Instructions to use mradermacher/CodeLLama-NL2SQL-34B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mradermacher/CodeLLama-NL2SQL-34B-GGUF with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mradermacher/CodeLLama-NL2SQL-34B-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mradermacher/CodeLLama-NL2SQL-34B-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 mradermacher/CodeLLama-NL2SQL-34B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/CodeLLama-NL2SQL-34B-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 mradermacher/CodeLLama-NL2SQL-34B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/CodeLLama-NL2SQL-34B-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 mradermacher/CodeLLama-NL2SQL-34B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf mradermacher/CodeLLama-NL2SQL-34B-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 mradermacher/CodeLLama-NL2SQL-34B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf mradermacher/CodeLLama-NL2SQL-34B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/mradermacher/CodeLLama-NL2SQL-34B-GGUF:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use mradermacher/CodeLLama-NL2SQL-34B-GGUF with Ollama:
ollama run hf.co/mradermacher/CodeLLama-NL2SQL-34B-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use mradermacher/CodeLLama-NL2SQL-34B-GGUF with Docker Model Runner:
docker model run hf.co/mradermacher/CodeLLama-NL2SQL-34B-GGUF:Q4_K_M
- Lemonade
How to use mradermacher/CodeLLama-NL2SQL-34B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mradermacher/CodeLLama-NL2SQL-34B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.CodeLLama-NL2SQL-34B-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download CodeLLama-NL2SQL-34B.Q2_K.gguf from mradermacher/CodeLLama-NL2SQL-34B-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 12.5 GB
-
https://huggingface.co/mradermacher/CodeLLama-NL2SQL-34B-GGUF/resolve/main/CodeLLama-NL2SQL-34B.Q2_K.gguf
- Command line
-
hf download hf://mradermacher/CodeLLama-NL2SQL-34B-GGUF/CodeLLama-NL2SQL-34B.Q2_K.gguf
-
curl -L -o CodeLLama-NL2SQL-34B.Q2_K.gguf https://huggingface.co/mradermacher/CodeLLama-NL2SQL-34B-GGUF/resolve/main/CodeLLama-NL2SQL-34B.Q2_K.gguf
12.5 GB
- Xet hash:
- 4dd92e28b8a3df1cc0cb15431da09dbb8837c31c6b920bca58e4293a1380dda7
- Size of remote file:
- 12.5 GB
- SHA256:
- 5061384a625f406a6fab40d5033ab08bfb9ba8c0701f3b5855b470585b74a209
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.