---
language:
- en
license: mit
library_name: peft
pipeline_tag: text-generation
base_model: mistralai/Mistral-7B-Instruct-v0.3
tags:
- lora
- peft
- transformers
- mistral
- grpo
- molecule-optimization
---

# C-MORAL Mistral GRPO Adapters

This repository contains **LoRA adapters** released for **C-MORAL**:

**C-MORAL: Controllable Multi-Objective Molecular Optimization with Reinforcement Alignment for LLMs**

These adapters are trained on top of:

- `mistralai/Mistral-7B-Instruct-v0.3`

using:

- `GRPO`

for controllable multi-objective molecular optimization.

## Available Task Subfolders

Each task is stored as a separate subfolder in this Hugging Face repository.

- `abmp`: `amp+bbbp+mutag+plogp`
- `acep`: `amp+carc+herg+plogp`
- `bcmq`: `bbbp+carc+mutag+qed`
- `bdeq`: `bbbp+drd2+herg+qed`
- `bdpq`: `bbbp+drd2+qed+plogp`
- `bpq`: `bbbp+plogp+qed`
- `cde`: `carc+drd2+herg`
- `dhmq`: `drd2+hia+mutag+qed`
- `elq`: `herg+liv+qed`
- `hlmpq`: `hia+liv+mutag+plogp+qed`

## Usage

Load a task-specific adapter with PEFT by setting `subfolder` to the desired task name.

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model_id = "mistralai/Mistral-7B-Instruct-v0.3"
adapter_repo = "Rwigle/C-MORAL-Mistral-GRPO"
task_subfolder = "bpq"  # change to abmp / elq / hlmpq / ...

tokenizer = AutoTokenizer.from_pretrained(base_model_id)
model = AutoModelForCausalLM.from_pretrained(base_model_id)
model = PeftModel.from_pretrained(model, adapter_repo, subfolder=task_subfolder)
```

## Method

- Base model: `mistralai/Mistral-7B-Instruct-v0.3`
- Adapter type: `LoRA`
- Training algorithm: `GRPO`
- Domain: multi-objective molecular optimization

## Project

- GitHub: `https://github.com/Rwigie/C-MORAL`

## Citation

If you use these adapters, please cite:

```text
C-MORAL: Controllable Multi-Objective Molecular Optimization with
Reinforcement Alignment for LLMs
```