Text Generation
Transformers
Safetensors
English
qwen2
code
codeqwen
chat
qwen
qwen-coder
conversational
text-generation-inference
Instructions to use Qwen/Qwen2.5-Coder-7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Qwen/Qwen2.5-Coder-7B-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Qwen/Qwen2.5-Coder-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-Coder-7B-Instruct") model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-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-Coder-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-Coder-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-Coder-7B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Qwen/Qwen2.5-Coder-7B-Instruct
- SGLang
How to use Qwen/Qwen2.5-Coder-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-Coder-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-Coder-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-Coder-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-Coder-7B-Instruct", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Qwen/Qwen2.5-Coder-7B-Instruct with Docker Model Runner:
docker model run hf.co/Qwen/Qwen2.5-Coder-7B-Instruct
Model Hallucination in Function Call
#22
by princepride - opened
I've encountered an issue while testing function calls with vLLM. Function calls aren't being triggered as expected. After inspecting the response, I noticed that instead of returning <tool_call></tool_call>, it returns <function_call></function_call>:
{'id': 'chatcmpl-aa7d39bb3d084ff5b4d82f5a87ef5951', 'object': 'chat.completion', 'created': 1747139480, 'model': 'Qwen/Qwen2.5-Coder-7B-Instruct', 'choices': [{'index': 0, 'message': {'role': 'assistant', 'reasoning_content': None, 'content': '```xml\n<function_call>\n {"name": "get_weather", "arguments": {"location": "Seoul", "unit": "celsius"}}\n</function_call>\n```', 'tool_calls': []}, 'logprobs': None, 'finish_reason': 'stop', 'stop_reason': None}], 'usage': {'prompt_tokens': 203, 'total_tokens': 241, 'completion_tokens': 38, 'prompt_tokens_details': None}, 'prompt_logprobs': None, 'kv_transfer_params': None}
Here's the command I used:
vllm serve Qwen/Qwen2.5-Coder-7B-Instruct --quantization gptq --download-dir /models --enable-auto-tool-choice --tool-call-parser hermes
And here's my request code:
import requests
import json
url = "http://localhost:8000/v1/chat/completions"
headers = {'Content-Type': 'application/json'}
data = {
"model": "Qwen/Qwen2.5-Coder-7B-Instruct",
"messages": [
{
"role": "system",
"content": "you are helpful ai"
},
{
"role": "user",
"content": "what is the weather in seoul?"
}
],
"stream": False,
"tool_choice": "auto",
"tools": [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "City and state, e.g., 'San Francisco, CA'"
},
"unit": {
"type": "string",
"enum": [
"celsius",
"fahrenheit"
]
}
},
"required": [
"location",
"unit"
]
}
}
}
]
}
try:
response = requests.post(url, headers=headers, data=json.dumps(data))
response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx)
print("Request successful!")
print("Response content:")
print(response.json())
except requests.exceptions.RequestException as e:
print(f"Request failed: {e}")
if response is not None:
print(f"Response status code: {response.status_code}")
print(f"Response content: {response.text}")