Create README.md

README.md

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+---
+base_model:
+- arcee-ai/Arcee-Maestro-7B-Preview
+library_name: transformers
+license: apache-2.0
+---
+
+**Arcee-Maestro-7B-Preview (7B)** is Arcee's first reasoning model trained with reinforment learning. It is based on the Qwen2.5-7B DeepSeek-R1 distillation **DeepSeek-R1-Distill-Qwen-7B** with further GPRO training. Though this is just a preview of our upcoming work, it already shows promising improvements to mathematical and coding abilities across a range of tasks.
+
+### Model Details
+
+- Architecture Base: DeepSeek-R1-Distill-Qwen-7B (Qwen2.5-7B)
+- Parameter Count: 7B
+- Reinforcement Learning: GRPO with 450,000 **verified** math problems with some coding examples
+- License: [Apache-2.0](https://huggingface.co/arcee-ai/Arcee-Maestro-7B-Preview#license)
+
+### Intended Use Cases
+
+- Advanced reasoning
+- Mathematics
+- Coding
+
+### Evaluations
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/6435718aaaef013d1aec3b8b/DlSBEmCFS7yjJi2kOGuLa.png)
+
+Arcee Maestro 7B preview shows great gains in mathematics and coding, surpassing O1 preview in many metrics.
+
+### How to use
+
+Below is a sample code snippet using `transformers`:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_name = "arcee-ai/Arcee-Maestro-7B-Preview"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+prompt = "Provide a concise summary of quantum entanglement."
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=150)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+### Training & Fine-Tuning
+
+- **Initial Training**: Began with DeepSeek-R1-Distill-Qwen-7B
+- **GRPO**:
+  - Trained on 450,000 verified math problems
+  - Additional bootstrapped coding examples
+
+### Performance
+
+Arcee-Maestro-7B-Preview shows strong performance in mathematics as well as coding, competing against even O1 preview, a model far surprassing its size.
+
+### Limitations
+
+- **Context Length:** 128k Tokens (may vary depending on the final tokenizer settings and system resources).
+- **Knowledge Cut-off:** Training data may not reflect the latest events or developments beyond June 2024.
+
+### Ethical Considerations
+- **Content Generation Risks:** Like any language model, Arcee-Maestro-7B-Preview can generate potentially harmful or biased content if prompted in certain ways.
+
+### License
+**Arcee-Maestro-7B-Preview (7B)** is released under the [Apache-2.0 License](https://www.apache.org/licenses/LICENSE-2.0). You are free to use, modify, and distribute this model in both commercial and non-commercial applications, subject to the terms and conditions of the license.
+
+If you have questions or would like to share your experiences using Arcee-Maestro-7B-Preview (7B), please connect with us on social media. We’re excited to see what you build—and how this model helps you innovate!