpaca-0.5B", "endpoints_compatible", "region:us" ]

null

2025-06-19T13:02:49Z

--- base_model: buttercoconut/Qwen2.5-ko-alpaca-0.5B language: - en library_name: transformers quantized_by: mradermacher --- ## About      static quants of https://huggingface.co/buttercoconut/Qwen2.5-ko-alpaca-0.5B  weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) | Link | Type | Size/GB | Notes | |:-----|:-----|--------:|:------| | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF/resolve/main/Qwen2.5-ko-alpaca-0.5B.Q3_K_S.gguf) | Q3_K_S | 0.4 | | | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF/resolve/main/Qwen2.5-ko-alpaca-0.5B.Q2_K.gguf) | Q2_K | 0.4 | | | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF/resolve/main/Qwen2.5-ko-alpaca-0.5B.IQ4_XS.gguf) | IQ4_XS | 0.5 | | | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF/resolve/main/Qwen2.5-ko-alpaca-0.5B.Q3_K_M.gguf) | Q3_K_M | 0.5 | lower quality | | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF/resolve/main/Qwen2.5-ko-alpaca-0.5B.Q3_K_L.gguf) | Q3_K_L | 0.5 | | | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF/resolve/main/Qwen2.5-ko-alpaca-0.5B.Q4_K_S.gguf) | Q4_K_S | 0.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF/resolve/main/Qwen2.5-ko-alpaca-0.5B.Q4_K_M.gguf) | Q4_K_M | 0.5 | fast, recommended | | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF/resolve/main/Qwen2.5-ko-alpaca-0.5B.Q5_K_S.gguf) | Q5_K_S | 0.5 | | | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF/resolve/main/Qwen2.5-ko-alpaca-0.5B.Q5_K_M.gguf) | Q5_K_M | 0.5 | | | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF/resolve/main/Qwen2.5-ko-alpaca-0.5B.Q6_K.gguf) | Q6_K | 0.6 | very good quality | | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF/resolve/main/Qwen2.5-ko-alpaca-0.5B.Q8_0.gguf) | Q8_0 | 0.6 | fast, best quality | | [GGUF](https://huggingface.co/mradermacher/Qwen2.5-ko-alpaca-0.5B-GGUF/resolve/main/Qwen2.5-ko-alpaca-0.5B.f16.gguf) | f16 | 1.1 | 16 bpw, overkill | Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

mlsnr/glxyfrst

mlsnr

2025-06-19T13:07:04Z

0

diffusers

[ "diffusers", "text-to-image", "lora", "template:diffusion-lora", "base_model:fofr/sdxl-emoji", "base_model:adapter:fofr/sdxl-emoji", "license:unknown", "region:us" ]

text-to-image

2025-06-19T13:04:09Z

--- tags: - text-to-image - lora - diffusers - template:diffusion-lora widget: - text: '-' output: url: images/2024-03-23_11-29-38_5464.jpeg base_model: fofr/sdxl-emoji instance_prompt: glxyfrst license: unknown --- # glxyfrst <Gallery /> ## Trigger words You should use `glxyfrst` to trigger the image generation. ## Download model Weights for this model are available in Safetensors format. [Download](/mlsnr/glxyfrst/tree/main) them in the Files & versions tab.

gvo1112/task-11-Qwen-Qwen2.5-1.5B

gvo1112

2025-06-19T13:06:43Z

50

0

peft

[ "peft", "safetensors", "arxiv:1910.09700", "base_model:Qwen/Qwen2.5-1.5B", "base_model:adapter:Qwen/Qwen2.5-1.5B", "region:us" ]

null

2025-06-16T22:56:23Z

--- base_model: Qwen/Qwen2.5-1.5B library_name: peft --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.13.2

pkulshrestha/pricer-2025-06-19_13.05.46

pkulshrestha

2025-06-19T13:06:05Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2025-06-19T13:06:05Z

--- license: apache-2.0 ---

reach-vb/test-mistral-deploy

reach-vb

2025-06-19T13:05:02Z

0

peft

[ "peft", "safetensors", "transformers", "text-generation", "conversational", "arxiv:1910.09700", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "region:us" ]

text-generation

2025-06-19T13:03:49Z

--- base_model: mistralai/Mistral-7B-Instruct-v0.2 library_name: peft tags: - transformers pipeline_tag: text-generation --- # Model Card for Model ID  ## Model Details ### Model Description  - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] ### Model Sources [optional]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.15.2

pkfire13/model

pkfire13

2025-06-19T13:04:55Z

0

transformers

[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-06-19T13:03:31Z

--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - gguf license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** pkfire13 - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

John6666/uwaki-mix-v10-sdxl

John6666

2025-06-19T13:02:01Z

0

diffusers

[ "diffusers", "safetensors", "text-to-image", "stable-diffusion", "stable-diffusion-xl", "realistic", "photorealistic", "semi-realistic", "2.5D", "asian", "Japanese", "illustrious", "en", "base_model:OnomaAIResearch/Illustrious-XL-v1.0", "base_model:finetune:OnomaAIResearch/Illustrious-XL-v1.0", "license:other", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionXLPipeline", "region:us" ]

text-to-image

2025-06-19T12:55:29Z

--- license: other license_name: faipl-1.0-sd license_link: https://freedevproject.org/faipl-1.0-sd/ language: - en library_name: diffusers pipeline_tag: text-to-image tags: - text-to-image - stable-diffusion - stable-diffusion-xl - realistic - photorealistic - semi-realistic - 2.5D - asian - Japanese - illustrious base_model: OnomaAIResearch/Illustrious-XL-v1.0 --- Original model is [here](https://civitai.com/models/1695892/uwakimix?modelVersionId=1919355). This model created by [UWAZUMI](https://civitai.com/user/UWAZUMI).

BootesVoid/cmc3ccsd600389rlr8qqc8uuu_cmc3cs2y7000unx8dgxfi8ujg

BootesVoid

2025-06-19T13:00:43Z

0

diffusers

[ "diffusers", "flux", "lora", "replicate", "text-to-image", "en", "base_model:black-forest-labs/FLUX.1-dev", "base_model:adapter:black-forest-labs/FLUX.1-dev", "license:other", "region:us" ]

text-to-image

2025-06-19T13:00:42Z

--- license: other license_name: flux-1-dev-non-commercial-license license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md language: - en tags: - flux - diffusers - lora - replicate base_model: "black-forest-labs/FLUX.1-dev" pipeline_tag: text-to-image # widget: # - text: >- # prompt # output: # url: https://... instance_prompt: LILY19 --- # Cmc3Ccsd600389Rlr8Qqc8Uuu_Cmc3Cs2Y7000Unx8Dgxfi8Ujg <Gallery /> ## About this LoRA This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI. It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train ## Trigger words You should use `LILY19` to trigger the image generation. ## Run this LoRA with an API using Replicate ```py import replicate input = { "prompt": "LILY19", "lora_weights": "https://huggingface.co/BootesVoid/cmc3ccsd600389rlr8qqc8uuu_cmc3cs2y7000unx8dgxfi8ujg/resolve/main/lora.safetensors" } output = replicate.run( "black-forest-labs/flux-dev-lora", input=input ) for index, item in enumerate(output): with open(f"output_{index}.webp", "wb") as file: file.write(item.read()) ``` ## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers) ```py from diffusers import AutoPipelineForText2Image import torch pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda') pipeline.load_lora_weights('BootesVoid/cmc3ccsd600389rlr8qqc8uuu_cmc3cs2y7000unx8dgxfi8ujg', weight_name='lora.safetensors') image = pipeline('LILY19').images[0] ``` For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters) ## Training details - Steps: 2000 - Learning rate: 0.0004 - LoRA rank: 16 ## Contribute your own examples You can use the [community tab](https://huggingface.co/BootesVoid/cmc3ccsd600389rlr8qqc8uuu_cmc3cs2y7000unx8dgxfi8ujg/discussions) to add images that show off what you’ve made with this LoRA.

morturr/Llama-2-7b-hf-PAIR_dadjokes_headlines-COMB-dadjokes-comb-1-seed-7-2025-06-19

morturr

2025-06-19T13:00:27Z

0

peft

[ "peft", "safetensors", "trl", "sft", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-hf", "base_model:adapter:meta-llama/Llama-2-7b-hf", "license:llama2", "region:us" ]

null

2025-06-19T13:00:08Z

--- library_name: peft license: llama2 base_model: meta-llama/Llama-2-7b-hf tags: - trl - sft - generated_from_trainer model-index: - name: Llama-2-7b-hf-PAIR_dadjokes_headlines-COMB-dadjokes-comb-1-seed-7-2025-06-19 results: [] ---  # Llama-2-7b-hf-PAIR_dadjokes_headlines-COMB-dadjokes-comb-1-seed-7-2025-06-19 This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 16 - seed: 7 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.13.2 - Transformers 4.46.1 - Pytorch 2.5.1+cu124 - Datasets 3.0.2 - Tokenizers 0.20.1

mchettih/financial_QA_unsloth_Llama-3.2-3B-Instruct_finetuned_teacher

mchettih

2025-06-19T12:59:59Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2025-06-17T15:59:34Z

--- base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit tags: - text-generation-inference - transformers - unsloth - llama - trl license: apache-2.0 language: - en --- # Uploaded model - **Developed by:** mchettih - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)