Instructions to use mradermacher/granite-20b-code-instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mradermacher/granite-20b-code-instruct-GGUF with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mradermacher/granite-20b-code-instruct-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mradermacher/granite-20b-code-instruct-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/granite-20b-code-instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/granite-20b-code-instruct-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/granite-20b-code-instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/granite-20b-code-instruct-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/granite-20b-code-instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf mradermacher/granite-20b-code-instruct-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/granite-20b-code-instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf mradermacher/granite-20b-code-instruct-GGUF:Q4_K_M
Use Docker
docker model run hf.co/mradermacher/granite-20b-code-instruct-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use mradermacher/granite-20b-code-instruct-GGUF with Ollama:
ollama run hf.co/mradermacher/granite-20b-code-instruct-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use mradermacher/granite-20b-code-instruct-GGUF with Docker Model Runner:
docker model run hf.co/mradermacher/granite-20b-code-instruct-GGUF:Q4_K_M
- Lemonade
How to use mradermacher/granite-20b-code-instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mradermacher/granite-20b-code-instruct-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.granite-20b-code-instruct-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download granite-20b-code-instruct.IQ3_M.gguf from mradermacher/granite-20b-code-instruct-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 9.59 GB
-
https://huggingface.co/mradermacher/granite-20b-code-instruct-GGUF/resolve/main/granite-20b-code-instruct.IQ3_M.gguf
- Command line
-
hf download hf://mradermacher/granite-20b-code-instruct-GGUF/granite-20b-code-instruct.IQ3_M.gguf
-
curl -L -o granite-20b-code-instruct.IQ3_M.gguf https://huggingface.co/mradermacher/granite-20b-code-instruct-GGUF/resolve/main/granite-20b-code-instruct.IQ3_M.gguf
9.59 GB
- Xet hash:
- 5abc475f21a40f024b2196bb09bb9f952f48fc98c8552d99d8ba1ee119a700aa
- Size of remote file:
- 9.59 GB
- SHA256:
- b13821422b456eae947017f67ba646c03efeaf585c042e4e31596e15d8bfa8a6
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