Instructions to use insilicomedicine/C3LM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use insilicomedicine/C3LM with Transformers:

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

pipe = pipeline("text-generation", model="insilicomedicine/C3LM")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("insilicomedicine/C3LM")
model = AutoModelForMultimodalLM.from_pretrained("insilicomedicine/C3LM")
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 Settings

vLLM

How to use insilicomedicine/C3LM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "insilicomedicine/C3LM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "insilicomedicine/C3LM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/insilicomedicine/C3LM

SGLang

How to use insilicomedicine/C3LM 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 "insilicomedicine/C3LM" \
    --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": "insilicomedicine/C3LM",
		"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 "insilicomedicine/C3LM" \
        --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": "insilicomedicine/C3LM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use insilicomedicine/C3LM with Docker Model Runner:
```
docker model run hf.co/insilicomedicine/C3LM
```

C3LM

Chemistry Constraint–Consistent Language Model

Overview

C3LM is a chemistry-focused language model for Single-Step Retrosynthesis task. This checkpoint was initialized from LFM2-2.6B and trained with supervised fine-tuning (SFT) on a combined CREED + USPTO-full dataset.

Load model

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model and tokenizer
model_id = "insilicomedicine/C3LM"
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype="bfloat16",
#    attn_implementation="flash_attention_2" <- uncomment on compatible GPU
)
tokenizer = AutoTokenizer.from_pretrained(model_id)

Usage example

import re

ATOM_TOKENS = ["Ag","Al","As","Au","Bi","Br","Ca","Cl","Cr","Cu","Fe","Ga","Gd","Ge","Hg","Li","Mg","Mo","Na","Pt","Ru","Sb","Se","Si","Sn","Sc","B","C","F","H","I","K","M","N","O","P","S","V","W","Z","c","e","n","o","p","s"]

SMI_REGEX_PATTERN = (
    r"(\[|\]|\(|\)|\.|=|#|-|\+|\\|\/|:|~|@|\?|>>?|\*|\$|\%[0-9]{2}|[0-9]|"
    + "|".join(ATOM_TOKENS)
    + ")"
)
smi_regex = re.compile(SMI_REGEX_PATTERN)

def isolate_sequence(s):
    return "".join(["<sm_" + tok + ">" for tok in smi_regex.findall(s)])

def deisolate_sequence(s):
    s = re.sub("[<\s]?sm_[^>]+>", lambda matchobj: matchobj.group(0)[4:-1], s).strip()
    return s

# Prepare prompts
product_smiles = "N1C(=O)Cc2ccc(Nc3ccc(N[C@@H]4CN(C(c5cc(NCCO)ccc5)=O)C[C@@H]4c4scnc4)cc3)cc21"
prompt = f"What are the possible reactants that could have formed the following product <smiles>{isolate_sequence(product_smiles)}</smiles>? Wrap an answer in <smiles> tags/think"

input_ids = tokenizer.apply_chat_template(
    [
        {"role": "system", "content": "You are a helpful assistant in chemistry and biology."},
        {"role": "user", "content": prompt}
    ],
    add_generation_prompt=True,
    return_tensors="pt",
    tokenize=True,
)['input_ids'].to(model.device)

# Generate answer
output = model.generate(
    input_ids,
    do_sample=True,
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_new_tokens=512,
)

for out in output:
    out = tokenizer.decode(out, skip_special_tokens=False)
    answer_part = out.split('</think>')[1].split('<smiles>')[1].split('</smiles>')[0]
    answer_part = deisolate_sequence(answer_part)
    print('Reactants: ', answer_part)

Citation

@misc{zagribelnyy2026singleanswerenoughrethinking,
      title={When Single Answer Is Not Enough: Rethinking Single-Step Retrosynthesis Benchmarks for LLMs}, 
      author={Bogdan Zagribelnyy and Ivan Ilin and Maksim Kuznetsov and Nikita Bondarev and Roman Schutski
                and Thomas MacDougall and Rim Shayakhmetov and Zulfat Miftakhutdinov
                and Mikolaj Mizera and Vladimir Aladinskiy and Alex Aliper and Alex Zhavoronkov},
      year={2026},
      eprint={2602.03554},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2602.03554}, 
}

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Safetensors

Model size

3B params

Tensor type

BF16

Model tree for insilicomedicine/C3LM

Base model

LiquidAI/LFM2-2.6B

Finetuned

(33)

this model

Paper for insilicomedicine/C3LM

When Single Answer Is Not Enough: Rethinking Single-Step Retrosynthesis Benchmarks for LLMs

Paper • 2602.03554 • Published Feb 3