---
library_name: transformers
tags:
- transformers
- text-generation-inference
- text-generation
- reasoning
- r1-reasoning
- fine-tuned
license: mit
datasets:
- openai/gsm8k
language:
- en
base_model:
- Qwen/Qwen2.5-7B-Instruct
pipeline_tag: text-generation
---

# **Qwen-2.5-7B-Reasoning (Fine-Tuned by HyperX-Sen)**

## 🚀 **Model Overview**
This model is a fine-tuned version of **Qwen/Qwen2.5-7B-Instruct**, specifically optimized for **advanced reasoning tasks**. Fine-tuned on the **OpenAI GSM8K dataset**, it significantly enhances multi-step reasoning and problem-solving capabilities.

## 🔧 **Fine-Tuning Details**
- **Base Model:** [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)  
- **Fine-tuned by:** HyperX-Sen  
- **Dataset:** [GSM8K (Grade School Math 8K)](https://huggingface.co/datasets/openai/gsm8k)  
- **Hardware:** 2× Tesla T4 GPUs
- **Objective:** Improve complex reasoning and logical deduction  

## 📈 **Performance Improvements**
Through fine-tuning on **GSM8K**, the model has improved in:
- **Mathematical reasoning**
- **Step-by-step logical deduction**
- **Commonsense reasoning**
- **Word problem-solving**  

This makes it ideal for applications requiring **high-level reasoning**, such as **AI tutoring, research assistance, and problem-solving AI agents**.

## 🛠 **How to Use**
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "HyperX-Sen/Qwen-2.5-7B-Reasoning"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)

SYSTEM_PROMPT = """
Respond in the following format:
<reasoning>
...
</reasoning>
<answer>
...
</answer>
"""

# Define the conversation
messages = [
    {"role": "system", "content": f"{SYSTEM_PROMPT}"},
    {"role": "user", "content": "What are the potential impacts of artificial intelligence on employment?"}
]

# Format the chat input
input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

# Tokenize the formatted input
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)

# Generate the response
output = model.generate(**inputs, max_length=512, do_sample=True, temperature=0.7)

# Decode and display the response
response = tokenizer.decode(output[0], skip_special_tokens=True)
print(response)
```

## 🙌 **Acknowledgments**
A huge thanks to **Qwen** for providing the powerful **Qwen2.5-7B-Instruct** model, which served as the base for this fine-tuned version.