Instructions to use FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-GGUF",
	filename="llama-3.3-70b-instruct-mxfp4.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 FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-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 FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-GGUF
# Run inference directly in the terminal:
llama cli -hf FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-GGUF
# Run inference directly in the terminal:
llama cli -hf FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-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 FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-GGUF
# Run inference directly in the terminal:
./llama-cli -hf FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-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 FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-GGUF

Use Docker

docker model run hf.co/FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-GGUF

LM Studio
Jan

vLLM

How to use FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-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": "FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-GGUF

Ollama
How to use FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-GGUF with Ollama:
```
ollama run hf.co/FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-GGUF
```

Unsloth Studio

How to use FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-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 FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-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 FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-GGUF to start chatting

How to use FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-GGUF

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": "FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-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 FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-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 FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-GGUF

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-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 "FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-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"

Docker Model Runner
How to use FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-GGUF with Docker Model Runner:
```
docker model run hf.co/FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-GGUF
```

Lemonade

How to use FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-GGUF

Run and chat with the model

lemonade run user.Llama-3.3-70B-Instruct-MXFP4-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Llama 3.3 70B Instruct — MXFP4 GGUF

MXFP4 quantization of meta-llama/Llama-3.3-70B-Instruct, a 70B parameter multilingual instruction-tuned language model from Meta.

About the Model

Llama 3.3 70B Instruct is an auto-regressive transformer optimized for multilingual dialogue use cases. It outperforms many open-source and closed chat models on common industry benchmarks.

70B parameters — dense transformer architecture
128K context window with Grouped-Query Attention (GQA)
15T+ tokens pretrained on publicly available data
Multilingual — English, German, French, Italian, Portuguese, Hindi, Spanish, Thai
Tool calling support with native function calling
Text-only — no vision/multimodal capabilities

Quantization

This GGUF was quantized from Unsloth's official F16 GGUF using llama.cpp (build 537). The F16 weights were dequantized to F32 during quantization, then quantized to MXFP4 format.

MXFP4 (Microscaling FP4) uses block-wise quantization with shared exponents per block.

Files

File	Size	Description
`llama-3.3-70b-instruct-mxfp4.gguf`	~35.2 GB	MXFP4 quantized model weights

Note: This is a text-only model. No mmproj/vision files needed.

Usage

llama.cpp

# Server mode with OpenAI-compatible API
llama-server \
  -m llama-3.3-70b-instruct-mxfp4.gguf \
  -ngl 99 \
  --host 0.0.0.0 \
  --port 8080

# Direct inference
llama-cli \
  -m llama-3.3-70b-instruct-mxfp4.gguf \
  -ngl 99 \
  -p "Explain the theory of relativity in simple terms"

LM Studio

Download the GGUF file from this repository
Load the GGUF file in LM Studio
Set GPU offload layers to maximum

Architecture

Parameters: 70B (dense transformer)
Layers: 80
Hidden dimension: 8192
Attention heads: 64 (8 KV heads for GQA)
Context: 128K
Vocabulary: 128K tokens

Hardware Requirements

Minimum: 40 GB VRAM for partial offload
Recommended: 48+ GB VRAM for full GPU offload
Disk: ~35.2 GB

Benchmarks

Benchmark	Score
MMLU (CoT)	86.0
MATH (CoT)	77.0
HumanEval	88.4
GPQA Diamond	50.5
MGSM (multilingual)	91.1

License

Llama 3.3 Community License — see LICENSE.

Note: If your product/service has >700M monthly active users, you need a separate license from Meta.

Downloads last month: 58

GGUF

Model size

71B params

Architecture

llama

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for FreedomAISVR/Llama-3.3-70B-Instruct-MXFP4-GGUF

Base model

meta-llama/Llama-3.1-70B

Finetuned

meta-llama/Llama-3.3-70B-Instruct

Quantized

(158)

this model