91 lines
2.2 KiB
Markdown
91 lines
2.2 KiB
Markdown
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---
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license: other
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license_name: qwen-research
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license_link: https://huggingface.co/Qwen/Qwen2.5-3B/blob/main/LICENSE
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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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- qwen
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- qwen2.5
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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: Qwen/Qwen2.5-3B
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model_name: calme-3.2-baguette-3b
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datasets:
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- MaziyarPanahi/calme-legalkit-v0.2
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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-3.2-baguette-3b
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This model is an advanced iteration of the powerful `Qwen/Qwen2.5-3B`, 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-3.2-baguette-3b-GGUF](https://huggingface.co/MaziyarPanahi/calme-3.2-baguette-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-3.2-baguette-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-3.2-baguette-3b")
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model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/calme-3.2-baguette-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.
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