Instructions to use gasolsun/Code_Qwen2.5-Coder-3B-Instruct_Qwen2.5-Coder-32B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gasolsun/Code_Qwen2.5-Coder-3B-Instruct_Qwen2.5-Coder-32B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="gasolsun/Code_Qwen2.5-Coder-3B-Instruct_Qwen2.5-Coder-32B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gasolsun/Code_Qwen2.5-Coder-3B-Instruct_Qwen2.5-Coder-32B-Instruct")
model = AutoModelForCausalLM.from_pretrained("gasolsun/Code_Qwen2.5-Coder-3B-Instruct_Qwen2.5-Coder-32B-Instruct", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gasolsun/Code_Qwen2.5-Coder-3B-Instruct_Qwen2.5-Coder-32B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gasolsun/Code_Qwen2.5-Coder-3B-Instruct_Qwen2.5-Coder-32B-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": "gasolsun/Code_Qwen2.5-Coder-3B-Instruct_Qwen2.5-Coder-32B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gasolsun/Code_Qwen2.5-Coder-3B-Instruct_Qwen2.5-Coder-32B-Instruct

SGLang

How to use gasolsun/Code_Qwen2.5-Coder-3B-Instruct_Qwen2.5-Coder-32B-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 "gasolsun/Code_Qwen2.5-Coder-3B-Instruct_Qwen2.5-Coder-32B-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": "gasolsun/Code_Qwen2.5-Coder-3B-Instruct_Qwen2.5-Coder-32B-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 "gasolsun/Code_Qwen2.5-Coder-3B-Instruct_Qwen2.5-Coder-32B-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": "gasolsun/Code_Qwen2.5-Coder-3B-Instruct_Qwen2.5-Coder-32B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use gasolsun/Code_Qwen2.5-Coder-3B-Instruct_Qwen2.5-Coder-32B-Instruct with Docker Model Runner:
```
docker model run hf.co/gasolsun/Code_Qwen2.5-Coder-3B-Instruct_Qwen2.5-Coder-32B-Instruct
```

Add paper and code links

by nielsr HF Staff - opened 18 days ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

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nielsr

18 days ago

This PR adds direct links to the paper Ask to Distill: Learning to Synthesize Data for On-Policy Distillation and to the code repository Ask_to_Distill in the model card content, making it easier for users to find the relevant research and implementation. The existing metadata is already complete and remains unchanged.

Add paper and code links0a06c09b

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