Instructions to use HenryShan/Qwen2.5-Math-7B-DPO-10K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HenryShan/Qwen2.5-Math-7B-DPO-10K with Transformers:

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

pipe = pipeline("text-generation", model="HenryShan/Qwen2.5-Math-7B-DPO-10K")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HenryShan/Qwen2.5-Math-7B-DPO-10K")
model = AutoModelForCausalLM.from_pretrained("HenryShan/Qwen2.5-Math-7B-DPO-10K")
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]:]))

MLX

How to use HenryShan/Qwen2.5-Math-7B-DPO-10K with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("HenryShan/Qwen2.5-Math-7B-DPO-10K")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

vLLM

How to use HenryShan/Qwen2.5-Math-7B-DPO-10K with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HenryShan/Qwen2.5-Math-7B-DPO-10K"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HenryShan/Qwen2.5-Math-7B-DPO-10K",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/HenryShan/Qwen2.5-Math-7B-DPO-10K

SGLang

How to use HenryShan/Qwen2.5-Math-7B-DPO-10K 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 "HenryShan/Qwen2.5-Math-7B-DPO-10K" \
    --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": "HenryShan/Qwen2.5-Math-7B-DPO-10K",
		"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 "HenryShan/Qwen2.5-Math-7B-DPO-10K" \
        --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": "HenryShan/Qwen2.5-Math-7B-DPO-10K",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

How to use HenryShan/Qwen2.5-Math-7B-DPO-10K with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "HenryShan/Qwen2.5-Math-7B-DPO-10K"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "HenryShan/Qwen2.5-Math-7B-DPO-10K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use HenryShan/Qwen2.5-Math-7B-DPO-10K with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "HenryShan/Qwen2.5-Math-7B-DPO-10K"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default HenryShan/Qwen2.5-Math-7B-DPO-10K

Run Hermes

hermes

MLX LM

How to use HenryShan/Qwen2.5-Math-7B-DPO-10K with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "HenryShan/Qwen2.5-Math-7B-DPO-10K"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "HenryShan/Qwen2.5-Math-7B-DPO-10K"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "HenryShan/Qwen2.5-Math-7B-DPO-10K",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use HenryShan/Qwen2.5-Math-7B-DPO-10K with Docker Model Runner:
```
docker model run hf.co/HenryShan/Qwen2.5-Math-7B-DPO-10K
```

Qwen2.5-Math-7B • Fine-tuned for Mathematical Reasoning

$Base Model$ $Training Dataset$

Qwen2.5-Math-7B is a fine-tuned version of Qwen2.5-Math-7B, specifically optimized for mathematical reasoning through Direct Preference Optimization (DPO) on the Math-Step-DPO-10K dataset. This model specializes in generating step-by-step solutions to mathematical problems across various domains including algebra, calculus, and geometry.

🧮 Training Details

Base Model: Qwen/Qwen2.5-Math-7B
Fine-tuning Method: LoRA (Low-Rank Adaptation)
Framework: mlx_lm.lora (Apple MLX)
Hardware: Apple Silicon Mac
Dataset: Math-Step-DPO-10K
Objective: Enhance step-by-step mathematical reasoning through parameter-efficient adaptation
Parameters:
- optimizer: adamw
- Training iterations: 50
- Learning rate: 1e-5
LoRA Configuration:
- Rank: 8
- Alpha (scale): 10
- Dropout: 0

💻 Usage

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("HenryShan/Qwen2.5-Math-7B-DPO-10K")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

License

Qwen2.5-Math-7B-DPO-10K is licensed under the Apache license 2.0. It is finetuned from Qwen2.5-Math-7B, under Apache 2.0.

✍️ Citation

@misc{haotian_shan_2025,
    author       = { Haotian Shan },
    title        = { Qwen2.5-Math-7B-DPO-10K (Revision e4f4bb3) },
    year         = 2025,
    url          = { https://huggingface.co/HenryShan/Qwen2.5-Math-7B-DPO-10K },
    doi          = { 10.57967/hf/5631 },
    publisher    = { Hugging Face }
}

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Safetensors

Model size

8B params

Tensor type

BF16

MLX

Hardware compatibility

Quantized

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