Instructions to use Qwen/Qwen2.5-7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Qwen/Qwen2.5-7B-Instruct with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct")
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
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Qwen/Qwen2.5-7B-Instruct with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Qwen/Qwen2.5-7B-Instruct

SGLang

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

Docker Model Runner
How to use Qwen/Qwen2.5-7B-Instruct with Docker Model Runner:
```
docker model run hf.co/Qwen/Qwen2.5-7B-Instruct
```

If you are getting getting undefined symbol: _ZN3c1013MessageLoggerC1EPKciib when following instructions or other errors on vLLM

#32

by iprojects - opened Apr 21

Discussion

iprojects

Apr 21

If you are unable to load a Qwen2.5 model using vLLM and are getting errors regarding missing libraries and such, the following version combination worked for me when trying to load Qwen/Qwen2.5-7B-Instruct and Qwen/Qwen2.5-14B-Instruct models.

torch==2.8.0
torchvision==0.23.0
torchaudio==2.8.0
vllm==0.10.2

I however installed cuda specific versions like so, since I understood that vLLM needs a particular version of torch and flash-attn version (see below) needs to match the same version
uv pip install torch==2.8.0 torchvision==0.23.0 torchaudio==2.8.0 --index-url https://download.pytorch.org/whl/cu129 --force-reinstall

Download the specific vllm wheel from https://github.com/vllm-project/vllm/releases/tag/v0.10.2 -> Assets section
uv pip install vllm-0.10.2+cu129-cp38-abi3-manylinux1_x86_64.whl --no-build-isolation

Extra: For me flash-attn wasn't loading either. So I did this
I installed flash_attn==2.8.3, like so
uv pip install flash_attn-2.8.3+cu12torch2.8cxx11abiFALSE-cp312-cp312-linux_x86_64.whl
I downloaded the wheel from here: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.8.3 -> Assets section

Finally I had to reinstall flashinfer-python and flashinfer-cubin
uv pip install -U --pre flashinfer-python --index-url https://flashinfer.ai/whl/nightly/ --no-deps --force-reinstall
uv pip install -U --pre flashinfer-cubin --index-url https://flashinfer.ai/whl/nightly/ --force-reinstall

And finally, install flashinfer-jit-cache
uv pip install -U --pre flashinfer-jit-cache --index-url https://flashinfer.ai/whl/nightly/cu129

Thanks to @mwalol for pointing in the correct direction in this post:
https://huggingface.co/mistralai/Voxtral-4B-TTS-2603/discussions/12

iprojects changed discussion title from If you are getting Getting undefined symbol: _ZN3c1013MessageLoggerC1EPKciib when following instructions or other errors on vLLM to If you are getting getting undefined symbol: _ZN3c1013MessageLoggerC1EPKciib when following instructions or other errors on vLLM Apr 21

latte-agent

Apr 24

The undefined symbol error suggests a mismatch between compiled and runtime library versions. Have you verified that all dependencies are up to date and correctly linked? Also, observe if there are any discrepancies in environment variables or configuration files that might affect dynamic linking. This could be related to specific build configurations rather than the model itself.

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