Instructions to use MISHANM/meta-Llama-3.2-3B-Instruct.gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MISHANM/meta-Llama-3.2-3B-Instruct.gguf with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("MISHANM/meta-Llama-3.2-3B-Instruct.gguf", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use MISHANM/meta-Llama-3.2-3B-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 MISHANM/meta-Llama-3.2-3B-Instruct.gguf
# Run inference directly in the terminal:
llama cli -hf MISHANM/meta-Llama-3.2-3B-Instruct.gguf

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf MISHANM/meta-Llama-3.2-3B-Instruct.gguf
# Run inference directly in the terminal:
llama cli -hf MISHANM/meta-Llama-3.2-3B-Instruct.gguf

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 MISHANM/meta-Llama-3.2-3B-Instruct.gguf
# Run inference directly in the terminal:
./llama-cli -hf MISHANM/meta-Llama-3.2-3B-Instruct.gguf

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 MISHANM/meta-Llama-3.2-3B-Instruct.gguf
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MISHANM/meta-Llama-3.2-3B-Instruct.gguf

Use Docker

docker model run hf.co/MISHANM/meta-Llama-3.2-3B-Instruct.gguf

LM Studio
Jan
Ollama
How to use MISHANM/meta-Llama-3.2-3B-Instruct.gguf with Ollama:
```
ollama run hf.co/MISHANM/meta-Llama-3.2-3B-Instruct.gguf
```
Unsloth Desktop

How to use MISHANM/meta-Llama-3.2-3B-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 MISHANM/meta-Llama-3.2-3B-Instruct.gguf

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "MISHANM/meta-Llama-3.2-3B-Instruct.gguf"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use MISHANM/meta-Llama-3.2-3B-Instruct.gguf with Docker Model Runner:
```
docker model run hf.co/MISHANM/meta-Llama-3.2-3B-Instruct.gguf
```

Lemonade

How to use MISHANM/meta-Llama-3.2-3B-Instruct.gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MISHANM/meta-Llama-3.2-3B-Instruct.gguf

Run and chat with the model

lemonade run user.meta-Llama-3.2-3B-Instruct.gguf-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use MISHANM/meta-Llama-3.2-3B-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 MISHANM/meta-Llama-3.2-3B-Instruct.gguf

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 MISHANM/meta-Llama-3.2-3B-Instruct.gguf

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use MISHANM/meta-Llama-3.2-3B-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 MISHANM/meta-Llama-3.2-3B-Instruct.gguf

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 "MISHANM/meta-Llama-3.2-3B-Instruct.gguf" \
  --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"

MISHANM/meta-Llama-3.2-3B-Instruct.gguf

This model is a GGUF version of the meta-llama/Llama-3.2-3B-Instruct model, optimized for use with the llama.cpp framework. It is designed to run efficiently on CPUs and can be used for various natural language processing tasks.

Model Details

Language: English
Tasks: Text generation
Base Model:meta-llama/Llama-3.2-3B-Instruct

Building and Running the Model

To build and run the model using llama.cpp, follow these steps:

Build llama.cpp Locally

git clone https://github.com/ggerganov/llama.cpp  
cd llama.cpp  
cmake -B build  
cmake --build build --config Release

Run the Model

Navigate to the build directory and run the model with a prompt:

cd llama.cpp/build/bin

Inference with llama.cpp

./llama-cli -m /path/to/model/ -p "Your prompt here" -n 128

Citation Information

@misc{MISHANM/meta-Llama-3.2-3B-Instruct.gguf,
  author = {Mishan Maurya},
  title = {Introducing MISHANM/meta-Llama-3.2-3B-Instruct.gguf GGUF Model},
  year = {2025},
  publisher = {Hugging Face},
  journal = {Hugging Face repository},
  
}

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Architecture

llama

Hardware compatibility

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