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

alinemati commited on Oct 25, 2024

Commit

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1 Parent(s): a00dd6f

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README.md +27 -18

README.md CHANGED Viewed

@@ -161,26 +161,35 @@ Granite-3.0-8B-Instruct is an 8B parameter model finetuned from Granite-3.0-8B-B
 **Technical Specifications**:
-This model has been meticulously fine-tuned using a combination of permissive open source datasets along with internally generated synthetic datasets to enhance its capabilities.
-It is specifically crafted to effectively respond to a wide array of general instructions, making it highly versatile for the creation of AI assistants that can operate across various domains and applications.
-**Languages Supported:** This powerful model is proficient in multiple languages, including but not limited to English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese, ensuring comprehensive accessibility for a diverse user base.
-**Developers**: Granite Team, IBM
-**GitHub Repository**: [ibm-granite/granite-3.0-language-models](https://github.com/ibm-granite/granite-3.0-language-models)
-**Website**: [Granite Docs](https://example.com)
-**Paper**: [Granite 3.0 Language Models](https://example.com)
-**Release Date**: October 21st, 2024
-**License**: Apache 2.0
 **About [osllm.ai](https://osllm.ai)**:

 **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](https://github.com/ibm-granite/granite-3.0-language-models)
+- **Website**: [Granite Docs](https://www.ibm.com/granite/docs/)
+- **Paper:** [Granite 3.0 Language Models](https://github.com/ibm-granite/granite-3.0-language-models/blob/main/paper.pdf)
+- **Release Date**: October 21st, 2024
+- **License:** [Apache 2.0](https://www.apache.org/licenses/LICENSE-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](https://osllm.ai)**: