Instructions to use makiisthebes/gemma-2-2b-Instruct-NL2SQL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use makiisthebes/gemma-2-2b-Instruct-NL2SQL with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("makiisthebes/gemma-2-2b-Instruct-NL2SQL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use makiisthebes/gemma-2-2b-Instruct-NL2SQL 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 makiisthebes/gemma-2-2b-Instruct-NL2SQL:Q4_K_M # Run inference directly in the terminal: llama cli -hf makiisthebes/gemma-2-2b-Instruct-NL2SQL:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf makiisthebes/gemma-2-2b-Instruct-NL2SQL:Q4_K_M # Run inference directly in the terminal: llama cli -hf makiisthebes/gemma-2-2b-Instruct-NL2SQL: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 makiisthebes/gemma-2-2b-Instruct-NL2SQL:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf makiisthebes/gemma-2-2b-Instruct-NL2SQL: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 makiisthebes/gemma-2-2b-Instruct-NL2SQL:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf makiisthebes/gemma-2-2b-Instruct-NL2SQL:Q4_K_M
Use Docker
docker model run hf.co/makiisthebes/gemma-2-2b-Instruct-NL2SQL:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use makiisthebes/gemma-2-2b-Instruct-NL2SQL with Ollama:
ollama run hf.co/makiisthebes/gemma-2-2b-Instruct-NL2SQL:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use makiisthebes/gemma-2-2b-Instruct-NL2SQL with Docker Model Runner:
docker model run hf.co/makiisthebes/gemma-2-2b-Instruct-NL2SQL:Q4_K_M
- Lemonade
How to use makiisthebes/gemma-2-2b-Instruct-NL2SQL with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull makiisthebes/gemma-2-2b-Instruct-NL2SQL:Q4_K_M
Run and chat with the model
lemonade run user.gemma-2-2b-Instruct-NL2SQL-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download tokenizer.json from makiisthebes/gemma-2-2b-Instruct-NL2SQL: direct link, hf CLI and curl.
- Browser
- Download file 34.4 MB
-
https://huggingface.co/makiisthebes/gemma-2-2b-Instruct-NL2SQL/resolve/main/tokenizer.json
- Command line
-
hf download hf://makiisthebes/gemma-2-2b-Instruct-NL2SQL/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/makiisthebes/gemma-2-2b-Instruct-NL2SQL/resolve/main/tokenizer.json
34.4 MB
- Xet hash:
- afa78551f6fdaff5671645d2394d4478e28255203cd7456448b0da82ae0a3ba8
- Size of remote file:
- 34.4 MB
- SHA256:
- 5f7eee611703c5ce5d1eee32d9cdcfe465647b8aff0c1dfb3bed7ad7dbb05060
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