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--- |
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language: |
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- en |
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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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- finetune |
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- chatml |
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base_model: dnhkng/RYS-XLarge |
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datasets: |
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- MaziyarPanahi/truthy-dpo-v0.1-axolotl |
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model_name: calme-2.4-rys-78b |
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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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license: mit |
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--- |
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<img src="./calme-2.webp" alt="Calme-2 Models" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/> |
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# MaziyarPanahi/calme-2.4-rys-78b |
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This model is a fine-tuned version of the `dnhkng/RYS-XLarge`, 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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Coming soon! |
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# 🏆 [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) |
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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-2.4-rys-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-2.4-rys-78b") |
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model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/calme-2.4-rys-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. |
