Instructions to use Elliott/Qwen2.5-Math-7B-16k-think with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Elliott/Qwen2.5-Math-7B-16k-think with Transformers:

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

pipe = pipeline("text-generation", model="Elliott/Qwen2.5-Math-7B-16k-think")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Elliott/Qwen2.5-Math-7B-16k-think")
model = AutoModelForCausalLM.from_pretrained("Elliott/Qwen2.5-Math-7B-16k-think")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Elliott/Qwen2.5-Math-7B-16k-think with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Elliott/Qwen2.5-Math-7B-16k-think"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Elliott/Qwen2.5-Math-7B-16k-think",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Elliott/Qwen2.5-Math-7B-16k-think

SGLang

How to use Elliott/Qwen2.5-Math-7B-16k-think 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 "Elliott/Qwen2.5-Math-7B-16k-think" \
    --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": "Elliott/Qwen2.5-Math-7B-16k-think",
		"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 "Elliott/Qwen2.5-Math-7B-16k-think" \
        --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": "Elliott/Qwen2.5-Math-7B-16k-think",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Elliott/Qwen2.5-Math-7B-16k-think with Docker Model Runner:
```
docker model run hf.co/Elliott/Qwen2.5-Math-7B-16k-think
```

nielsr HF Staff commited on Apr 23, 2025

Commit

cdc752a

verified ·

1 Parent(s): a546ca4

Add library name, pipeline tag, link to Github

Browse files

This PR improves the model card by adding the `library_name` and `pipeline_tag`, and links to the Github repository. This ensures:
- The model can be easily found when searching by pipeline tag (https://huggingface.co/models?pipeline_tag=text-generation&sort=trending).
- The "Use in Transformers" widget appears on the model page.

Files changed (1) hide show

README.md +7 -2

README.md CHANGED Viewed

@@ -1,9 +1,14 @@
 ---
 license: mit
 ---
-The base Qwen2.5-Math-7B model used by LUFFY.
 We change to rope_theta from 10000 to 40000 and extend the context window to 16k.
-Also, we modify the chat_template for the system prompt and add <think>.
 # Citation
 If you find our model, data, or evaluation code useful, please kindly cite our paper:

 ---
 license: mit
+library_name: transformers
+pipeline_tag: text-generation
 ---
+The base Qwen2.5-Math-7B model used by LUFFY, described in [Learning to Reason under Off-Policy Guidance](https://huggingface.co/papers/2504.14945).
 We change to rope_theta from 10000 to 40000 and extend the context window to 16k.
+Also, we modify the chat_template for the system prompt and add <think>.
+Github: https://github.com/ElliottYan/LUFFY
 # Citation
 If you find our model, data, or evaluation code useful, please kindly cite our paper: