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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

vLLM

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Qwen/Qwen2-7B-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/Qwen2-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

load from local filepath error

by bdambrosio - opened Jun 6, 2024

Discussion

bdambrosio

Jun 6, 2024

•

edited Jun 7, 2024

The model loads fine, but when I try to load the tokenizer: transformers.from_pretrained( 'local filepath')

File "/home/bruce/.local/lib/python3.10/site-packages/transformers/models/qwen2/tokenization_qwen2.py", line 179, in init
with open(merges_file, encoding="utf-8") as merges_handle:
TypeError: expected str, bytes or os.PathLike object, not NoneType

btw, using transformers 4.41.1

jklj077

Qwen org Jun 7, 2024

I'm not sure what is transformers.from_pretrained( 'local filepath'), but the following should be fine:

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("local filepath/")

Byerose

Oct 26, 2024

same question.
I tried:

model_id = "Qwen2.5-72B-Instruct"
model_path = os.path.join("/disk/mount/models/", model_id)
model_path += "/"
tokenizer = AutoTokenizer.from_pretrained(model_path)

don't work...

  File "main.py", line 90, in <module>
    tokenizer = AutoTokenizer.from_pretrained(model_path)  # for qwen
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/anaconda3/lib/python3.11/site-packages/transformers/models/auto/tokenization_auto.py", line 926, in from_pretrained
    return tokenizer_class_fast.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/anaconda3/lib/python3.11/site-packages/transformers/tokenization_utils_base.py", line 2208, in from_pretrained
    return cls._from_pretrained(
           ^^^^^^^^^^^^^^^^^^^^^
  File "/root/anaconda3/lib/python3.11/site-packages/transformers/tokenization_utils_base.py", line 2246, in _from_pretrained
    slow_tokenizer = (cls.slow_tokenizer_class)._from_pretrained(
                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/anaconda3/lib/python3.11/site-packages/transformers/tokenization_utils_base.py", line 2442, in _from_pretrained
    tokenizer = cls(*init_inputs, **init_kwargs)
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/root/anaconda3/lib/python3.11/site-packages/transformers/models/qwen2/tokenization_qwen2.py", line 172, in __init__
    with open(vocab_file, encoding="utf-8") as vocab_handle:
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: expected str, bytes or os.PathLike object, not NoneType

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