Instructions to use DongkiKim/Mol-Llama-3.1-8B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DongkiKim/Mol-Llama-3.1-8B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="DongkiKim/Mol-Llama-3.1-8B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import MolLLaMA
model = MolLLaMA.from_pretrained("DongkiKim/Mol-Llama-3.1-8B-Instruct", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use DongkiKim/Mol-Llama-3.1-8B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DongkiKim/Mol-Llama-3.1-8B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DongkiKim/Mol-Llama-3.1-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DongkiKim/Mol-Llama-3.1-8B-Instruct

SGLang

How to use DongkiKim/Mol-Llama-3.1-8B-Instruct 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 "DongkiKim/Mol-Llama-3.1-8B-Instruct" \
    --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": "DongkiKim/Mol-Llama-3.1-8B-Instruct",
		"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 "DongkiKim/Mol-Llama-3.1-8B-Instruct" \
        --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": "DongkiKim/Mol-Llama-3.1-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DongkiKim/Mol-Llama-3.1-8B-Instruct with Docker Model Runner:
```
docker model run hf.co/DongkiKim/Mol-Llama-3.1-8B-Instruct
```

Mol-Llama-3.1-8B-Instruct

[Project Page] [Paper] [GitHub]

This repo contains the weights of Mol-LLaMA including the LoRA weights and projectors, based on meta-llama/Llama-3.1-8B-Instruct.

Architecture

Molecular encoders: Pretrained 2D encoder (MoleculeSTM) and 3D encoder (Uni-Mol)
Blending Module: Combining complementary information from 2D and 3D encoders via cross-attention
Q-Former: Embed molecular representations into query tokens based on SciBERT
LoRA: Adapters for fine-tuning LLMs

Training Dataset

Mol-LLaMA is trained on Mol-LLaMA-Instruct, to learn the fundamental characteristics of molecules with the reasoning ability and explanbility.

How to Use

Please check out the exemplar code for inference in the Github repo.

Citation

If you find our model useful, please consider citing our work.

@misc{kim2025molllama,
    title={Mol-LLaMA: Towards General Understanding of Molecules in Large Molecular Language Model},
    author={Dongki Kim and Wonbin Lee and Sung Ju Hwang},
    year={2025},
    eprint={2502.13449},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}