Instructions to use Mungert/granite-3.1-3b-a800m-instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mungert/granite-3.1-3b-a800m-instruct-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Mungert/granite-3.1-3b-a800m-instruct-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Mungert/granite-3.1-3b-a800m-instruct-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Mungert/granite-3.1-3b-a800m-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 Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16

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 Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16

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 Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16

Use Docker

docker model run hf.co/Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16

LM Studio
Jan

vLLM

How to use Mungert/granite-3.1-3b-a800m-instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mungert/granite-3.1-3b-a800m-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": "Mungert/granite-3.1-3b-a800m-instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16

SGLang

How to use Mungert/granite-3.1-3b-a800m-instruct-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Mungert/granite-3.1-3b-a800m-instruct-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mungert/granite-3.1-3b-a800m-instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Mungert/granite-3.1-3b-a800m-instruct-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mungert/granite-3.1-3b-a800m-instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Mungert/granite-3.1-3b-a800m-instruct-GGUF with Ollama:
```
ollama run hf.co/Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16
```

Unsloth Studio

How to use Mungert/granite-3.1-3b-a800m-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 Mungert/granite-3.1-3b-a800m-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 Mungert/granite-3.1-3b-a800m-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 Mungert/granite-3.1-3b-a800m-instruct-GGUF to start chatting

How to use Mungert/granite-3.1-3b-a800m-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 Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16

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": "Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Mungert/granite-3.1-3b-a800m-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 Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16

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 Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16

Run Hermes

hermes

OpenClaw new

How to use Mungert/granite-3.1-3b-a800m-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 Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16

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 "Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16" \
  --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 Mungert/granite-3.1-3b-a800m-instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16
```

Lemonade

How to use Mungert/granite-3.1-3b-a800m-instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Mungert/granite-3.1-3b-a800m-instruct-GGUF:BF16

Run and chat with the model

lemonade run user.granite-3.1-3b-a800m-instruct-GGUF-BF16

List all available models

lemonade list

Atomic Chat

granite-3.1-3b-a800m-instruct-GGUF / granite-3.1-3b-a800m-instruct-q5_k_m.gguf

Mungert

Super-squash history to reclaim storage

dcd79ee verified 10 months ago

Download

History Blame GGUF Editor new Delete

2.42 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:: 3ad38f5aa27ce6c696d04f324840ee278d6077b30b4aede6171e6607799dc9fa
Size of remote file:: 2.42 GB
SHA256:: eec3b3a465cc1aefb3c3fafad335b7da18c89d19388f19fe3a1c09d29893c70c

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