---
library_name: transformers
tags: []
---
# ***Mol-MoE***: Performant & Steerable Multi-Objective RLHF in Drug Design
*Diego Calanzone (1, 2), Pierluca D'Oro (2), Pierre-Luc Bacon (1, 2)*
*(1) Universite de Montreal, (2) Mila Quebec AI Institute*
## How to use this model
This LM is fine-tuned to generate molecules in the SMILES format wrt. desired properties.
For unconditioned SMILES generation, use the BOS token ``.
For conditioned generation, please refer to the paper and the official codebase to derive different conditioned models.
This model is the merging result of 5 fine-tuned versions (`JNK3, DRD2, GSK3B, CYP2D6, CYP2D19`) with equal interpolation weight: *w_i = 0.2*.
An example of the generation pipeline:
```
from transformers import AutoTokenizer, AutoModelForCausalLM
import re
# Setup
device = "cuda"
tokenizer = AutoTokenizer.from_pretrained("ddidacus/RS-mol-llama-1b")
model = AutoModelForCausalLM.from_pretrained("ddidacus/RS-mol-llama-1b")
generation_kwargs = {
"max_new_tokens": 128,
"min_length": -1,
"top_k": 0.0,
"top_p": 0.9,
"do_sample": True,
"pad_token_id": tokenizer.eos_token_id,
"temperature": 1.0
}
# Inference
query = ""
toks = tokenizer([query], return_tensors="pt")["input_ids"].to(device)
output = model.generate(toks, **generation_kwargs)
output = tokenizer.batch_decode(output)
# Parsing
filter = r'(.*?)'
molecule = re.findall(filter, output[0], re.DOTALL)
```
### Model Description
This model is a fine-tuned version of LLaMa 3.2 1B through two stages:
1. Fine-tuning on ~3.5M molecules extracted from: ZINC 250K, MOSES, CHEMBL
2. RLHF-tuning using RLOO on 5 distinct reward functions from PyTDC [1]
- **Developed by:** Diego Calanzone (diego.calanzone@mila.quebec)
- **Model type:** Decoder-only Transformer
- **Finetuned from model [optional]:** LLaMA 3.2 1B
Read the paper for further details.
### Sources
[1] https://tdcommons.ai/single_pred_tasks/overview