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  1. LICENSE.txt +201 -0
  2. README.md +36 -3
  3. config.json +29 -0
  4. generation_config.json +7 -0
  5. hash_record_sha256.json +61 -0
  6. openvino_detokenizer.bin +3 -0
  7. openvino_detokenizer.xml +97 -0
  8. openvino_model.bin +3 -0
  9. openvino_model.xml +0 -0
  10. openvino_tokenizer.bin +3 -0
  11. openvino_tokenizer.xml +231 -0
  12. special_tokens_map.json +23 -0
  13. tokenizer.json +0 -0
  14. tokenizer.model +3 -0
  15. tokenizer_config.json +41 -0
LICENSE.txt ADDED
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README.md CHANGED
@@ -1,3 +1,36 @@
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- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ license: apache-2.0
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+ inference: false
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+ ---
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+
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+ # bling-tiny-llama-ov
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+
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+ <!-- Provide a quick summary of what the model is/does. -->
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+
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+ **bling-tiny-llama-ov** is an OpenVino int4 quantized version of BLING Tiny-Llama 1B, providing a very fast, very small inference implementation, optimized for AI PCs using Intel GPU, CPU and NPU.
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+
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+ [**bling-tiny-llama**](https://huggingface.co/llmware/bling-tiny-llama-v0) is a fact-based question-answering model, optimized for complex business documents.
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+
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+ Get started right away with [OpenVino](https://github.com/openvinotoolkit/openvino)
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+
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+ Looking for AI PC solutions and demos, contact us at [llmware](https://www.llmware.ai)
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+
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+
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+ ### Model Description
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+
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+ - **Developed by:** llmware
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+ - **Model type:** tinyllama
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+ - **Parameters:** 1.1 billion
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+ - **Model Parent:** llmware/bling-tiny-llama-v0
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+ - **Language(s) (NLP):** English
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+ - **License:** Apache 2.0
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+ - **Uses:** Fact-based question-answering
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+ - **RAG Benchmark Accuracy Score:** 86.5
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+ - **Quantization:** int4
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+
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+
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+ ## Model Card Contact
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+
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+ [llmware on hf](https://www.huggingface.co/llmware)
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+
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+ [llmware website](https://www.llmware.ai)
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+ {
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+ "_name_or_path": "llmware/slim-summary-tiny",
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+ "architectures": [
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+ "LlamaForCausalLM"
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+ ],
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+ "attention_bias": false,
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+ "attention_dropout": 0.0,
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+ "bos_token_id": 1,
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+ "eos_token_id": 2,
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+ "hidden_act": "silu",
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+ "hidden_size": 2048,
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+ "pretraining_tp": 1,
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+ "rms_norm_eps": 1e-05,
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+ "rope_scaling": null,
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+ "rope_theta": 10000.0,
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+ "tie_word_embeddings": false,
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+ "trained": "custom training",
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+ "transformers_version": "4.41.2",
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+ "use_cache": true,
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+ "vocab_size": 32000
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+ }
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