---
license: apache-2.0
base_model: Qwen/Qwen2.5-7B
tags:
- finetuned
- qwen2
- text-generation
- gguf
datasets:
- custom
language:
- en
---

# Qwen2.5-7B Finetuned on Argus Dataset

This model is a finetuned version of Qwen2.5-7B using LoRA with rank 128.

## Training Details
- **Base Model**: Qwen/Qwen2.5-7B
- **Training Method**: LoRA (rank=128, alpha=256)
- **Dataset**: 27,997 text samples
- **Epochs**: 2 (best checkpoint from epoch 1)
- **Batch Size**: 16 (effective)
- **Learning Rate**: 5e-5
- **Hardware**: A100 GPU

## Training Results
- **Epoch 1**: Training Loss: 1.301, Validation Loss: 1.589 (best)
- **Epoch 2**: Training Loss: 1.699, Validation Loss: 1.826

## Available Formats
- **PyTorch**: Original model weights
- **GGUF**: Multiple quantization levels available
  - Q8_0: Highest quality (7.5GB)
  - Q6_K: Very high quality (5.5GB)
  - Q5_K_M: High quality (4.8GB)
  - Q4_K_M: Good quality (3.8GB)
  - Q4_0: Acceptable quality (3.5GB)

## Usage

### With Transformers
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("UdayGattu23/qwen2.5-7b-finetuned-argus")
tokenizer = AutoTokenizer.from_pretrained("UdayGattu23/qwen2.5-7b-finetuned-argus")
```

### With llama.cpp (GGUF)
```bash
./main -m qwen2.5-7b-finetuned-Q4_K_M.gguf -p "Your prompt here"
```

## License
Apache 2.0