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--- |
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language: |
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- fr |
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- en |
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pipeline_tag: text-generation |
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tags: |
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- chat |
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- llama |
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- llama3 |
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- finetune |
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- french |
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- legal |
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- loi |
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library_name: transformers |
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inference: false |
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model_creator: MaziyarPanahi |
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quantized_by: MaziyarPanahi |
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base_model: meta-llama/Llama-3.2-3B |
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model_name: calme-3.3-llamaloi-3b |
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datasets: |
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- MaziyarPanahi/calme-legalkit-v0.2 |
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license: llama3.2 |
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--- |
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<img src="./calme_3.png" alt="Calme-3 Models" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> |
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> [!TIP] |
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> This is avery small model, so it might not perform well for some prompts and may be sensitive to hyper parameters. I would appreciate any feedback to see if I can fix any issues in the next iteration. ❤️ |
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# MaziyarPanahi/calme-3.3-llamaloi-3b |
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This model is an advanced iteration of the powerful `meta-llama/Llama-3.2-3B`, specifically fine-tuned to enhance its capabilities in French Legal domain. |
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# ⚡ Quantized GGUF |
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All GGUF models are available here: [MaziyarPanahi/calme-3.3-llamaloi-3b-GGUF](https://huggingface.co/MaziyarPanahi/calme-3.3-llamaloi-3b-GGUF) |
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# 🏆 [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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Leaderboard 2 coming soon! |
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# Prompt Template |
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This model uses `ChatML` prompt template: |
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``` |
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<|im_start|>system |
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{System} |
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<|im_end|> |
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<|im_start|>user |
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{User} |
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<|im_end|> |
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<|im_start|>assistant |
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{Assistant} |
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```` |
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# How to use |
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```python |
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# Use a pipeline as a high-level helper |
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from transformers import pipeline |
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messages = [ |
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{"role": "user", "content": "Who are you?"}, |
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] |
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pipe = pipeline("text-generation", model="MaziyarPanahi/calme-3.3-llamaloi-3b") |
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pipe(messages) |
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# Load model directly |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/calme-3.3-llamaloi-3b") |
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model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/calme-3.3-llamaloi-3b") |
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``` |
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# Ethical Considerations |
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As with any large language model, users should be aware of potential biases and limitations. We recommend implementing appropriate safeguards and human oversight when deploying this model in production environments. |
