Instructions to use Qwen/Qwen2-0.5B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Qwen/Qwen2-0.5B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="Qwen/Qwen2-0.5B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B-Instruct")
model = AutoModelForMultimodalLM.from_pretrained("Qwen/Qwen2-0.5B-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/Qwen2-0.5B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Qwen/Qwen2-0.5B-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/Qwen2-0.5B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Qwen/Qwen2-0.5B-Instruct

SGLang

How to use Qwen/Qwen2-0.5B-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/Qwen2-0.5B-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/Qwen2-0.5B-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/Qwen2-0.5B-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/Qwen2-0.5B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Qwen/Qwen2-0.5B-Instruct with Docker Model Runner:
```
docker model run hf.co/Qwen/Qwen2-0.5B-Instruct
```

yangapku commited on Jun 6, 2024

Commit

197b20c

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1 Parent(s): bf61048

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@@ -69,6 +69,18 @@ generated_ids = [
 response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 ```
 ## Citation
 If you find our work helpful, feel free to give us a cite.

 response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 ```
+## Evaluation
+We briefly compare Qwen2-0.5B-Instruct with Qwen1.5-0.5B-Chat. The results are as follows:
+| Datasets | Qwen1.5-0.5B-Chat | **Qwen2-0.5B-Instruct** | Qwen1.5-1.8B-Chat | **Qwen2-1.5B-Instruct** |
+| :--- | :---: | :---: | :---: | :---: |
+| MMLU | 35.0 | **37.9** | 43.7 | **52.4** |
+| HumanEval | 9.1 | **17.1** | 25.0 | **37.8** |
+| GSM8K | 11.3 | **40.1** | 35.3 | **61.6** |
+| C-Eval | 37.2 | **45.2** | 55.3 | **63.8** |
+| IFEval (Prompt Strict-Acc.) | 14.6 | **20.0** | 16.8 | **29.0** |
 ## Citation
 If you find our work helpful, feel free to give us a cite.