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

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="SandLogicTechnologies/Llama-3.3-70B-Instruct-GGUF",
	filename="Llama-3.3-70B-Instruct-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use SandLogicTechnologies/Llama-3.3-70B-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 SandLogicTechnologies/Llama-3.3-70B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf SandLogicTechnologies/Llama-3.3-70B-Instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SandLogicTechnologies/Llama-3.3-70B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf SandLogicTechnologies/Llama-3.3-70B-Instruct-GGUF:Q4_K_M

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 SandLogicTechnologies/Llama-3.3-70B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf SandLogicTechnologies/Llama-3.3-70B-Instruct-GGUF:Q4_K_M

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 SandLogicTechnologies/Llama-3.3-70B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SandLogicTechnologies/Llama-3.3-70B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/SandLogicTechnologies/Llama-3.3-70B-Instruct-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use SandLogicTechnologies/Llama-3.3-70B-Instruct-GGUF with Ollama:
```
ollama run hf.co/SandLogicTechnologies/Llama-3.3-70B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio

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

How to use SandLogicTechnologies/Llama-3.3-70B-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 SandLogicTechnologies/Llama-3.3-70B-Instruct-GGUF:Q4_K_M

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": "SandLogicTechnologies/Llama-3.3-70B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use SandLogicTechnologies/Llama-3.3-70B-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 SandLogicTechnologies/Llama-3.3-70B-Instruct-GGUF:Q4_K_M

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 SandLogicTechnologies/Llama-3.3-70B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use SandLogicTechnologies/Llama-3.3-70B-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 SandLogicTechnologies/Llama-3.3-70B-Instruct-GGUF:Q4_K_M

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 "SandLogicTechnologies/Llama-3.3-70B-Instruct-GGUF:Q4_K_M" \
  --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 SandLogicTechnologies/Llama-3.3-70B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/SandLogicTechnologies/Llama-3.3-70B-Instruct-GGUF:Q4_K_M
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SandLogicTechnologies/Llama-3.3-70B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3.3-70B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Meta's Llama 3.3 70B Instruct Quantized Model

This repository contains the Q4 K-means quantized GGUF version of Meta's Llama 3.3 70B Instruct model, prepared by SandLogic Technologies. The quantization dramatically reduces the model size while preserving most of its performance, making it suitable for consumer hardware deployment.

Performance Characteristics

The Q4_KM quantization offers an excellent balance between model size, memory usage, and quality:

Preserves approximately 97-98% of the original model's performance
Reduces the model size from ~140GB (FP16) to ~39GB
Enables inference on consumer-grade GPUs with 12GB+ VRAM
Suitable for CPU inference on systems with 48GB+ RAM

Downloads last month: 29

GGUF

Model size

71B params

Architecture

llama

Hardware compatibility

4-bit

Inference Providers NEW

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Model tree for SandLogicTechnologies/Llama-3.3-70B-Instruct-GGUF

Base model

meta-llama/Llama-3.1-70B

Finetuned

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

Quantized

(158)

this model