Instructions to use osllmai/granite-3.0-8b-instruct-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 osllmai/granite-3.0-8b-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 osllmai/granite-3.0-8b-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf osllmai/granite-3.0-8b-instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf osllmai/granite-3.0-8b-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf osllmai/granite-3.0-8b-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 osllmai/granite-3.0-8b-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf osllmai/granite-3.0-8b-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 osllmai/granite-3.0-8b-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf osllmai/granite-3.0-8b-instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/osllmai/granite-3.0-8b-instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use osllmai/granite-3.0-8b-instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "osllmai/granite-3.0-8b-instruct-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "osllmai/granite-3.0-8b-instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/osllmai/granite-3.0-8b-instruct-GGUF:Q4_K_M

Ollama
How to use osllmai/granite-3.0-8b-instruct-GGUF with Ollama:
```
ollama run hf.co/osllmai/granite-3.0-8b-instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use osllmai/granite-3.0-8b-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 osllmai/granite-3.0-8b-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 osllmai/granite-3.0-8b-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 osllmai/granite-3.0-8b-instruct-GGUF to start chatting

How to use osllmai/granite-3.0-8b-instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf osllmai/granite-3.0-8b-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": "osllmai/granite-3.0-8b-instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use osllmai/granite-3.0-8b-instruct-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf osllmai/granite-3.0-8b-instruct-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "osllmai/granite-3.0-8b-instruct-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use osllmai/granite-3.0-8b-instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/osllmai/granite-3.0-8b-instruct-GGUF:Q4_K_M
```

Lemonade

How to use osllmai/granite-3.0-8b-instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull osllmai/granite-3.0-8b-instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.granite-3.0-8b-instruct-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use osllmai/granite-3.0-8b-instruct-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf osllmai/granite-3.0-8b-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 osllmai/granite-3.0-8b-instruct-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

osllm.ai Models Highlights Program

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Highlighting new and noteworthy models from the community. Join the conversation on Discord.

Model creator: ibm-granite

Original model: granite-3.0-3b-a800m-instruct

Official Website • Documentation • Discord

NEW: Subscribe to our mailing list for updates and news!

Email: support@osllm.ai

Model Summary:

Granite-3.0-8B-Instruct is an 8B parameter model finetuned from Granite-3.0-8B-Base using a combination of open-source instruction datasets with permissive licenses and internally collected synthetic datasets. This model is developed using a diverse set of techniques with a structured chat format, including supervised finetuning, model alignment using reinforcement learning, and model merging.

Technical Specifications:

Granite-3.0-8B-Instruct

Model Summary: Granite-3.0-8B-Instruct is a 8B parameter model finetuned from Granite-3.0-8B-Base using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets. This model is developed using a diverse set of techniques with a structured chat format, including supervised finetuning, model alignment using reinforcement learning, and model merging.

Developers: Granite Team, IBM
GitHub Repository: ibm-granite/granite-3.0-language-models
Website: Granite Docs
Paper: Granite 3.0 Language Models
Release Date: October 21st, 2024
License: Apache 2.0

Supported Languages: English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 3.0 models for languages beyond these 12 languages.

Intended use: The model is designed to respond to general instructions and can be used to build AI assistants for multiple domains, including business applications.

Capabilities

Summarization
Text classification
Text extraction
Question-answering
Retrieval Augmented Generation (RAG)
Code related tasks
Function-calling tasks
Multilingual dialog use cases

About osllm.ai:

osllm.ai is a community-driven platform that provides access to a wide range of open-source language models.

IndoxJudge: A free, open-source tool for evaluating large language models (LLMs).
It provides key metrics to assess performance, reliability, and risks like bias and toxicity, helping ensure model safety.
inDox: An open-source retrieval augmentation tool for extracting data from various
document formats (text, PDFs, HTML, Markdown, LaTeX). It handles structured and unstructured data and supports both
online and offline LLMs.
IndoxGen: A framework for generating high-fidelity synthetic data using LLMs and
human feedback, designed for enterprise use with high flexibility and precision.
Phoenix: A multi-platform, open-source chatbot that interacts with documents
locally, without internet or GPU. It integrates inDox and IndoxJudge to improve accuracy and prevent hallucinations,
ideal for sensitive fields like healthcare.
Phoenix_cli: A multi-platform command-line tool that runs LLaMA models locally,
supporting up to eight concurrent tasks through multithreading, eliminating the need for cloud-based services.

Special thanks

🙏 Special thanks to Georgi Gerganov and the whole team working on llama.cpp for making all of this possible.

Disclaimers

osllm.ai is not the creator, originator, or owner of any Model featured in the Community Model Program.
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Model, or use of any other Community Model provided by or through osllm.ai.

Downloads last month: 82

GGUF

Model size

8B params

Architecture

granite

Hardware compatibility

3-bit

4-bit

6-bit

8-bit

Model tree for osllmai/granite-3.0-8b-instruct-GGUF

Base model

ibm-granite/granite-3.0-8b-base

Finetuned

ibm-granite/granite-3.0-8b-instruct

Quantized

(23)

this model

Evaluation results

pass@1 on IFEval
self-reported

52.270
pass@1 on IFEval
self-reported

8.220
pass@1 on AGI-Eval
self-reported

40.520
pass@1 on AGI-Eval
self-reported

65.820
pass@1 on AGI-Eval
self-reported

34.450
pass@1 on OBQA
self-reported

46.600
pass@1 on OBQA
self-reported

71.210
pass@1 on OBQA
self-reported

82.610