Instructions to use mekpro/gemma-3n-e2b-thailawqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mekpro/gemma-3n-e2b-thailawqa with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mekpro/gemma-3n-e2b-thailawqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mekpro/gemma-3n-e2b-thailawqa 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 mekpro/gemma-3n-e2b-thailawqa:Q8_0 # Run inference directly in the terminal: llama cli -hf mekpro/gemma-3n-e2b-thailawqa:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf mekpro/gemma-3n-e2b-thailawqa:Q8_0 # Run inference directly in the terminal: llama cli -hf mekpro/gemma-3n-e2b-thailawqa:Q8_0
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 mekpro/gemma-3n-e2b-thailawqa:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf mekpro/gemma-3n-e2b-thailawqa:Q8_0
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 mekpro/gemma-3n-e2b-thailawqa:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf mekpro/gemma-3n-e2b-thailawqa:Q8_0
Use Docker
docker model run hf.co/mekpro/gemma-3n-e2b-thailawqa:Q8_0
- LM Studio
- Jan
- Ollama
How to use mekpro/gemma-3n-e2b-thailawqa with Ollama:
ollama run hf.co/mekpro/gemma-3n-e2b-thailawqa:Q8_0
- Unsloth Desktop
- Docker Model Runner
How to use mekpro/gemma-3n-e2b-thailawqa with Docker Model Runner:
docker model run hf.co/mekpro/gemma-3n-e2b-thailawqa:Q8_0
- Lemonade
How to use mekpro/gemma-3n-e2b-thailawqa with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mekpro/gemma-3n-e2b-thailawqa:Q8_0
Run and chat with the model
lemonade run user.gemma-3n-e2b-thailawqa-Q8_0
List all available models
lemonade list
- Atomic Chat
Download processor_config.json from mekpro/gemma-3n-e2b-thailawqa: direct link, hf CLI and curl.
- Browser
- Download file 98 Bytes
-
https://huggingface.co/mekpro/gemma-3n-e2b-thailawqa/resolve/main/processor_config.json
- Command line
-
hf download hf://mekpro/gemma-3n-e2b-thailawqa/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/mekpro/gemma-3n-e2b-thailawqa/resolve/main/processor_config.json
98 Bytes
| { | |
| "audio_seq_length": 188, | |
| "image_seq_length": 256, | |
| "processor_class": "Gemma3nProcessor" | |
| } | |