Instructions to use Qwen/Qwen2.5-3B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Qwen/Qwen2.5-3B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="Qwen/Qwen2.5-3B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-3B-Instruct")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-3B-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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Qwen/Qwen2.5-3B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Qwen/Qwen2.5-3B-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/Qwen2.5-3B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Qwen/Qwen2.5-3B-Instruct

SGLang

How to use Qwen/Qwen2.5-3B-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/Qwen2.5-3B-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/Qwen2.5-3B-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/Qwen2.5-3B-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/Qwen2.5-3B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Qwen/Qwen2.5-3B-Instruct with Docker Model Runner:
```
docker model run hf.co/Qwen/Qwen2.5-3B-Instruct
```

FinCode-Reasoning-v1: Fine-tuned Qwen2.5-3B with Execution-Verified Financial Trajectories

#18

by coslinedev - opened about 7 hours ago

Discussion

coslinedev

about 7 hours ago

Hi Qwen Team and Community! 👋

We've recently used Qwen2.5-3B as the base model to build an execution-verified financial reasoning model: Qwen2.5-3B-FinCode-Reasoning.

Why this fine-tune?

Standard financial LLMs often suffer from hallucinations when handling complex quantitative reasoning or financial code generation. To solve this, we trained Qwen2.5-3B on FinCode-Reasoning-v1, a synthetic dataset where every trajectory is strictly verified via code execution and Property-Based Testing (PBT).

Key Highlights:

Execution-Verified: Reduces hallucination in quantitative logic and formulas.
RLVR Ready: Designed with verifiable reward traces for RL pipelines.
Fine-tuned on Qwen2.5-3B: Leverages Qwen's strong base capabilities for high-precision outputs.

Qwen
/

Qwen2.5-3B-Instruct

FinCode-Reasoning-v1: Fine-tuned Qwen2.5-3B with Execution-Verified Financial Trajectories

Why this fine-tune?

Key Highlights:

Links: