Instructions to use RichardErkhov/ibm-granite_-_granite-8b-code-base-128k-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use RichardErkhov/ibm-granite_-_granite-8b-code-base-128k-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 RichardErkhov/ibm-granite_-_granite-8b-code-base-128k-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf RichardErkhov/ibm-granite_-_granite-8b-code-base-128k-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 RichardErkhov/ibm-granite_-_granite-8b-code-base-128k-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf RichardErkhov/ibm-granite_-_granite-8b-code-base-128k-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 RichardErkhov/ibm-granite_-_granite-8b-code-base-128k-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf RichardErkhov/ibm-granite_-_granite-8b-code-base-128k-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 RichardErkhov/ibm-granite_-_granite-8b-code-base-128k-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf RichardErkhov/ibm-granite_-_granite-8b-code-base-128k-gguf:Q4_K_M
Use Docker
docker model run hf.co/RichardErkhov/ibm-granite_-_granite-8b-code-base-128k-gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use RichardErkhov/ibm-granite_-_granite-8b-code-base-128k-gguf with Ollama:
ollama run hf.co/RichardErkhov/ibm-granite_-_granite-8b-code-base-128k-gguf:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use RichardErkhov/ibm-granite_-_granite-8b-code-base-128k-gguf with Docker Model Runner:
docker model run hf.co/RichardErkhov/ibm-granite_-_granite-8b-code-base-128k-gguf:Q4_K_M
- Lemonade
How to use RichardErkhov/ibm-granite_-_granite-8b-code-base-128k-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull RichardErkhov/ibm-granite_-_granite-8b-code-base-128k-gguf:Q4_K_M
Run and chat with the model
lemonade run user.ibm-granite_-_granite-8b-code-base-128k-gguf-Q4_K_M
List all available models
lemonade list
- Atomic Chat
- Xet hash:
- 5b1157bc81cc8be9a58466440b4f194bfa8306b62c87e5a9ceec8bb9f31a36db
- Size of remote file:
- 4.41 GB
- SHA256:
- 32b7d22b8e0da82566f469b3ae7a80d28c870a1d5687f5fc3683570492d0c636
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