README.md

---
license: apache-2.0
tags:
- fine-tuned
- qwen2.5c
- instruct
- causal-lm
---

# Qwen2.5-7b-instruct-sft

This is a fine-tuned version of the **Qwen2.5-7B-Instruct** model for **instruction-following tasks**. It was fine-tuned using the `SFTTrainer` from the `trl` library on the **OpenAssistant Guanaco** dataset.

## Model Details

### **Base Model**
- **Model**: [Qwen2.5-7B-Instruct](https://huggingface.co./Qwen/Qwen2.5-7B-Instruct)
- **Architecture**: Transformer-based causal language model
- **License**: Apache 2.0

### **Fine-Tuning Details**
- **Dataset**: OpenAssistant Guanaco
- **Training Epochs**: 1
- **Batch Size**: 2
- **Gradient Accumulation Steps**: 16
- **Learning Rate**: 1e-5
- **Optimizer**: Paged AdamW 8-bit
- **Mixed Precision**: `fp16` (if `bf16` is not supported) or `bf16`
- **Max Sequence Length**: 512 tokens

### **Training Hardware**
- **GPU**: NVIDIA A100 (or your specific GPU)
- **Training Time**: X hours (optional)

## Usage

You can use this model with the Hugging Face `transformers` library:

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model = AutoModelForCausalLM.from_pretrained("Ameer-linnk/qwen2.5-7b-instruct-sft")
tokenizer = AutoTokenizer.from_pretrained("Ameer-linnk/qwen2.5-7b-instruct-sft")

# Prepare input
input_text = "What is the capital of France?"
inputs = tokenizer(input_text, return_tensors="pt")

# Generate output
outputs = model.generate(**inputs)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response) 
Example Output
Input:
What is the capital of France?

Output:
The capital of France is Paris.