TorpedoSoftware/the-luau-stack
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How to use TorpedoSoftware/Gemma-3-27B-Roblox-Luau with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="TorpedoSoftware/Gemma-3-27B-Roblox-Luau", filename="Gemma-3-27B-Roblox-Luau-Q3_K_M.gguf", )
llm.create_chat_completion(
messages = [
{
"role": "user",
"content": "What is the capital of France?"
}
]
)How to use TorpedoSoftware/Gemma-3-27B-Roblox-Luau with llama.cpp:
brew install llama.cpp # 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
winget install llama.cpp # 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
# 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
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
docker model run hf.co/TorpedoSoftware/Gemma-3-27B-Roblox-Luau:Q4_K_M
How to use TorpedoSoftware/Gemma-3-27B-Roblox-Luau with vLLM:
# 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?"
}
]
}'docker model run hf.co/TorpedoSoftware/Gemma-3-27B-Roblox-Luau:Q4_K_M
How to use TorpedoSoftware/Gemma-3-27B-Roblox-Luau with Ollama:
ollama run hf.co/TorpedoSoftware/Gemma-3-27B-Roblox-Luau:Q4_K_M
How to use TorpedoSoftware/Gemma-3-27B-Roblox-Luau with Unsloth Studio:
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/Gemma-3-27B-Roblox-Luau to start chatting
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/Gemma-3-27B-Roblox-Luau to start chatting
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for TorpedoSoftware/Gemma-3-27B-Roblox-Luau to start chatting
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
How to use TorpedoSoftware/Gemma-3-27B-Roblox-Luau with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull TorpedoSoftware/Gemma-3-27B-Roblox-Luau:Q4_K_M
lemonade run user.Gemma-3-27B-Roblox-Luau-Q4_K_M
lemonade list
A fine tune of google/gemma-3-27b-it using boatbomber/roblox-info-dump and 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. |