Update README.md

README.md

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+
+# Chirp-3b
+
+## Overview
+
+Chirp-3b is a high-performing 3B parameter language model crafted by the Ozone Research team. Fine-tuned from a robust base model, it was trained on 50 million tokens of distilled data from GPT-4o. This compact yet powerful model delivers exceptional results, outperforming expectations on benchmarks like MMLU Pro and IFEval.
+
+Chirp-3b is an open-source effort to push the limits of what small-scale LLMs can achieve, making it a valuable tool for researchers and enthusiasts alike.
+
+## Key Features
+
+- **Parameters**: 3 billion
+- **Training Data**: 50M tokens distilled from GPT-4o
+- **Fine-Tuned From**: [Base model name TBD—update if applicable]
+- **License**: [Specify license, e.g., MIT, Apache 2.0, etc.—update as needed]
+
+## Benchmarks
+
+Chirp-3b excels on rigorous evaluation datasets, showcasing its strength for a 3B model.
+
+### MMLU Pro
+
+| Subject             | Average Accuracy |
+|---------------------|------------------|
+| Biology             | 0.6234           |
+| Business            | 0.5032           |
+| Chemistry           | 0.3701           |
+| Computer Science    | 0.4268           |
+| Economics           | 0.5284           |
+| Engineering         | 0.3013           |
+| Health              | 0.3900           |
+| History             | 0.3885           |
+| Law                 | 0.2252           |
+| Math                | 0.5736           |
+| Other               | 0.4145           |
+| Philosophy          | 0.3687           |
+| Physics             | 0.3995           |
+| Psychology          | 0.5589           |
+| **Overall Average** | **0.4320**       |
+
+- **Improvement**: 9 points above the base model.
+
+### IFEval
+
+- **Score**: 72%
+- **Improvement**: 14% better than the base model.
+
+More benchmarks are in the works and will be shared soon!
+
+## Download
+
+Access Chirp-3b here:  
+https://huggingface.co/ozone-research/Chirp-01
+
+## Usage
+
+### Requirements
+
+- Recommended GPU: 8 GB VRAM Minimum
+
+### Example
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "ozone-research/Chirp-01"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+input_text = "What’s the future of AI?"
+inputs = tokenizer(input_text, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=50)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Future Work
+
+The Ozone AI team is exploring additional models, including 2B and larger variants. Keep an eye out for upcoming releases!
+
+## Feedback
+
+We’re eager for your input! Try Chirp-3b and let us know your thoughts, use cases, or ideas for improvement. Open an issue here or contact us via [contact method—update as needed].
+
+## Acknowledgments
+
+A big thanks to the open-source community for driving projects like this forward. Chirp-3b is our contribution to making AI research more accessible.