Instructions to use TorpedoSoftware/Gemma-3-27B-Roblox-Luau with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use TorpedoSoftware/Gemma-3-27B-Roblox-Luau with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf TorpedoSoftware/Gemma-3-27B-Roblox-Luau:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf TorpedoSoftware/Gemma-3-27B-Roblox-Luau:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf TorpedoSoftware/Gemma-3-27B-Roblox-Luau:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf TorpedoSoftware/Gemma-3-27B-Roblox-Luau:Q4_K_M

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/Gemma-3-27B-Roblox-Luau:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf TorpedoSoftware/Gemma-3-27B-Roblox-Luau:Q4_K_M

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/Gemma-3-27B-Roblox-Luau:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf TorpedoSoftware/Gemma-3-27B-Roblox-Luau:Q4_K_M

Use Docker

docker model run hf.co/TorpedoSoftware/Gemma-3-27B-Roblox-Luau:Q4_K_M

LM Studio
Jan

vLLM

How to use TorpedoSoftware/Gemma-3-27B-Roblox-Luau with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TorpedoSoftware/Gemma-3-27B-Roblox-Luau"
# 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/Gemma-3-27B-Roblox-Luau",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TorpedoSoftware/Gemma-3-27B-Roblox-Luau:Q4_K_M

Ollama
How to use TorpedoSoftware/Gemma-3-27B-Roblox-Luau with Ollama:
```
ollama run hf.co/TorpedoSoftware/Gemma-3-27B-Roblox-Luau:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use TorpedoSoftware/Gemma-3-27B-Roblox-Luau with Docker Model Runner:
```
docker model run hf.co/TorpedoSoftware/Gemma-3-27B-Roblox-Luau:Q4_K_M
```

Lemonade

How to use TorpedoSoftware/Gemma-3-27B-Roblox-Luau with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull TorpedoSoftware/Gemma-3-27B-Roblox-Luau:Q4_K_M

Run and chat with the model

lemonade run user.Gemma-3-27B-Roblox-Luau-Q4_K_M

List all available models

lemonade list

Atomic Chat

Gemma-3-27B-Roblox-Luau

File size: 1,224 Bytes

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---
license: gemma
license_link: https://ai.google.dev/gemma/terms
base_model: google/gemma-3-27b-it
base_model_relation: finetune
finetuned_by: boatbomber
pipeline_tag: text-generation
tags:
- chat
- roblox
- luau
language:
- en
datasets:
- boatbomber/roblox-info-dump
- boatbomber/the-luau-stack
---

# Gemma-3-27B-Roblox-Luau

A fine tune of [google/gemma-3-27b-it](google/gemma-3-27b-it) using [boatbomber/roblox-info-dump](https://huggingface.co/datasets/boatbomber/roblox-info-dump) and [boatbomber/the-luau-stack](https://huggingface.co/datasets/boatbomber/the-luau-stack) for Roblox domain knowledge.

Available quants:

| Quant  | Size    | Notes |
| ------ | ------- | ----- |
| Q8_O   | 30.21GB | High resource use, but generally acceptable. Use only when accuracy is crucial. |
| Q6_K   | 23.32GB | Uses Q6_K for all tensors. Good for high end GPUs. |
| Q5_K_M | 20.24GB | Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K |
| Q4_K_M | 17.34GB | **Recommended.** Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K |
| Q3_K_M | 14.04GB | Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K. Quality is noticeably degraded. |