---
license: apache-2.0
base_model: Qwen/Qwen2.5-0.5B-Instruct
tags:
- geology
- earth-science
- qwen
- grpo
- instruction-tuned
language:
- en
pipeline_tag: text-generation
library_name: transformers
---

# Qwen-GRPO-geological-training

GRPO-trained Qwen model specialized for geological questions and analysis

## Model Details

- **Base Model**: Qwen/Qwen2.5-0.5B-Instruct
- **Training Method**: GRPO (Generalized Reward Preference Optimization)
- **Domain**: Geology and Earth Sciences
- **Model Type**: Causal Language Model
- **Architecture**: Transformer-based

## Training Details

This model was trained using GRPO (Generalized Reward Preference Optimization) on geological datasets. The training process included:

- **Reward Functions**: 
  - Geological accuracy reward
  - Format compliance reward
  - Reasoning steps reward
- **System Prompt**: Specialized geological expert system prompt
- **Response Format**: Structured thinking process with solution tags

## Intended Use

This model is designed for:
- Answering geological questions
- Providing educational content about earth sciences
- Assisting with mineral identification
- Explaining geological processes
- Rock and mineral analysis

## Usage Example

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load the model
model_name = "joe-xhedi/Qwen-GRPO-geological-training"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16)

# Geological system prompt
system_prompt = '''You are a geological expert assistant. When answering geological questions, follow this format: 
First, analyze the problem step by step in your thinking process within <|begin_of_thought|> and <|end_of_thought|> tags. 
Then provide your solution within <|begin_of_solution|> and <|end_of_solution|> tags. 
Your thinking process should include geological principles, data analysis, and reasoning. 
Your solution should be clear, accurate, and based on geological expertise.'''

# Example usage
messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": "What type of rock is formed by cooling magma?"}
]

# Generate response
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=300, do_sample=True, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```

## Response Format

The model is trained to respond in a structured format:

```
<|begin_of_thought|>
[Step-by-step geological reasoning and analysis]
<|end_of_thought|>

<|begin_of_solution|>
[Clear, accurate geological solution or explanation]
<|end_of_solution|>
```

## Limitations

- Specialized for geological topics
- May not perform well on general conversational tasks
- Responses are structured and may seem formal
- Based on training data available up to the training cutoff

## Training Data

The model was trained on geological datasets including:
- Mineral identification questions
- Rock formation processes
- Geological principles and concepts
- Earth science educational content

## Ethical Considerations

- This model is designed for educational and research purposes
- Users should verify geological information for professional applications
- The model may have biases present in the training data

## Citation

If you use this model in your research, please cite:

```bibtex
@model{qwen-geological-expert,
  author = {joe-xhedi},
  title = {GRPO-trained Qwen Model for Geological Analysis},
  year = {2025},
  url = {https://huggingface.co/joe-xhedi/Qwen-GRPO-geological-training}
}
```

## Model Card Contact

For questions about this model, please contact the model author through Hugging Face.