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, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yerevann/chemlactica-125m")
model = AutoModelForCausalLM.from_pretrained("yerevann/chemlactica-125m", device_map="auto")

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

Chemlactica-125M is a 125M-parameter causal language model for molecular generation, property conditioning, property prediction, and optimization. It continues pretraining from Galactica-125M on PubChemForLM: 40 billion tokens describing more than 110 million PubChem molecules, computed properties, and molecular similarities.

Model details

Architecture: OPTForCausalLM; 12 layers, hidden size 768, 12 attention heads
Context length: 2,048 tokens
Training objective: causal language modeling
Training-data cutoff: January 26, 2023
Molecular preprocessing: RDKit-canonicalized SMILES; numeric properties rounded to two decimals

Prompt format

Chemlactica prompts begin with </s>. Generate a molecule by ending a structured property prefix with [START_SMILES]:

</s>[SAS]2.25[/SAS][SIMILAR]CC(=O)OC1=CC=CC=C1C(=O)O 0.62[/SIMILAR][START_SMILES]

The similarity field is reference SMILES, a space, then the target ECFP Tanimoto similarity. To predict a property, provide the molecule and open the desired property tag:

</s>[START_SMILES]CC(=O)OC1=CC=CC=C1C(=O)O[END_SMILES][SAS]

Common trained tags include [SAS], [QED], [WEIGHT], [TPSA], [CLOGP], [FORMULA], [RINGCOUNT], and [SIMILAR]. Use only tags present in the training format; arbitrary natural-language instructions or oracle names are unsupported.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "yerevann/chemlactica-125m"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id, device_map="auto", torch_dtype="auto"
)

prompt = "</s>[QED]0.80[/QED][START_SMILES]"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs, max_new_tokens=128, do_sample=True, temperature=0.8, top_p=0.95
)
print(tokenizer.decode(outputs[0], skip_special_tokens=False))

For optimization, use the Mol-E implementation and canonical PMO prompt registry. In the five-seed PMO-10K reproduction, this checkpoint obtains summed Top-10 AUC scores of 16.944598 (task-agnostic) and 20.429421 (task-informed) across 23 tasks.

Intended use and limitations

This checkpoint is intended for research on small-molecule language modeling and optimization. Generated strings may be invalid, duplicated, chemically implausible, difficult to synthesize, unsafe, or outside the requested property range. Reported properties are predictions or optimization-oracle outputs, not experimental measurements. PubChem-derived data may contain errors or biases, and the model has no knowledge of changes after the cutoff date. Do not use it for clinical decisions or as a substitute for experimental and expert review.