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

Libraries

How to use mukel/Llama-3.2-3B-Instruct-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="mukel/Llama-3.2-3B-Instruct-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mukel/Llama-3.2-3B-Instruct-GGUF", dtype="auto")

llama-cpp-python

How to use mukel/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="mukel/Llama-3.2-3B-Instruct-GGUF",
	filename="Llama-3.2-3B-Instruct-Q4_0.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use mukel/Llama-3.2-3B-Instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mukel/Llama-3.2-3B-Instruct-GGUF:Q4_0
# Run inference directly in the terminal:
llama-cli -hf mukel/Llama-3.2-3B-Instruct-GGUF:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf mukel/Llama-3.2-3B-Instruct-GGUF:Q4_0
# Run inference directly in the terminal:
llama-cli -hf mukel/Llama-3.2-3B-Instruct-GGUF:Q4_0

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 mukel/Llama-3.2-3B-Instruct-GGUF:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf mukel/Llama-3.2-3B-Instruct-GGUF:Q4_0

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 mukel/Llama-3.2-3B-Instruct-GGUF:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mukel/Llama-3.2-3B-Instruct-GGUF:Q4_0

Use Docker

docker model run hf.co/mukel/Llama-3.2-3B-Instruct-GGUF:Q4_0

LM Studio
Jan

vLLM

How to use mukel/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 "mukel/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": "mukel/Llama-3.2-3B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mukel/Llama-3.2-3B-Instruct-GGUF:Q4_0

SGLang

How to use mukel/Llama-3.2-3B-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 "mukel/Llama-3.2-3B-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": "mukel/Llama-3.2-3B-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 "mukel/Llama-3.2-3B-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": "mukel/Llama-3.2-3B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use mukel/Llama-3.2-3B-Instruct-GGUF with Ollama:
```
ollama run hf.co/mukel/Llama-3.2-3B-Instruct-GGUF:Q4_0
```

Unsloth Studio new

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

Pi new

How to use mukel/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-server -hf mukel/Llama-3.2-3B-Instruct-GGUF:Q4_0

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": "mukel/Llama-3.2-3B-Instruct-GGUF:Q4_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use mukel/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-server -hf mukel/Llama-3.2-3B-Instruct-GGUF:Q4_0

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 mukel/Llama-3.2-3B-Instruct-GGUF:Q4_0

Run Hermes

hermes

Docker Model Runner
How to use mukel/Llama-3.2-3B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/mukel/Llama-3.2-3B-Instruct-GGUF:Q4_0
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mukel/Llama-3.2-3B-Instruct-GGUF:Q4_0

Run and chat with the model

lemonade run user.Llama-3.2-3B-Instruct-GGUF-Q4_0

List all available models

lemonade list

GGUF models for llama3.java

Pure .gguf Q4_0 and Q8_0 quantizations of Llama 3.2 models, ready to consume by llama3.java.

In the wild, Q8_0 quantizations are fine, but Q4_0 quantizations are rarely pure e.g. the output.weights tensor is quantized with Q6_K, instead of Q4_0.
A pure Q4_0 quantization can be generated from a high precision (F32, F16, BFLOAT16) .gguf source with the llama-quantize utility from llama.cpp as follows:

./llama-quantize --pure ./Meta-Llama-3-8B-Instruct-F32.gguf ./Meta-Llama-3-8B-Instruct-Q4_0.gguf Q4_0

Model Information

The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks.

Model Developer: Meta

Model Architecture: Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.

	Training Data	Params	Input modalities	Output modalities	Context Length	GQA	Shared Embeddings	Token count	Knowledge cutoff
Llama 3.2 (text only)	A new mix of publicly available online data.	1B (1.23B)	Multilingual Text	Multilingual Text and code	128k	Yes	Yes	Up to 9T tokens	December 2023
		3B (3.21B)	Multilingual Text	Multilingual Text and code

Supported Languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly.

Llama 3.2 Model Family: Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.

Model Release Date: Sept 25, 2024

Status: This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety.

License: Use of Llama 3.2 is governed by the Llama 3.2 Community License (a custom, commercial license agreement).

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GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

4-bit

8-bit

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Collection

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