Instructions to use language-plus-molecules/mCLM_1k-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use language-plus-molecules/mCLM_1k-3b with Transformers:

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

pipe = pipeline("text-generation", model="language-plus-molecules/mCLM_1k-3b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("language-plus-molecules/mCLM_1k-3b")
model = AutoModelForCausalLM.from_pretrained("language-plus-molecules/mCLM_1k-3b")
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

vLLM

How to use language-plus-molecules/mCLM_1k-3b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "language-plus-molecules/mCLM_1k-3b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "language-plus-molecules/mCLM_1k-3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/language-plus-molecules/mCLM_1k-3b

SGLang

How to use language-plus-molecules/mCLM_1k-3b 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 "language-plus-molecules/mCLM_1k-3b" \
    --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": "language-plus-molecules/mCLM_1k-3b",
		"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 "language-plus-molecules/mCLM_1k-3b" \
        --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": "language-plus-molecules/mCLM_1k-3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use language-plus-molecules/mCLM_1k-3b with Docker Model Runner:
```
docker model run hf.co/language-plus-molecules/mCLM_1k-3b
```

mCLM_1k-3b

mCLM: A Modular Chemical Language Model that Generates Functional and Makeable Molecules

Relevant Links

:globe_with_meridians: Website | :octocat: Code | :hugs: Data and Model | :desktop_computer: Demo | :page_with_curl: Paper

Architecture

Base Model: Qwen2.5-3B
Molecular Encoder: GNN with 5 message passing layers
Molecular Tokenizer: Custom block-based tokenizer for SMILES representations

Usage

Please follow installation instructions from the Github .


from mCLM.model.models import mCLM
from mCLM.tokenizer.utils import convert_instruction_to_input, message_ids_to_string, get_processor
import torch

# ===========================
# Settings
# ===========================
DTYPE = torch.bfloat16
DEVICE = torch.device("cpu")

if __name__ == "__main__":

        
    model = mCLM.from_pretrained("language-plus-molecules/mCLM_1k-3b")

    tokenizer = model.tokenizer
    molecule_tokenizer = model.molecule_tokenizer

    bad_words_ids = None

    model.to(DEVICE).to(DTYPE) #This is important for the HF model
        
    while True:
        user_input = input("Enter an instruction (type 'quit' to exit): ")
        if user_input == 'quit': break
        user_input = user_input.strip()

        message_tokens = convert_instruction_to_input(user_input, model, molecule_tokenizer, tokenizer)
            
        ################## Generate results ###################################

        beam_size = 5            

        input_ids = message_tokens.to(DEVICE)

        processor = get_processor(molecule_tokenizer, tokenizer) #we do this every time in case vocab was expanded

        generated = model.generate(
            input_ids=input_ids,
            attention_mask=torch.ones_like(input_ids), #This is to turn off the attention mask warning
            pad_token_id=tokenizer.eos_token_id, #This is to turn off the pad token warning
            max_new_tokens=32,
            num_beams=beam_size,
            num_return_sequences=beam_size,
            logits_processor=processor,
            do_sample=False,
            bad_words_ids=bad_words_ids,
            diversity_penalty=1.0,
            num_beam_groups=beam_size,
        )

        for i in [0]: #range(beam_size):
            message_ids = generated[i, message_tokens.shape[1]:]
            
            mol_msg, smiles_msg, mol_list, smiles_list = message_ids_to_string(message_ids, molecule_tokenizer, tokenizer)

            if smiles_msg != None:
                print(mol_msg)
                if len(smiles_list) > 0:
                    print("SMILES list:", smiles_list)
            else:
                print(mol_msg)

            print()

Training Data

The model was trained on:

Citation

If you use this model, please cite:

@misc{edwards2025mclmmodularchemicallanguage,
      title={mCLM: A Modular Chemical Language Model that Generates Functional and Makeable Molecules}, 
      author={Carl Edwards and Chi Han and Gawon Lee and Thao Nguyen and Sara Szymkuć and Chetan Kumar Prasad and Bowen Jin and Jiawei Han and Ying Diao and Ge Liu and Hao Peng and Bartosz A. Grzybowski and Martin D. Burke and Heng Ji},
      year={2025},
      eprint={2505.12565},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2505.12565}, 
}