---
base_model: klei1/bleta-meditor-27b
tags:
- text-generation-inference
- transformers
- gemma3
- reasoning
- mathematics
- grpo
license: apache-2.0
language:
- al
inference:
  parameters:
    temperature: 0.7
    top_p: 0.95
    top_k: 64
    max_new_tokens: 512
---

# Gemma 3 27B GRPO Reasoning Model

## Model Description
- **Developed by:** klei1
- **Model type:** Gemma 3 27B fine-tuned with GRPO for reasoning tasks
- **License:** apache-2.0
- **Finetuned from model:** Google's Gemma 3 27B instruction-tuned model
- **Framework:** Hugging Face Transformers

This model is a fine-tuned version of Google's Gemma 3 27B instruction-tuned model, enhanced using Generative Rejection Policy Optimization (GRPO) to improve its reasoning capabilities.

## Capabilities & Training

### Fine-tuning Approach
This model was fine-tuned using GRPO (Generative Rejection Policy Optimization), a reinforcement learning technique that trains models to optimize for specific reward functions. The model was trained to:

1. Follow a specific reasoning format with dedicated sections for workings and solutions
2. Produce correct mathematical solutions
3. Show clear step-by-step reasoning processes

### Special Formatting
The model has been trained to follow a specific reasoning format:
- Working out/reasoning sections are enclosed within `<start_working_out>` and `<end_working_out>` tags
- Final solutions are provided between `<SOLUTION>` and `</SOLUTION>` tags

### Training Configuration
- **Framework:** Hugging Face's TRL library
- **Optimization:** LoRA fine-tuning (r=8, alpha=8)
- **Reward Functions:** Format adherence, answer accuracy, and reasoning quality

## Technical Specifications

### Available Formats
This model is available in two formats:
- Standard adapter format (adapter_model.safetensors)
- GGUF 8-bit quantized format (bleta-meditor-27b-finetune.Q8_0.gguf) for use with llama.cpp

### Gemma 3 Architecture Benefits
- 27B parameters, trained on 14 trillion tokens
- 128K context window (extended from 32K)
- QK normalization (replaced attention softcapping)
- 5 sliding + 1 global attention pattern
- 1024 sliding window attention

## Limitations
- While this model excels at reasoning tasks, particularly mathematical problems, it may still occasionally provide incorrect solutions for complex problems.
- The model's performance might vary depending on problem complexity and wording.
- Like all language models, it may occasionally hallucinate or provide incorrect information outside its training domain.

## Acknowledgments
- Google for developing the Gemma 3 model family
- Hugging Face for their TRL library and GRPO implementation

## Citation
If you use this model in your research, please cite:
```
@misc{klei1_gemma3_grpo,
  author = {klei1},
  title = {Gemma 3 27B GRPO Reasoning Model},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/klei1/gemma-3-27b-grpo}}
}
```