Instructions to use mrothroc/molgpt-moses-smiles-mixlab with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mrothroc/molgpt-moses-smiles-mixlab with Transformers:

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

pipe = pipeline("text-generation", model="mrothroc/molgpt-moses-smiles-mixlab")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mrothroc/molgpt-moses-smiles-mixlab")
model = AutoModelForCausalLM.from_pretrained("mrothroc/molgpt-moses-smiles-mixlab", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mrothroc/molgpt-moses-smiles-mixlab with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/mrothroc/molgpt-moses-smiles-mixlab

SGLang

How to use mrothroc/molgpt-moses-smiles-mixlab 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 "mrothroc/molgpt-moses-smiles-mixlab" \
    --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": "mrothroc/molgpt-moses-smiles-mixlab",
		"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 "mrothroc/molgpt-moses-smiles-mixlab" \
        --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": "mrothroc/molgpt-moses-smiles-mixlab",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mrothroc/molgpt-moses-smiles-mixlab with Docker Model Runner:
```
docker model run hf.co/mrothroc/molgpt-moses-smiles-mixlab
```

MolGPT (MOSES), reproduced in mixlab, SMILES

A small SMILES transformer that generates drug-like molecules. It is a faithful reproduction of MolGPT, trained in mixlab on a single Apple-silicon Mac.

This is a model to learn from and tinker with, not a state-of-the-art result. Architecture is something you discover by intuition and iteration, and the point of mixlab is to make that loop fast: reproduce a known model quickly, then experiment from there. This checkpoint is a starting point for that.

What it is

6,342,656 parameters: 8 layers, 256 dim, 8 heads, causal GPT with learned absolute positions and a GELU MLP. Exported as a native Hugging Face GPT2LMHeadModel.
Character-level SMILES tokenizer, vocab 30.
Trained on the MOSES benchmark, one molecule per sequence, 33,000 steps (about 10 epochs), roughly 5 hours on an M1 Max. Newer Apple chips are much faster.

How close is the reproduction

Scored on 30,000 unconditional samples with the official moses.metrics:

Metric	this model	MolGPT (published)
Valid	0.988	0.994
Unique@10k	0.991	~1.000
Novelty	0.783	0.797
IntDiv	0.857	0.857

Within a point on every firm metric. FCD/Test is 2.83; MolGPT never published an FCD at this size, so there is no firm number to match (the SELFIES sibling below reaches 0.43).

Load and generate

It loads with stock transformers as a GPT2LMHeadModel, no trust_remote_code. The framing tokens are BOS=1, EOS=2, PAD=0: prompt with BOS, sample until EOS, and read the SMILES off the tokens.

import torch
from transformers import AutoModelForCausalLM
from tokenizers import Tokenizer
from huggingface_hub import hf_hub_download

repo = "mrothroc/molgpt-moses-smiles-mixlab"
model = AutoModelForCausalLM.from_pretrained(repo).eval()             # GPT2LMHeadModel
tok = Tokenizer.from_file(hf_hub_download(repo, "tokenizer.json"))
BOS, EOS, PAD = 1, 2, 0

out = model.generate(torch.full((8, 1), BOS), do_sample=True, temperature=1.0,
                     top_k=0, max_length=65, eos_token_id=EOS, pad_token_id=PAD)
for row in out.tolist():
    seq = [t for t in row[1:] if t not in (BOS, PAD)]
    if EOS in seq: seq = seq[:seq.index(EOS)]
    print("".join(tok.id_to_token(t) for t in seq))                  # SMILES strings

The SELFIES sibling

mrothroc/molgpt-moses-selfies-mixlab retrains the same model on SELFIES, a representation where every string decodes to a valid molecule by construction: validity 1.000, Novelty 0.859, FCD/Test 0.43. Same size, same budget, better numbers, from changing the representation rather than the model.

Learn more

The full recipe (reproduce, three ways to guarantee validity, and goal-directed fine-tuning) is in the mixlab cookbook: https://github.com/mrothroc/mixlab-cookbook. Trainer: mixlab.

Citation

This is a reproduction, so please cite what it builds on:

MolGPT: Bagal, V.; Aggarwal, R.; Vinod, P. K.; Priyakumar, U. D. "MolGPT: Molecular Generation Using a Transformer-Decoder Model." J. Chem. Inf. Model. 2022, 62 (9), 2064-2076. doi:10.1021/acs.jcim.1c00600
MOSES: Polykovskiy, D. et al. "Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models." Front. Pharmacol. 2020, 11, 565644. doi:10.3389/fphar.2020.565644

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Safetensors

Model size

6.35M params

Tensor type

F32