Instructions to use moe2382/gemma-mca-agent-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use moe2382/gemma-mca-agent-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="moe2382/gemma-mca-agent-gguf", filename="gemma-mca-agent-Q4_K_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use moe2382/gemma-mca-agent-gguf 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 moe2382/gemma-mca-agent-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf moe2382/gemma-mca-agent-gguf:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf moe2382/gemma-mca-agent-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf moe2382/gemma-mca-agent-gguf: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 moe2382/gemma-mca-agent-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf moe2382/gemma-mca-agent-gguf: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 moe2382/gemma-mca-agent-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf moe2382/gemma-mca-agent-gguf:Q4_K_M
Use Docker
docker model run hf.co/moe2382/gemma-mca-agent-gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use moe2382/gemma-mca-agent-gguf with Ollama:
ollama run hf.co/moe2382/gemma-mca-agent-gguf:Q4_K_M
- Unsloth Studio
How to use moe2382/gemma-mca-agent-gguf 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 moe2382/gemma-mca-agent-gguf 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 moe2382/gemma-mca-agent-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for moe2382/gemma-mca-agent-gguf to start chatting
- Atomic Chat new
- Docker Model Runner
How to use moe2382/gemma-mca-agent-gguf with Docker Model Runner:
docker model run hf.co/moe2382/gemma-mca-agent-gguf:Q4_K_M
- Lemonade
How to use moe2382/gemma-mca-agent-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull moe2382/gemma-mca-agent-gguf:Q4_K_M
Run and chat with the model
lemonade run user.gemma-mca-agent-gguf-Q4_K_M
List all available models
lemonade list
Gemma MCA Agent - GGUF Quantized
Quantized versions of the Gemma 3 1B MCA SMS agent for fast CPU inference.
Models
| File | Size | Quantization | Notes |
|---|---|---|---|
| gemma-mca-agent-Q4_K_M.gguf | 769MB | Q4_K_M | Recommended - best quality/size balance |
| gemma-mca-agent-Q5_K_M.gguf | 812MB | Q5_K_M | Higher quality, slightly larger |
Usage with llama-cpp-python
from llama_cpp import Llama
llm = Llama(
model_path="./gemma-mca-agent-Q4_K_M.gguf",
n_ctx=2048,
n_threads=4,
)
prompt = "<start_of_turn>user\nI'm interested in funding<end_of_turn>\n<start_of_turn>model\n"
output = llm(prompt, max_tokens=256, temperature=0.7)
print(output["choices"][0]["text"])
Base Model
Fine-tuned from google/gemma-3-1b-it using LoRA on 3,300+ SMS conversation examples.
Original adapter: moe2382/gemma-mca-agent
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