Instructions to use TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2 with Transformers:

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

pipe = pipeline("text-generation", model="TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2")
model = AutoModelForCausalLM.from_pretrained("TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2")
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]:]))

llama-cpp-python

How to use TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2",
	filename="Luau-Devstral-24B-Instruct-v0.2-BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Inference
Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2:Q4_K_XL
# Run inference directly in the terminal:
llama-cli -hf TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2:Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2:Q4_K_XL
# Run inference directly in the terminal:
llama-cli -hf TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2:Q4_K_XL

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2:Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2:Q4_K_XL

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2:Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2:Q4_K_XL

Use Docker

docker model run hf.co/TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2:Q4_K_XL

LM Studio
Jan

vLLM

How to use TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2:Q4_K_XL

SGLang

How to use TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2 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 "TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2" \
    --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": "TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2",
		"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 "TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2" \
        --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": "TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2 with Ollama:
```
ollama run hf.co/TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2:Q4_K_XL
```

Unsloth Studio new

How to use TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2 to start chatting

Pi new

How to use TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2:Q4_K_XL

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2:Q4_K_XL"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2:Q4_K_XL

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2:Q4_K_XL

Run Hermes

hermes

Docker Model Runner
How to use TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2 with Docker Model Runner:
```
docker model run hf.co/TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2:Q4_K_XL
```

Lemonade

How to use TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull TorpedoSoftware/Luau-Devstral-24B-Instruct-v0.2:Q4_K_XL

Run and chat with the model

lemonade run user.Luau-Devstral-24B-Instruct-v0.2-Q4_K_XL

List all available models

lemonade list

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Luau Devstral 24B Instruct v0.2

State-of-the-art Luau code generation through reinforcement learning post-training

A refined version of Luau-Devstral-24B-Instruct-v0.1, enhanced with Dr. GRPO (Zichen Liu et al., 2025) to deliver superior Luau programming capabilities for Roblox development.

Overview

This model represents a significant advancement in specialized code generation for Luau, building upon continuous pretraining with targeted reinforcement learning to achieve exceptional code quality.

Key Achievements:

State-of-the-art code formatting and linting performance
Minimal typechecker issues with strict mode compliance
Concise, direct responses without unnecessary verbosity
Robust problem-solving capabilities on complex Luau challenges

Model Information

Developer: Zack Williams (boatbomber)
Sponsor: Torpedo Software LLC
Base Model: Luau-Devstral-24B-Instruct-v0.1
Training Method: Dr. GRPO (Group Relative Policy Optimization)

Performance Benchmarks

Evaluated on the test split of TorpedoSoftware/LuauLeetcode containing 226 challenges, with results averaged across 3 runs per challenge.

Comparison Models

Base Models:

Competitive Benchmarks:

Note: OpenAI models utilize reasoning tokens as complete disabling of thinking is not available.

Benchmark Results

Unit Test Pass Rate

Measures problem-solving accuracy and correctness

Result: 4th place overall, demonstrating solid problem-solving capabilities while outperforming OpenAI models.

Linter Errors

Evaluates fundamental code quality

Result: State-of-the-art performance with the lowest error rate by a significant margin.

Linter Warnings

Assesses non-critical code quality issues

Result: State-of-the-art performance in minimizing code warnings.

Type Safety

Strict mode typechecking compliance

Result: 2nd place, closely trailing Claude Opus 4.1. Our model favors explicit type definitions for enhanced code clarity, which creates more opportunities for mistakes compared to Claude's reliance on inferred types.

Code Formatting

Edit distance from Stylua's standard format

Result: State-of-the-art performance with exceptional adherence to standard formatting conventions.

Response Length

Average response size (excluding reasoning tokens)

Result: Most concise responses among all models, delivering direct solutions without unnecessary preamble. This efficiency suggests potential for further improvements in problem solving through explicit problem decomposition or reasoning.

Training Methodology

Dataset

Primary Source: TorpedoSoftware/LuauLeetcode

2.6K leetcode-style Luau programming challenges
Structured difficulty progression: Easy → Medium → Hard

Training Process

Curriculum Learning Approach:

Easy Difficulty Phase
- 6.45M input tokens
- 25 hours training
Medium Difficulty Phase
- 17.02M input tokens
- 58 hours training
Hard Difficulty Phase
- 6.07M input tokens
- 20 hours training

Technical Configuration:

LoRA adapter with rank=128
Full precision training
Final merge to BF16 model

Reward Function Design

The model was optimized using four complementary reward signals:

Correctness - Unit testing via Jest-Lua
Quality - Code linting with Selene
Type Safety - Strict typechecking using Luau
Formatting - Style conformance via Stylua

Training Progress

Easy Difficulty Training

Medium Difficulty Training

Hard Difficulty Training

Quantization Support

Imatrix Calibration

Custom importance matrix computed using 5.73MB of specialized text data:

Calibration Sources:

This calibration ensures optimal performance for Luau/Roblox tasks while maintaining general intelligence. The imatrix.gguf file is included in the repository for custom quantization needs.

Environmental Impact

Carbon emissions estimated using the Machine Learning Impact calculator (Lacoste et al., 2019):

Hardware: A100 80GB SXM
Training Duration: 103 hours
Carbon Emissions: ~12 kg CO2eq
Equivalent Impact: ~31 miles driven by an average internal combustion engine vehicle