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

Libraries

How to use m-a-p/Qwen2-Instruct-7B-COIG-P with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("m-a-p/Qwen2-Instruct-7B-COIG-P")
model = AutoModelForMultimodalLM.from_pretrained("m-a-p/Qwen2-Instruct-7B-COIG-P")
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 Settings

vLLM

How to use m-a-p/Qwen2-Instruct-7B-COIG-P with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/m-a-p/Qwen2-Instruct-7B-COIG-P

SGLang

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

Docker Model Runner
How to use m-a-p/Qwen2-Instruct-7B-COIG-P with Docker Model Runner:
```
docker model run hf.co/m-a-p/Qwen2-Instruct-7B-COIG-P
```

Improve and complete model card

by nielsr HF Staff - opened Apr 9, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+60

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nielsr

Apr 9, 2025

This PR improves the model card by adding missing metadata (pipeline tag, library name, license, tags), filling in missing information in the Model Details section, and restructuring the card for improved clarity and readability. The license information and BibTeX citation were taken from the Github README.

I've also added a brief description of the model and its intended use, along with placeholders for important missing information. The usage example has been simplified for easier understanding. Please review and fill in the placeholder information.

Improve and complete model card947da31e

SiweiWu changed pull request status to merged Apr 9, 2025

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