Instructions to use AgPerry/SWE-Lego-Qwen3-4B-posttrain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AgPerry/SWE-Lego-Qwen3-4B-posttrain with Transformers:

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

pipe = pipeline("text-generation", model="AgPerry/SWE-Lego-Qwen3-4B-posttrain")
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("AgPerry/SWE-Lego-Qwen3-4B-posttrain")
model = AutoModelForCausalLM.from_pretrained("AgPerry/SWE-Lego-Qwen3-4B-posttrain", 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 AgPerry/SWE-Lego-Qwen3-4B-posttrain with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/AgPerry/SWE-Lego-Qwen3-4B-posttrain

SGLang

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

Docker Model Runner
How to use AgPerry/SWE-Lego-Qwen3-4B-posttrain with Docker Model Runner:
```
docker model run hf.co/AgPerry/SWE-Lego-Qwen3-4B-posttrain
```

SWE-Lego-Qwen3-4B-posttrain / training_args.bin

AgPerry

Upload folder using huggingface_hub

edb3441 verified 6 months ago

History Blame Contribute Delete

7.1 kB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 8d810388199435149040a70d9a84022e958245a48693111d803a1464a590c5cf
Size of remote file:: 7.1 kB
SHA256:: 36b645a8358cb0183b693507565996812d2286b5f5ec752a88f185603f202fa2

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.

AgPerry
/

SWE-Lego-Qwen3-4B-posttrain

Detected Pickle imports (15)

Xet Pointer Details