Instructions to use HyperX-Sen/Qwen-2.5-7B-Reasoning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HyperX-Sen/Qwen-2.5-7B-Reasoning with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="HyperX-Sen/Qwen-2.5-7B-Reasoning")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HyperX-Sen/Qwen-2.5-7B-Reasoning")
model = AutoModelForCausalLM.from_pretrained("HyperX-Sen/Qwen-2.5-7B-Reasoning")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use HyperX-Sen/Qwen-2.5-7B-Reasoning with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HyperX-Sen/Qwen-2.5-7B-Reasoning"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HyperX-Sen/Qwen-2.5-7B-Reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HyperX-Sen/Qwen-2.5-7B-Reasoning

SGLang

How to use HyperX-Sen/Qwen-2.5-7B-Reasoning with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "HyperX-Sen/Qwen-2.5-7B-Reasoning" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HyperX-Sen/Qwen-2.5-7B-Reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "HyperX-Sen/Qwen-2.5-7B-Reasoning" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HyperX-Sen/Qwen-2.5-7B-Reasoning",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use HyperX-Sen/Qwen-2.5-7B-Reasoning with Docker Model Runner:
```
docker model run hf.co/HyperX-Sen/Qwen-2.5-7B-Reasoning
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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
Fine-tuned by: HyperX-Sen
Dataset: GSM8K (Grade School Math 8K)
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

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)