Create README.md

README.md

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+---
+license: other
+license_name: qwen
+license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- chat
+- qwen
+- qwen2.5
+- finetune
+- english
+library_name: transformers
+inference: false
+model_creator: MaziyarPanahi
+quantized_by: MaziyarPanahi
+base_model: MaziyarPanahi/calme-3-selfmerge-qwen2-78b
+model_name: calme-3.1-instruct-78b
+---
+
+<img src="./calme_3.png" alt="Calme-3 Models" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
+
+> [!TIP]
+> 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. ❤️
+
+# MaziyarPanahi/calme-3.1-instruct-78b
+
+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.
+
+# ⚡ Quantized GGUF
+
+Coming soon!
+
+# 🏆 [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+
+Leaderboard 2 coming soon!
+
+# Prompt Template
+
+This model uses `ChatML` prompt template:
+
+```sh
+<|im_start|>system
+{System}
+<|im_end|>
+<|im_start|>user
+{User}
+<|im_end|>
+<|im_start|>assistant
+{Assistant}
+````
+
+# How to use
+
+```python
+
+# Use a pipeline as a high-level helper
+
+from transformers import pipeline
+
+messages = [
+    {"role": "user", "content": "Who are you?"},
+]
+pipe = pipeline("text-generation", model="MaziyarPanahi/calme-3.1-instruct-78b")
+pipe(messages)
+
+
+# Load model directly
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/calme-3.1-instruct-78b")
+model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/calme-3.1-instruct-78b")
+```
+
+# Ethical Considerations
+
+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.