Instructions to use luzimu/FullStack-Learn-LM-30B-A3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use luzimu/FullStack-Learn-LM-30B-A3B with Transformers:

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

pipe = pipeline("text-generation", model="luzimu/FullStack-Learn-LM-30B-A3B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("luzimu/FullStack-Learn-LM-30B-A3B")
model = AutoModelForCausalLM.from_pretrained("luzimu/FullStack-Learn-LM-30B-A3B")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use luzimu/FullStack-Learn-LM-30B-A3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "luzimu/FullStack-Learn-LM-30B-A3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "luzimu/FullStack-Learn-LM-30B-A3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/luzimu/FullStack-Learn-LM-30B-A3B

SGLang

How to use luzimu/FullStack-Learn-LM-30B-A3B 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 "luzimu/FullStack-Learn-LM-30B-A3B" \
    --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": "luzimu/FullStack-Learn-LM-30B-A3B",
		"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 "luzimu/FullStack-Learn-LM-30B-A3B" \
        --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": "luzimu/FullStack-Learn-LM-30B-A3B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use luzimu/FullStack-Learn-LM-30B-A3B with Docker Model Runner:
```
docker model run hf.co/luzimu/FullStack-Learn-LM-30B-A3B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

FullStack-Agent

Overview

This model is introduced in the paper "FullStack-Agent: Enhancing Agentic Full-Stack Web Coding via Development-Oriented Testing and Repository Back-Translation".

In this paper, we introduce FullStack-Agent, a unified system that combines a multi-agent full-stack development framework equipped with efficient coding and debugging tools (FullStack-Dev), an iterative self-improvement method that improves the abilities of LLMs through repository augmentation and back-translation (FullStack-Learn), and a full-stack development benchmark that comprehensively evaluates frontend, backend, and database functionalities (FullStack-Bench).

Official Repository: mnluzimu/FullStack-Agent

Quick Start

Instructions for installation and running of the three components are in the following documents:

Models

Model Name	Huggingface Link
FullStack-Learn-LM-30B-A3B	🤗 luzimu/FullStack-Learn-LM-30B-A3B

Dataset

Dataset Name	Huggingface Link
FullStack-Bench	🤗 luzimu/FullStack-Bench

Experimental Results

Experimental results of FullStack-Dev on FullStack-Bench compared to popular baseline methods are shown below:

The result of using more templates is presented below:

Using more templates result in better performance in most of the metrics, which might be due to the fact that with more templates to choose from, the agent can find the most appropriate and easy-to-work-with templates, thus making the development process smoother.

Experimental results of FullStack-Learn tested on with FullStack-Dev on FullStack-Bench are as follows:

Citation

If you find our project helpful, please cite:

@misc{lu2026fullstackagentenhancingagenticfullstack,
      title={FullStack-Agent: Enhancing Agentic Full-Stack Web Coding via Development-Oriented Testing and Repository Back-Translation}, 
      author={Zimu Lu and Houxing Ren and Yunqiao Yang and Ke Wang and Zhuofan Zong and Mingjie Zhan and Hongsheng Li},
      year={2026},
      eprint={2602.03798},
      archivePrefix={arXiv},
      primaryClass={cs.SE},
      url={https://arxiv.org/abs/2602.03798}, 
}