Instructions to use yerevann/chemlactica-125m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yerevann/chemlactica-125m with Transformers:

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

pipe = pipeline("text-generation", model="yerevann/chemlactica-125m")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("yerevann/chemlactica-125m")
model = AutoModelForMultimodalLM.from_pretrained("yerevann/chemlactica-125m")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yerevann/chemlactica-125m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yerevann/chemlactica-125m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yerevann/chemlactica-125m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/yerevann/chemlactica-125m

SGLang

How to use yerevann/chemlactica-125m 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 "yerevann/chemlactica-125m" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yerevann/chemlactica-125m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "yerevann/chemlactica-125m" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yerevann/chemlactica-125m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use yerevann/chemlactica-125m with Docker Model Runner:
```
docker model run hf.co/yerevann/chemlactica-125m
```

Chemlactica-125m is a continually pretrained galactica-125m model for organic molecules. It is pretrained on 40B tokens covering 110M+ molecules from PubChem as well as their chemical properties (molecular weight, synthetic accessibility score, drug-likeness etc.) and similarities (Tanimoto distance between ECFP fingerprints).

Example prompts:

</s>[START_SMILES]CC(=O)OC1=CC=CC=C1C(=O)O[END_SMILES][SAS] will attempt to predict the synthetic accessibility score of the given molecule.

</s>[SAS]2.25[/SAS][SIMILAR]0.62 CC(=O)OC1=CC=CC=C1C(=O)O[/SIMILAR][START_SMILES] will attempt to generate a molecule that has 2.25 SAS score and has a 0.62 similarity score to the given molecule.

The model can be wrapped into an optimization loop to traverse the chemical space with evolving prompts. See the code on GitHub.

A preprint with the details of the model and an optimization algorithm built on top of this model that sets state-of-the-art on Practical Molecular Optimization and other benchmarks is available on arxiv.

Few notes:

All queries should start with </s> symbol.
All numbers are rounded to two decimal points.
All SMILES are canonicalized using rdkit.
Available tags: [CLOGP], [WEIGHT], [QED], [SAS], [TPSA], [RINGCOUNT], [SIMILAR]...

The model is part of the 3-model family: Chemlactica-125M, Chemlactica-1.3B and Chemma-2B.

We are looking forward to see the community using the model in new applications and contexts.

Downloads last month: 26

Safetensors

Model size

0.1B params

Tensor type

F32

Collection including yerevann/chemlactica-125m

Small Molecule Optimization with LLMs

Collection

contains chemlactica-125m, chemlactica-1.3b, chemma-2b as well as training and validation data in JSONL format • 3 items • Updated Apr 16 • 3

Paper for yerevann/chemlactica-125m

Small Molecule Optimization with Large Language Models

Paper • 2407.18897 • Published Jul 26, 2024 • 1