Instructions to use Qwen/Qwen2-72B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Qwen/Qwen2-72B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="Qwen/Qwen2-72B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-72B-Instruct")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-72B-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
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Qwen/Qwen2-72B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Qwen/Qwen2-72B-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/Qwen2-72B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Qwen/Qwen2-72B-Instruct

SGLang

How to use Qwen/Qwen2-72B-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/Qwen2-72B-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/Qwen2-72B-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/Qwen2-72B-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/Qwen2-72B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Qwen/Qwen2-72B-Instruct with Docker Model Runner:
```
docker model run hf.co/Qwen/Qwen2-72B-Instruct
```

How to run on multiple gpus?

#25

by Chan-Y - opened Sep 26, 2024

Discussion

Chan-Y

Sep 26, 2024

I have 4 gpus that I want to run Qwen2 VL models.

model_name="Qwen/Qwen2-VL-2B-Instruct"
model = Qwen2VLForConditionalGeneration.from_pretrained(
          model_name, torch_dtype="auto", device_map="auto"
        )
model = nn.DataParallel(model)
processor = AutoProcessor.from_pretrained(model_name)

messages = [
        {
            "role": "user",
            "content": [
                {
                    "type": "image",
                    "image": file
                },
                {
                    "type": "text",
                    "text": """Describe the image"""
                }
            ]
        }
]
text = processor.apply_chat_template(
            messages, tokenize=False, add_generation_prompt=True)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
            text=[text],
            images=image_inputs,
            videos=video_inputs,
            padding=True,
            return_tensors="pt",
        )
with torch.no_grad():
    generated_ids = model.module.generate(**inputs, max_new_tokens=128)

but I always get:

../aten/src/ATen/native/cuda/IndexKernel.cu:92: operator(): block: [35,0,0], thread: [31,0,0] Assertion `-sizes[i] <= index && index < sizes[i] && "index out of bounds"` failed.
ERROR:  CUDA error: device-side assert triggered
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

Traceback (most recent call last):
  File "/home/ubuntu/projects/mistral-qaC/services/VisionService.py", line 104, in ask_vision
    generated_ids = self.model.module.generate(
                    ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/ubuntu/projects/upper/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
    return func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
  File "/home/ubuntu/projects/upper/lib/python3.12/site-packages/transformers/generation/utils.py", line 2015, in generate
    result = self._sample(
             ^^^^^^^^^^^^^
  File "/home/ubuntu/projects/upper/lib/python3.12/site-packages/transformers/generation/utils.py", line 2965, in _sample
    outputs = self(**model_inputs, return_dict=True)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/ubuntu/projects/upper/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1553, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/ubuntu/projects/upper/lib/python3.12/site-packages/torch/nn/modules/module.py", line 1562, in _call_impl
    return forward_call(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/ubuntu/projects/upper/lib/python3.12/site-packages/accelerate/hooks.py", line 169, in new_forward
    output = module._old_forward(*args, **kwargs)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/ubuntu/projects/upper/lib/python3.12/site-packages/transformers/models/qwen2_vl/modeling_qwen2_vl.py", line 1598, in forward
    inputs_embeds[image_mask] = image_embeds
    ~~~~~~~~~~~~~^^^^^^^^^^^^
RuntimeError: CUDA error: device-side assert triggered
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.```
I tried running my python script using `CUDA_LAUNCH_BLOCKING=1 python script.py` but it didnt work either.

my transformers and pytorch versions are:
```bash
transformers==4.45.0.dev0
torch==2.4.1+cu124