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---
license: llama3.2
datasets:
- open-thoughts/OpenThoughts-114k
- FreedomIntelligence/medical-o1-verifiable-problem
- open-r1/OpenR1-Math-220k
base_model:
- meta-llama/Llama-3.2-3B-Instruct
---
# mkurman/Llama-3.2-MedIT-3B-R1
**Important Notice:**
This model is provided strictly for research purposes and is not intended for production use. It should not be considered a validated source of medical or professional advice. Use only in controlled experimental settings.
---
## Model Overview
mkurman/Llama-3.2-MedIT-3B-R1 is a fine-tuned variant of meta-llama/Llama-3.2-3B-Instruct, adapted specifically for exploring natural language understanding and reasoning. This model leverages a multi-stage training approach, combining Blurred Thoughts Supervised Fine-Tuning (BT-SFT) and Group Relative Policy Optimization (GRPO) with an LLM evaluator to enhance its performance on specialized tasks.
---
## Training Procedure
The model was developed through the following sequential steps:
1. **Initial Blurred Thoughts Supervised Fine-Tuning (BT-SFT):**
- **Base Model:** meta-llama/Llama-3.2-3B-Instruct
- **Parameters:** 2000 steps, batch size 2, accumulation iterations 16, learning rate 1e-6
- **Dataset:** open-thoughts/OpenThoughts-114k
- **Details:** For further information on BT-SFT, see the [detailed post](https://huggingface.co/posts/mkurman/496852395740108) and the [GitHub repository](https://github.com/mkurman/blurred-thoughts-SFT).
2. **Group Relative Policy Optimization (GRPO) Stage 1:**
- **Dataset:** FreedomIntelligence/medical-o1-verifiable-problem
- **Training:** 200 steps
- **LLM Evaluator** mkurman/Qwen2.5-14B-DeepSeek-R1-1M
- **Details:** For further information on GRPO with LLM evaluators, see the [GitHub repository](https://github.com/mkurman/grpo-llm-evaluator).
3. **Group Relative Policy Optimization (GRPO) Stage 2:**
- **Dataset:** open-r1/OpenR1-Math-220k
- **Training:** 200 steps
- **LLM Evaluator** deepseek/deepseek-r1-distill-qwen-14b (OpenRouterAI)
---
## Datasets Utilized
- **open-thoughts/OpenThoughts-114k:**
A dataset consisting of open-ended thoughts that supports diverse conversational contexts during the initial supervised fine-tuning.
- **FreedomIntelligence/medical-o1-verifiable-problem:**
A dataset curated for enhancing the model's capabilities in addressing verifiable medical problems.
- **open-r1/OpenR1-Math-220k:**
A dataset designed to improve the model's reasoning and problem-solving skills in mathematical contexts.
---
## Intended Use
- **Research and Experimental Applications:**
This model is optimized for academic research and exploratory projects. It is ideal for investigating advanced fine-tuning methods and evaluating performance on task-oriented conversational scenarios.
- **Controlled Environments:**
Users should deploy this model only within controlled experimental frameworks where rigorous evaluation and proper safety guardrails are in place.
---
## Limitations and Ethical Considerations
- **Not for Clinical or Production Use:**
The model’s outputs have not been validated for clinical accuracy or professional decision-making. It must not be used as a primary source for medical, legal, or safety-critical information.
- **Safety and Guardrails:**
All users must implement appropriate safety measures and validation protocols. The model may produce biased or inaccurate results and should be used with caution.
- **Experimental Nature:**
Given its research-oriented design, the model’s performance can vary widely based on input and context. It is essential to perform thorough testing and validation before drawing any conclusions from its outputs.
---
## License
This model is released under the Llama 3.2 license. Users must adhere to the terms specified in the license when utilizing this model.
---
## Final Notice
All outputs from **mkurman/Llama-3.2-MedIT-3B-R1** are intended solely for research purposes. This model is not a comprehensive knowledge source and should not be used as a substitute for professional advice or decision-making. Ensure that all necessary guardrails and safety protocols are in place when conducting any experiments with this model. |