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--- |
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language: |
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- en |
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license: other |
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library_name: transformers |
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tags: |
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- chat |
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- qwen |
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- qwen2 |
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- calme |
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- calme2 |
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- finetune |
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- chatml |
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base_model: Qwen/Qwen2-72B |
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license_name: tongyi-qianwen |
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license_link: https://huggingface.co/Qwen/Qwen2-72B/blob/main/LICENSE |
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pipeline_tag: text-generation |
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inference: false |
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model_creator: MaziyarPanahi |
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quantized_by: MaziyarPanahi |
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--- |
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<img src="./calme-2.webp" alt="Qwen2 fine-tune" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> |
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# MaziyarPanahi/calme-2.3-qwen2-72b |
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This model is a fine-tuned version of the powerful `Qwen/Qwen2-72B-Instruct`, pushing the boundaries of natural language understanding and generation even further. My goal was to create a versatile and robust model that excels across a wide range of benchmarks and real-world applications. |
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## Use Cases |
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This model is suitable for a wide range of applications, including but not limited to: |
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- Advanced question-answering systems |
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- Intelligent chatbots and virtual assistants |
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- Content generation and summarization |
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- Code generation and analysis |
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- Complex problem-solving and decision support |
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# ⚡ Quantized GGUF |
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All GGUF models are available here: [MaziyarPanahi/calme-2.3-qwen2-72b-GGUF](https://huggingface.co/MaziyarPanahi/calme-2.3-qwen2-72b-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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| Tasks |Version|Filter|n-shot|Metric|Value | |Stderr| |
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|--------------|------:|------|-----:|------|-----:|---|-----:| |
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|truthfulqa_mc2| 2|none | 0|acc |0.6761|± |0.0148| |
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| Tasks |Version|Filter|n-shot|Metric|Value | |Stderr| |
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|----------|------:|------|-----:|------|-----:|---|-----:| |
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|winogrande| 1|none | 5|acc |0.8248|± |0.0107| |
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| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr| |
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|-------------|------:|------|-----:|--------|-----:|---|-----:| |
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|arc_challenge| 1|none | 25|acc |0.6852|± |0.0136| |
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| | |none | 25|acc_norm|0.7184|± |0.0131| |
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|Tasks|Version| Filter |n-shot| Metric |Value | |Stderr| |
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|-----|------:|----------------|-----:|-----------|-----:|---|-----:| |
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|gsm8k| 3|strict-match | 5|exact_match|0.8582|± |0.0096| |
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| | |flexible-extract| 5|exact_match|0.8893|± |0.0086| |
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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-2.3-qwen2-72b") |
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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-2.3-qwen2-72b") |
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model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/calme-2.3-qwen2-72b") |
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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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