Instructions to use SandLogicTechnologies/Granite-3.1-8b-instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SandLogicTechnologies/Granite-3.1-8b-instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="SandLogicTechnologies/Granite-3.1-8b-instruct-GGUF",
	filename="granite-3.1-8b-instruct-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use SandLogicTechnologies/Granite-3.1-8b-instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SandLogicTechnologies/Granite-3.1-8b-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf SandLogicTechnologies/Granite-3.1-8b-instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SandLogicTechnologies/Granite-3.1-8b-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf SandLogicTechnologies/Granite-3.1-8b-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 SandLogicTechnologies/Granite-3.1-8b-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf SandLogicTechnologies/Granite-3.1-8b-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 SandLogicTechnologies/Granite-3.1-8b-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SandLogicTechnologies/Granite-3.1-8b-instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/SandLogicTechnologies/Granite-3.1-8b-instruct-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use SandLogicTechnologies/Granite-3.1-8b-instruct-GGUF with Ollama:
```
ollama run hf.co/SandLogicTechnologies/Granite-3.1-8b-instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use SandLogicTechnologies/Granite-3.1-8b-instruct-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SandLogicTechnologies/Granite-3.1-8b-instruct-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SandLogicTechnologies/Granite-3.1-8b-instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SandLogicTechnologies/Granite-3.1-8b-instruct-GGUF to start chatting

How to use SandLogicTechnologies/Granite-3.1-8b-instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf SandLogicTechnologies/Granite-3.1-8b-instruct-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "SandLogicTechnologies/Granite-3.1-8b-instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use SandLogicTechnologies/Granite-3.1-8b-instruct-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf SandLogicTechnologies/Granite-3.1-8b-instruct-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default SandLogicTechnologies/Granite-3.1-8b-instruct-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use SandLogicTechnologies/Granite-3.1-8b-instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/SandLogicTechnologies/Granite-3.1-8b-instruct-GGUF:Q4_K_M
```

Lemonade

How to use SandLogicTechnologies/Granite-3.1-8b-instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SandLogicTechnologies/Granite-3.1-8b-instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Granite-3.1-8b-instruct-GGUF-Q4_K_M

List all available models

lemonade list

SandLogic Technology Quantized Granite-3.1-8B-Instruct-GGUF

This repository contains Q4_KM and Q5_KM quantized versions of the ibm-granite/granite-3.1-8b-instruct model. These quantized variants maintain the core capabilities of the original model while significantly reducing the memory footprint and increasing inference speed.

Discover our full range of quantized language models by visiting our SandLogic Lexicon GitHub. To learn more about our company and services, check out our website at SandLogic.

Model Details

Original Model: Granite-3.1-8B-Instruct
Quantized Versions:
- Q4_KM (4-bit quantization)
- Q5_KM (5-bit quantization)
Base Architecture: 8B parameter long-context instruct model
Developer: Granite Team, IBM
License: Apache 2.0
Release Date: December 18th, 2024

Quantization Benefits

Q4_KM Version

Reduced model size: ~4GB (75% smaller than original)
Faster inference speed
Minimal quality degradation
Optimal for resource-constrained environments

Q5_KM Version

Reduced model size: ~5GB (69% smaller than original)
Better quality preservation compared to Q4
Balanced trade-off between model size and performance
Recommended for quality-sensitive applications

Supported Languages

The quantized models maintain support for all original languages:

English
German
Spanish
French
Japanese
Portuguese
Arabic
Czech
Italian
Korean
Dutch
Chinese

Users can fine-tune these quantized models for additional languages.

Capabilities

Both quantized versions preserve the original model's capabilities:

Summarization
Text classification
Text extraction
Question-answering
Retrieval Augmented Generation (RAG)
Code related tasks
Function-calling tasks
Multilingual dialog use cases
Long-context tasks including document/meeting summarization and QA

Usage

from llama_cpp import Llama

llm = Llama(
    model_path="models/granite-3.1-8b-instruct-Q4_K_M.gguf",
    verbose=False,
    # n_gpu_layers=-1, # Uncomment to use GPU acceleration
    # n_ctx=2048, # Uncomment to increase the context window
)

output = llm.create_chat_completion(
    messages =[
    {
        "role": "system",
        "content": "You are an AI Assistant"  
        ,
    },
    {"role": "user", "content": "Please list one IBM Research laboratory located in the United States. You should only output its name and location."},
]
)

print(output["choices"][0]['message']['content'])

Intended Use

These quantized models are designed for:

Resource-constrained environments
Edge deployment scenarios
Applications requiring faster inference
Building AI assistants for multiple domains
Business applications with limited computational resources

Training Information

The base model was trained on:

Publicly available datasets with permissive license
Internal synthetic data targeting specific capabilities
Small amounts of human-curated data

Detailed attribution can be found in the upcoming Granite 3.1 Technical Report.

Acknowledgements

We thank Meta for developing the original IBM Granite model and the creators of the bigbio/med_qa dataset. Special thanks to Georgi Gerganov and the entire llama.cpp development team for their outstanding contributions.

Contact

For any inquiries or support, please contact us at support@sandlogic.com or visit our support page.

Explore More

For any inquiries or support, please contact us at support@sandlogic.com or visit our support page.

Downloads last month: 4

GGUF

Model size

8B params

Architecture

granite

Hardware compatibility

4-bit

5-bit

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Model tree for SandLogicTechnologies/Granite-3.1-8b-instruct-GGUF

Base model

ibm-granite/granite-3.1-8b-base

Finetuned

ibm-granite/granite-3.1-8b-instruct

Quantized

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