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

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

ibm-granite_-_granite-8b-code-base-128k-gguf / granite-8b-code-base-128k.Q4_0.gguf

RichardErkhov

uploaded model

b4332fc verified about 2 years ago

Download

History Blame GGUF Editor new Delete

4.59 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: f6f574c0c06c82fc786a91694bb6b30a7374eaa484ba72ef823a49fa8081eeee
Size of remote file:: 4.59 GB
SHA256:: 825ad7f016ccca196c77bbb220c5f22a14f2870df981a6bed2b901823af03f91

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.