Instructions to use AIRLab-POLIMI/llama-3.2-1b-it-ft-lora-bt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AIRLab-POLIMI/llama-3.2-1b-it-ft-lora-bt with Transformers:

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

pipe = pipeline("text-generation", model="AIRLab-POLIMI/llama-3.2-1b-it-ft-lora-bt")
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("AIRLab-POLIMI/llama-3.2-1b-it-ft-lora-bt")
model = AutoModelForCausalLM.from_pretrained("AIRLab-POLIMI/llama-3.2-1b-it-ft-lora-bt", 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 AIRLab-POLIMI/llama-3.2-1b-it-ft-lora-bt with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AIRLab-POLIMI/llama-3.2-1b-it-ft-lora-bt"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AIRLab-POLIMI/llama-3.2-1b-it-ft-lora-bt",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AIRLab-POLIMI/llama-3.2-1b-it-ft-lora-bt

SGLang

How to use AIRLab-POLIMI/llama-3.2-1b-it-ft-lora-bt 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 "AIRLab-POLIMI/llama-3.2-1b-it-ft-lora-bt" \
    --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": "AIRLab-POLIMI/llama-3.2-1b-it-ft-lora-bt",
		"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 "AIRLab-POLIMI/llama-3.2-1b-it-ft-lora-bt" \
        --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": "AIRLab-POLIMI/llama-3.2-1b-it-ft-lora-bt",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AIRLab-POLIMI/llama-3.2-1b-it-ft-lora-bt with Docker Model Runner:
```
docker model run hf.co/AIRLab-POLIMI/llama-3.2-1b-it-ft-lora-bt
```

llama-3.2-1b-it-ft-lora-bt / README.md

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metadata

library_name: transformers
pipeline_tag: text-generation
base_model: meta-llama/Llama-3.2-1B-Instruct
license: llama3.2
language:
  - en
tags:
  - robotics
  - behavior-trees
  - behaviortree-cpp
  - ros2
  - llama
  - arxiv:2602.01870
model-index:
  - name: BTGenBot-2
    results:
      - task:
          type: text-generation
          name: Behavior Tree Generation
        dataset:
          name: BT Benchmark
          type: custom
        metrics:
          - type: success_rate
            name: Zero-shot Success Rate with Error Recovery
            value: 90.38
            unit: '%'
          - type: success_rate
            name: One-shot Success Rate with Error Recovery
            value: 98.07
            unit: '%'
          - type: xml_syntax
            name: XML Syntax Correctness with Error Recovery
            value: 100
            unit: '%'
          - type: action_coherency
            name: Action Coherency with Error Recovery
            value: 100
            unit: '%'
datasets:
  - AIRLab-POLIMI/synthetic_bt

BTGenBot-2

AIRLab-POLIMI/llama-3.2-1b-it-ft-lora-bt is the fine-tuned Llama 3.2 1B Instruct model released with BTGenBot-2: Efficient Behavior Tree Generation with Small Language Models.

BTGenBot-2 generates executable robot Behavior Trees from:

a natural-language task description, and
a list of available robot action primitives.

The model outputs XML Behavior Trees compatible with BehaviorTree.CPP, supporting ROS 2 robotics behavior-tree pipelines.

For the complete project, examples, code, dataset, benchmark, and paper, visit:

👉 https://airlab-polimi.github.io/BTGenBot-2/

Model Details

Developed by: AIRLab, Politecnico di Milano
Authors: Riccardo Andrea Izzo, Gianluca Bardaro, Matteo Matteucci
Base model: meta-llama/Llama-3.2-1B-Instruct
Model type: Small language model for Behavior Tree generation
Fine-tuning method: QLoRA / LoRA parameter-efficient fine-tuning
Input: Natural-language robot task + available robot action primitives
Output: XML Behavior Tree compatible with BehaviorTree.CPP
Language: English
Project page: https://airlab-polimi.github.io/BTGenBot-2/
Paper: https://arxiv.org/abs/2602.01870
Code: https://github.com/AIRLab-POLIMI/BTGenBot-2

Intended Use

This model is intended to generate robot Behavior Trees for research and development in robotics task planning.

Example applications include:

ROS 2 / Nav2-compatible task planning;
navigation Behavior Tree generation;
manipulation Behavior Tree generation;
simulation-based robot-task validation;
benchmarking language-model-based Behavior Tree generation.

Input Format

The recommended input format is:

Task:
Describe the robot task in natural language.

Actions:
[ActionName(parameters: parameter_1, parameter_2), AnotherAction(parameters: parameter_1)]

Output Format

The model is expected to return XML only:

<root BTCPP_format="4">
  <BehaviorTree ID="MainTree">
    ...
  </BehaviorTree>
</root>

Training Data

BTGenBot-2 was trained on a synthetic instruction-following dataset of 5,204 natural-language instruction / Behavior Tree pairs.

Each sample contains:

instruction: system-level instructions for Behavior Tree generation;
input: task description and available robot actions;
output: XML Behavior Tree.

The dataset was generated from real Behavior Trees and expanded through controlled synthetic generation.

See the full project page for details:

https://airlab-polimi.github.io/BTGenBot-2/

Training Procedure

The model was fine-tuned from meta-llama/Llama-3.2-1B-Instruct using QLoRA / LoRA.

Reported training details from the paper include:

Train/test split: 95% / 5%
Learning rate: 1e-4
Warmup ratio: 0.1
Batch size: 16
Training duration: approximately 30 hours
Hardware: 2 × NVIDIA RTX Quadro 6000 GPUs, 48 GB total VRAM

Citation

@article{izzo2026btgenbot,
  title={BTGenBot-2: Efficient Behavior Tree Generation with Small Language Models},
  author={Izzo, Riccardo Andrea and Bardaro, Gianluca and Matteucci, Matteo},
  journal={arXiv preprint arXiv:2602.01870},
  year={2026}
}

More Information

Project page: https://airlab-polimi.github.io/BTGenBot-2/
Code: https://github.com/AIRLab-POLIMI/BTGenBot-2
Paper: https://arxiv.org/abs/2602.01870