Instructions to use Tuwhy/Qwen3-4B-OPSA-Code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tuwhy/Qwen3-4B-OPSA-Code with Transformers:

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

pipe = pipeline("text-generation", model="Tuwhy/Qwen3-4B-OPSA-Code")
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("Tuwhy/Qwen3-4B-OPSA-Code")
model = AutoModelForCausalLM.from_pretrained("Tuwhy/Qwen3-4B-OPSA-Code", 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 Tuwhy/Qwen3-4B-OPSA-Code with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Tuwhy/Qwen3-4B-OPSA-Code"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tuwhy/Qwen3-4B-OPSA-Code",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Tuwhy/Qwen3-4B-OPSA-Code

SGLang

How to use Tuwhy/Qwen3-4B-OPSA-Code 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 "Tuwhy/Qwen3-4B-OPSA-Code" \
    --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": "Tuwhy/Qwen3-4B-OPSA-Code",
		"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 "Tuwhy/Qwen3-4B-OPSA-Code" \
        --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": "Tuwhy/Qwen3-4B-OPSA-Code",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Tuwhy/Qwen3-4B-OPSA-Code with Docker Model Runner:
```
docker model run hf.co/Tuwhy/Qwen3-4B-OPSA-Code
```

Qwen3-4B-OPSA-Code / README.md

Tuwhy

Upload OPSA code checkpoint step 259

631e4fd verified about 1 month ago

preview code

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---
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
base_model: Qwen/Qwen3-4B
base_model_relation: finetune
arxiv: 2608.31046
tags:
- opsa
- code
- text-generation
---
# Qwen3-4B-OPSA-Code

This repository contains the code-domain checkpoint of [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) trained with [On-Policy Self-Adaptation (OPSA)](https://github.com/DripNowhy/On-Policy-Self-Adaptation).

- **Checkpoint:** step 259 (260 optimizer updates; zero-based checkpoint numbering).
- **Format:** full model weights in BF16 Safetensors, with configuration and tokenizer files.
- **License:** Apache 2.0, following the base model.

[Paper](https://arxiv.org/abs/2608.31046) · [Code](https://github.com/DripNowhy/On-Policy-Self-Adaptation) · [Collection](https://huggingface.co/collections/Tuwhy/on-policy-self-adaptation-6a62d0f36f1e42afa27c7215)

## Usage

Install `torch`, `accelerate`, and `transformers>=4.57.1`.

Use the Qwen chat template with `enable_thinking=False`.

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "Tuwhy/Qwen3-4B-OPSA-Code"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
messages = [{"role": "user", "content": "Write a Python function that checks whether a string is a palindrome."}]
inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt", enable_thinking=False,
).to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=2048,
    do_sample=True,
    temperature=0.7,
    top_p=0.8,
    top_k=20,
)
print(tokenizer.decode(outputs[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))
```

	---
	library_name: transformers
	license: apache-2.0
	pipeline_tag: text-generation
	base_model: Qwen/Qwen3-4B
	base_model_relation: finetune
	arxiv: 2608.31046
	tags:
	- opsa
	- code
	- text-generation
	---

	# Qwen3-4B-OPSA-Code

	This repository contains the code-domain checkpoint of [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) trained with [On-Policy Self-Adaptation (OPSA)](https://github.com/DripNowhy/On-Policy-Self-Adaptation).

	- Checkpoint: step 259 (260 optimizer updates; zero-based checkpoint numbering).
	- Format: full model weights in BF16 Safetensors, with configuration and tokenizer files.
	- License: Apache 2.0, following the base model.

	[Paper](https://arxiv.org/abs/2608.31046) · [Code](https://github.com/DripNowhy/On-Policy-Self-Adaptation) · [Collection](https://huggingface.co/collections/Tuwhy/on-policy-self-adaptation-6a62d0f36f1e42afa27c7215)

	## Usage

	Install `torch`, `accelerate`, and `transformers>=4.57.1`.

	Use the Qwen chat template with `enable_thinking=False`.

	```python
	import torch
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_id = "Tuwhy/Qwen3-4B-OPSA-Code"
	tokenizer = AutoTokenizer.from_pretrained(model_id)
	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	torch_dtype=torch.bfloat16,
	device_map="auto",
	)
	messages = [{"role": "user", "content": "Write a Python function that checks whether a string is a palindrome."}]
	inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt", enable_thinking=False,
	).to(model.device)
	outputs = model.generate(
	**inputs,
	max_new_tokens=2048,
	do_sample=True,
	temperature=0.7,
	top_p=0.8,
	top_k=20,
	)
	print(tokenizer.decode(outputs[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))
	```