Instructions to use Rwigle/C-MORAL-Mistral-GRPO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use Rwigle/C-MORAL-Mistral-GRPO with PEFT:
```
Task type is invalid.
```

How to use Rwigle/C-MORAL-Mistral-GRPO with Transformers:

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

pipe = pipeline("text-generation", model="Rwigle/C-MORAL-Mistral-GRPO")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Rwigle/C-MORAL-Mistral-GRPO", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Rwigle/C-MORAL-Mistral-GRPO with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Rwigle/C-MORAL-Mistral-GRPO

SGLang

How to use Rwigle/C-MORAL-Mistral-GRPO 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 "Rwigle/C-MORAL-Mistral-GRPO" \
    --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": "Rwigle/C-MORAL-Mistral-GRPO",
		"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 "Rwigle/C-MORAL-Mistral-GRPO" \
        --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": "Rwigle/C-MORAL-Mistral-GRPO",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Rwigle/C-MORAL-Mistral-GRPO with Docker Model Runner:
```
docker model run hf.co/Rwigle/C-MORAL-Mistral-GRPO
```

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.

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:

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

Downloads last month: -

Model tree for Rwigle/C-MORAL-Mistral-GRPO

Base model

mistralai/Mistral-7B-v0.3

Finetuned

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

Adapter

(900)

this model