Create README.md

README.md

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+---
+language:
+- en
+library_name: transformers
+tags:
+- chat
+- qwen
+- qwen2
+- finetune
+- chatml
+base_model: dnhkng/RYS-XLarge
+datasets:
+- MaziyarPanahi/truthy-dpo-v0.1-axolotl
+model_name: calme-2.1-rys-78b
+pipeline_tag: text-generation
+inference: false
+model_creator: MaziyarPanahi
+quantized_by: MaziyarPanahi
+---
+
+<img src="./calme-2.webp" alt="Calme-2 Models" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
+
+# MaziyarPanahi/calme-2.1-rys-78b
+
+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.
+
+## Use Cases
+
+This model is suitable for a wide range of applications, including but not limited to:
+
+- Advanced question-answering systems
+- Intelligent chatbots and virtual assistants
+- Content generation and summarization
+- Code generation and analysis
+- Complex problem-solving and decision support
+
+# ⚡ Quantized GGUF
+
+Coming soon!
+
+
+# 🏆 [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
+
+Coming soon!
+
+# Prompt Template
+
+This model uses `ChatML` prompt template:
+
+```
+<|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-2.1-rys-78b")
+pipe(messages)
+
+
+# Load model directly
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/calme-2.1-rys-78b")
+model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/calme-2.1-rys-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.