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

Libraries

How to use Renesas/Llama-3.2-3B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Renesas/Llama-3.2-3B-Instruct-GGUF",
	filename="fp16/Llama-3.2-3B-Instruct-f16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Renesas/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 Renesas/Llama-3.2-3B-Instruct-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf Renesas/Llama-3.2-3B-Instruct-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Renesas/Llama-3.2-3B-Instruct-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf Renesas/Llama-3.2-3B-Instruct-GGUF:F16

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 Renesas/Llama-3.2-3B-Instruct-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf Renesas/Llama-3.2-3B-Instruct-GGUF:F16

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 Renesas/Llama-3.2-3B-Instruct-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Renesas/Llama-3.2-3B-Instruct-GGUF:F16

Use Docker

docker model run hf.co/Renesas/Llama-3.2-3B-Instruct-GGUF:F16

LM Studio
Jan

vLLM

How to use Renesas/Llama-3.2-3B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Renesas/Llama-3.2-3B-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": "Renesas/Llama-3.2-3B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Renesas/Llama-3.2-3B-Instruct-GGUF:F16

Ollama
How to use Renesas/Llama-3.2-3B-Instruct-GGUF with Ollama:
```
ollama run hf.co/Renesas/Llama-3.2-3B-Instruct-GGUF:F16
```

Unsloth Studio

How to use Renesas/Llama-3.2-3B-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 Renesas/Llama-3.2-3B-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 Renesas/Llama-3.2-3B-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 Renesas/Llama-3.2-3B-Instruct-GGUF to start chatting

How to use Renesas/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 Renesas/Llama-3.2-3B-Instruct-GGUF:F16

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": "Renesas/Llama-3.2-3B-Instruct-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Renesas/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 Renesas/Llama-3.2-3B-Instruct-GGUF:F16

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 Renesas/Llama-3.2-3B-Instruct-GGUF:F16

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Renesas/Llama-3.2-3B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/Renesas/Llama-3.2-3B-Instruct-GGUF:F16
```

Lemonade

How to use Renesas/Llama-3.2-3B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Renesas/Llama-3.2-3B-Instruct-GGUF:F16

Run and chat with the model

lemonade run user.Llama-3.2-3B-Instruct-GGUF-F16

List all available models

lemonade list

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Llama-3.2-3B-Instruct - Renesas X5H

Introduction

This repository contains Llama-3.2-3B-Instruct model, optimized for Renesas X5H platform for text-generation inference.

Model Architecture: Llama 3.2-3B is an auto-regressive language model that uses an optimized transformer architecture.

Model Summary:

Parameter	Llama-3.2-3B-Instruct
NUM_LAYERS	28
HIDDEN_SIZE	3072
FFN_DIM	8192
NUM_HEADS	24
NUM_KV_HEADS	8
HEAD_DIM	128
GROUP_SIZE	3
VOCAB_SIZE	128256
RMS_NORM_EPS	1e-5
ROPE_THETA	500000.0

Source Model: meta-llama/Llama-3.2-3B-Instruct

Performance

The following performance metrics were measured with a prompt.

Model	Precision	Device	Response Rate (tokens/sec)
Llama-3.2-3B-Instruct	FP16	NPX6	8.76
Llama-3.2-3B-Instruct	W4A16	NPX6	17.46

Prerequisites

To run model, you need:

Renesas X5H Board with SDK v4.32.0
Hugging Face CLI: For downloading the model and installer.

Deployment

Download the installer in any linux PC from Files and versions tab "llama3p2-3b-runner-0.1.0-Linux.sh"

Llama-3.2-3B-Instruct (W4A16)

Download the installer llama3p2-3b-w4a16-runner-0.1.0-Linux.sh from Files and version tab under w4a16\binaries\rcar-x5hv1\xOS_v4. folder.

Copy the installer to the X5H board and run the installer.

bash ./llama3p2-3b-w4a16-runner-0.1.0-Linux.sh --prefix=./ --exclude-subdir --skip-license

Download GGUF Llama-3.2-3B-Instruct-f16.gguf from Files and version tab under fp16 folder and copy to the installed directory on the X5H board.

Expected directory structure on the X5H board.

 llama3p2-3b-w4a16-runner
 ├── Llama-3.2-3B-Instruct-f16.gguf
 ├── firmwares
 ├── kernel_modules
 ├── llama3p2-3b-w4a16
 ├── llama3p2-3b-w4a16-runner
 ├── scripts
 └── setup_npu.sh

Inference - Llama-3.2-3B-Instruct (W4A16)

bash ./setup_npu.sh
./llama3p2-3b-w4a16-runner <PROMPT>

Llama-3.2-3B-Instruct (FP16)

Download the installer llama3p2-3b-runner-0.1.0-Linux.sh from Files and version tab under fp16\binaries\rcar-x5hv1\xOS_v4. folder.

Copy the installer to the X5H board and run the installer.

bash ./llama3p2-3b-runner-0.1.0-Linux.sh --prefix=./ --exclude-subdir --skip-license

Download GGUF Llama-3.2-3B-Instruct-f16.gguf from Files and version tab under fp16 folder and copy to the installed directory on the X5H board.

Expected directory structure on the X5H board.

 llama3p2-3b-runner
 ├── Llama-3.2-3B-Instruct-f16.gguf
 ├── firmwares
 ├── kernel_modules
 ├── llama3p2-3b-runner
 ├── scripts
 └── setup_npu.sh

Inference - Llama-3.2-3B-Instruct (FP16)

bash ./setup_npu.sh
./llama3p2-3b-runner <PROMPT>

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GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

16-bit

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