Instructions to use Xiao-Youth/LECTOR-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Xiao-Youth/LECTOR-4B with Transformers:

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

pipe = pipeline("text-generation", model="Xiao-Youth/LECTOR-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Xiao-Youth/LECTOR-4B")
model = AutoModelForCausalLM.from_pretrained("Xiao-Youth/LECTOR-4B")
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 Settings

vLLM

How to use Xiao-Youth/LECTOR-4B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Xiao-Youth/LECTOR-4B

SGLang

How to use Xiao-Youth/LECTOR-4B 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 "Xiao-Youth/LECTOR-4B" \
    --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": "Xiao-Youth/LECTOR-4B",
		"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 "Xiao-Youth/LECTOR-4B" \
        --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": "Xiao-Youth/LECTOR-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Xiao-Youth/LECTOR-4B with Docker Model Runner:
```
docker model run hf.co/Xiao-Youth/LECTOR-4B
```

LECTOR-4B

LECTOR-4B is the 4B model checkpoint released with LECTOR: Joint Optimization of Scientific Reasoning Graphs and Introduction Generation.

LECTOR is designed for Content-Conditional Introduction Generation (CCIG). Given the main body of a scientific paper, it first extracts a reasoning logic graph and then generates a logic-aware introduction guided by that graph and citation context.

Paper: https://arxiv.org/abs/2605.25964
Code: https://github.com/Xiao-Youth/LECTOR
Base model: Qwen/Qwen3-4B-Instruct

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Xiao-Youth/LECTOR-4B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

For the full LECTOR prompting, rollout, and evaluation pipeline, see the project repository.

Intended Use

This model is intended for research on scientific reasoning graph extraction, logic-aware scientific writing, and content-conditional introduction generation.

Limitations

The model may produce incorrect reasoning graphs, unsupported claims, or citation errors. Generated text should be manually checked against the source paper content and references before use.

Citation

@misc{xiao2026lector,
      title={LECTOR: Joint Optimization of Scientific Reasoning Graphs and Introduction Generation},
      author={Jiabei Xiao and Yizhou Wang and Chen Tang and Pengze Li and Wanli Ouyang and Shixiang Tang},
      year={2026},
      eprint={2605.25964},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2605.25964},
}

Downloads last month: -

Safetensors

Model size

4B params

Tensor type

BF16

Dataset used to train Xiao-Youth/LECTOR-4B

Paper for Xiao-Youth/LECTOR-4B

LECTOR: Joint Optimization of Scientific Reasoning Graphs and Introduction Generation

Paper • 2605.25964 • Published May 25