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--- |
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license: other |
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license_name: qwen |
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license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE |
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language: |
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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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- english |
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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: MaziyarPanahi/calme-3-selfmerge-qwen2-78b |
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model_name: calme-3.1-instruct-78b |
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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-3.1-instruct-78b |
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This model is an advanced iteration of the powerful `Qwen/Qwen2.5-72B`, specifically fine-tuned to enhance its capabilities in generic domains. The `Qwen2.5-72B` base model was merged with itself to create a larger model. After that, the model was fine-tuned on a custom datasets. |
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# ⚡ Quantized GGUF |
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Coming soon! |
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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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```sh |
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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-3.1-instruct-78b") |
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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-3.1-instruct-78b") |
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model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/calme-3.1-instruct-78b") |
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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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