Instructions to use TOFU-SFT/Qwen2.5-Math-7B-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TOFU-SFT/Qwen2.5-Math-7B-4bit with Transformers:

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

pipe = pipeline("text-generation", model="TOFU-SFT/Qwen2.5-Math-7B-4bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TOFU-SFT/Qwen2.5-Math-7B-4bit")
model = AutoModelForCausalLM.from_pretrained("TOFU-SFT/Qwen2.5-Math-7B-4bit")
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 TOFU-SFT/Qwen2.5-Math-7B-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TOFU-SFT/Qwen2.5-Math-7B-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TOFU-SFT/Qwen2.5-Math-7B-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TOFU-SFT/Qwen2.5-Math-7B-4bit

SGLang

How to use TOFU-SFT/Qwen2.5-Math-7B-4bit 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 "TOFU-SFT/Qwen2.5-Math-7B-4bit" \
    --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": "TOFU-SFT/Qwen2.5-Math-7B-4bit",
		"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 "TOFU-SFT/Qwen2.5-Math-7B-4bit" \
        --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": "TOFU-SFT/Qwen2.5-Math-7B-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TOFU-SFT/Qwen2.5-Math-7B-4bit with Docker Model Runner:
```
docker model run hf.co/TOFU-SFT/Qwen2.5-Math-7B-4bit
```

powidla commited on 23 days ago

Commit

2fa050c

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1 Parent(s): 5a1be00

Update README.md

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@@ -8,20 +8,10 @@ license: apache-2.0
 license_link: https://huggingface.co/Qwen/Qwen2.5-Math-7B/blob/main/LICENSE
 ---
-# Model Card for Model ID
-**Qwen2.5-Math-7B** is a base model typically used for completion and few-shot inference, serving as a better starting point for fine-tuning.
-## Model Details
-### Model Description
-For more details, please refer to [blog post](https://qwenlm.github.io/blog/qwen2.5-math/) and [GitHub repo](https://github.com/QwenLM/Qwen2.5-Math).
 - **Developed by:** Qwen
-- **Language(s) (NLP):** English
 - **License:** Apache 2.0
 ## Citation

 license_link: https://huggingface.co/Qwen/Qwen2.5-Math-7B/blob/main/LICENSE
 ---
+## Model Description
 - **Developed by:** Qwen
+- **Model type:** Causal Language Models
 - **License:** Apache 2.0
 ## Citation