--- license: cc-by-nc-4.0 language: - ro base_model: OpenLLM-Ro/RoLlama3-8b-Instruct-2024-06-28 datasets: - OpenLLM-Ro/ro_sft_alpaca - OpenLLM-Ro/ro_sft_alpaca_gpt4 - OpenLLM-Ro/ro_sft_dolly - OpenLLM-Ro/ro_sft_selfinstruct_gpt4 - OpenLLM-Ro/ro_sft_norobots - OpenLLM-Ro/ro_sft_orca - OpenLLM-Ro/ro_sft_camel tags: - llama-cpp - gguf-my-repo model-index: - name: OpenLLM-Ro/RoLlama3-8b-Instruct-2024-06-28 results: - task: type: text-generation dataset: name: RoMT-Bench type: RoMT-Bench metrics: - type: Score value: 5.15 name: Score - type: Score value: 6.03 name: First turn - type: Score value: 4.28 name: Second turn - task: type: text-generation dataset: name: RoCulturaBench type: RoCulturaBench metrics: - type: Score value: 3.71 name: Score - task: type: text-generation dataset: name: Romanian_Academic_Benchmarks type: Romanian_Academic_Benchmarks metrics: - type: accuracy value: 50.56 name: Average accuracy - task: type: text-generation dataset: name: OpenLLM-Ro/ro_arc_challenge type: OpenLLM-Ro/ro_arc_challenge metrics: - type: accuracy value: 44.7 name: Average accuracy - type: accuracy value: 41.9 name: 0-shot - type: accuracy value: 44.3 name: 1-shot - type: accuracy value: 44.56 name: 3-shot - type: accuracy value: 45.5 name: 5-shot - type: accuracy value: 46.1 name: 10-shot - type: accuracy value: 45.84 name: 25-shot - task: type: text-generation dataset: name: OpenLLM-Ro/ro_mmlu type: OpenLLM-Ro/ro_mmlu metrics: - type: accuracy value: 52.19 name: Average accuracy - type: accuracy value: 50.85 name: 0-shot - type: accuracy value: 51.24 name: 1-shot - type: accuracy value: 53.3 name: 3-shot - type: accuracy value: 53.39 name: 5-shot - task: type: text-generation dataset: name: OpenLLM-Ro/ro_winogrande type: OpenLLM-Ro/ro_winogrande metrics: - type: accuracy value: 67.23 name: Average accuracy - type: accuracy value: 65.19 name: 0-shot - type: accuracy value: 66.54 name: 1-shot - type: accuracy value: 67.88 name: 3-shot - type: accuracy value: 69.3 name: 5-shot - task: type: text-generation dataset: name: OpenLLM-Ro/ro_hellaswag type: OpenLLM-Ro/ro_hellaswag metrics: - type: accuracy value: 57.69 name: Average accuracy - type: accuracy value: 56.12 name: 0-shot - type: accuracy value: 57.37 name: 1-shot - type: accuracy value: 57.92 name: 3-shot - type: accuracy value: 58.18 name: 5-shot - type: accuracy value: 58.85 name: 10-shot - task: type: text-generation dataset: name: OpenLLM-Ro/ro_gsm8k type: OpenLLM-Ro/ro_gsm8k metrics: - type: accuracy value: 30.23 name: Average accuracy - type: accuracy value: 29.42 name: 1-shot - type: accuracy value: 30.02 name: 3-shot - type: accuracy value: 31.24 name: 5-shot - task: type: text-generation dataset: name: OpenLLM-Ro/ro_truthfulqa type: OpenLLM-Ro/ro_truthfulqa metrics: - type: accuracy value: 51.34 name: Average accuracy - task: type: text-generation dataset: name: LaRoSeDa_binary type: LaRoSeDa_binary metrics: - type: macro-f1 value: 97.52 name: Average macro-f1 - type: macro-f1 value: 97.43 name: 0-shot - type: macro-f1 value: 96.6 name: 1-shot - type: macro-f1 value: 97.9 name: 3-shot - type: macro-f1 value: 98.13 name: 5-shot - task: type: text-generation dataset: name: LaRoSeDa_multiclass type: LaRoSeDa_multiclass metrics: - type: macro-f1 value: 67.41 name: Average macro-f1 - type: macro-f1 value: 63.77 name: 0-shot - type: macro-f1 value: 68.91 name: 1-shot - type: macro-f1 value: 66.36 name: 3-shot - type: macro-f1 value: 70.61 name: 5-shot - task: type: text-generation dataset: name: LaRoSeDa_binary_finetuned type: LaRoSeDa_binary_finetuned metrics: - type: macro-f1 value: 94.15 name: Average macro-f1 - task: type: text-generation dataset: name: LaRoSeDa_multiclass_finetuned type: LaRoSeDa_multiclass_finetuned metrics: - type: macro-f1 value: 87.13 name: Average macro-f1 - task: type: text-generation dataset: name: WMT_EN-RO type: WMT_EN-RO metrics: - type: bleu value: 24.01 name: Average bleu - type: bleu value: 6.92 name: 0-shot - type: bleu value: 29.33 name: 1-shot - type: bleu value: 29.79 name: 3-shot - type: bleu value: 30.02 name: 5-shot - task: type: text-generation dataset: name: WMT_RO-EN type: WMT_RO-EN metrics: - type: bleu value: 27.36 name: Average bleu - type: bleu value: 4.5 name: 0-shot - type: bleu value: 30.3 name: 1-shot - type: bleu value: 36.96 name: 3-shot - type: bleu value: 37.7 name: 5-shot - task: type: text-generation dataset: name: WMT_EN-RO_finetuned type: WMT_EN-RO_finetuned metrics: - type: bleu value: 26.53 name: Average bleu - task: type: text-generation dataset: name: WMT_RO-EN_finetuned type: WMT_RO-EN_finetuned metrics: - type: bleu value: 40.36 name: Average bleu - task: type: text-generation dataset: name: XQuAD type: XQuAD metrics: - type: exact_match value: 39.43 name: Average exact_match - type: f1 value: 59.5 name: Average f1 - task: type: text-generation dataset: name: XQuAD_finetuned type: XQuAD_finetuned metrics: - type: exact_match value: 44.45 name: Average exact_match - type: f1 value: 59.76 name: Average f1 - task: type: text-generation dataset: name: STS type: STS metrics: - type: spearman value: 77.2 name: Average spearman - type: pearson value: 77.87 name: Average pearson - task: type: text-generation dataset: name: STS_finetuned type: STS_finetuned metrics: - type: spearman value: 85.8 name: Average spearman - type: pearson value: 86.05 name: Average pearson - task: type: text-generation dataset: name: XQuAD_EM type: XQuAD_EM metrics: - type: exact_match value: 4.45 name: 0-shot - type: exact_match value: 48.24 name: 1-shot - type: exact_match value: 52.03 name: 3-shot - type: exact_match value: 53.03 name: 5-shot - task: type: text-generation dataset: name: XQuAD_F1 type: XQuAD_F1 metrics: - type: f1 value: 26.08 name: 0-shot - type: f1 value: 68.4 name: 1-shot - type: f1 value: 71.92 name: 3-shot - type: f1 value: 71.6 name: 5-shot - task: type: text-generation dataset: name: STS_Spearman type: STS_Spearman metrics: - type: spearman value: 77.76 name: 1-shot - type: spearman value: 76.72 name: 3-shot - type: spearman value: 77.12 name: 5-shot - task: type: text-generation dataset: name: STS_Pearson type: STS_Pearson metrics: - type: pearson value: 77.83 name: 1-shot - type: pearson value: 77.64 name: 3-shot - type: pearson value: 78.13 name: 5-shot --- # vladciocan88/RoLlama3-8b-Instruct-2024-06-28-Q8_0-GGUF This model was converted to GGUF format from [`OpenLLM-Ro/RoLlama3-8b-Instruct-2024-06-28`](https://huggingface.co/OpenLLM-Ro/RoLlama3-8b-Instruct-2024-06-28) 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/OpenLLM-Ro/RoLlama3-8b-Instruct-2024-06-28) 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 vladciocan88/RoLlama3-8b-Instruct-2024-06-28-Q8_0-GGUF --hf-file rollama3-8b-instruct-2024-06-28-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo vladciocan88/RoLlama3-8b-Instruct-2024-06-28-Q8_0-GGUF --hf-file rollama3-8b-instruct-2024-06-28-q8_0.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 vladciocan88/RoLlama3-8b-Instruct-2024-06-28-Q8_0-GGUF --hf-file rollama3-8b-instruct-2024-06-28-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo vladciocan88/RoLlama3-8b-Instruct-2024-06-28-Q8_0-GGUF --hf-file rollama3-8b-instruct-2024-06-28-q8_0.gguf -c 2048 ```