Instructions to use BabaK07/Llama-3.3-70B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BabaK07/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="BabaK07/Llama-3.3-70B-Instruct-GGUF",
	filename="Llama-3.3-70B-Instruct-IQ3_XS.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 BabaK07/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 BabaK07/Llama-3.3-70B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf BabaK07/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 BabaK07/Llama-3.3-70B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf BabaK07/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 BabaK07/Llama-3.3-70B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf BabaK07/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 BabaK07/Llama-3.3-70B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf BabaK07/Llama-3.3-70B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/BabaK07/Llama-3.3-70B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use BabaK07/Llama-3.3-70B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BabaK07/Llama-3.3-70B-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": "BabaK07/Llama-3.3-70B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BabaK07/Llama-3.3-70B-Instruct-GGUF:Q4_K_M

Ollama
How to use BabaK07/Llama-3.3-70B-Instruct-GGUF with Ollama:
```
ollama run hf.co/BabaK07/Llama-3.3-70B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use BabaK07/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 BabaK07/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 BabaK07/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 BabaK07/Llama-3.3-70B-Instruct-GGUF to start chatting

How to use BabaK07/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 BabaK07/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": "BabaK07/Llama-3.3-70B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use BabaK07/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 BabaK07/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 BabaK07/Llama-3.3-70B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use BabaK07/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 BabaK07/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 "BabaK07/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 BabaK07/Llama-3.3-70B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/BabaK07/Llama-3.3-70B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use BabaK07/Llama-3.3-70B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull BabaK07/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

Llamacpp imatrix Quantizations of Llama-3.3-70B-Instruct

Using llama.cpp release b4273 for quantization.

Original model: https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct

Run them in LM Studio

Prompt format

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

Cutting Knowledge Date: December 2023
Today Date: 26 Jul 2024

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>

Download a file (not the whole branch) from below:

Filename	Quant type	File Size	Split	Description
Llama-3.3-70B-Instruct-Q5_K_M.gguf	Q5_K_M	49.9 GB	false	High quality, recommended.
Llama-3.3-70B-Instruct-Q4_K_M.gguf	Q4_K_M	42.5 GB	false	Good quality, default size for most use cases, recommended.
Llama-3.3-70B-Instruct-IQ3_XS.gguf	IQ3_XS	29.3 GB	false	Lower quality, new method with decent performance, slightly better than Q3_K_S.

Downloading using huggingface-cli

Click to view download instructions

First, make sure you have hugginface-cli installed:

pip install -U "huggingface_hub[cli]"

Then, you can target the specific file you want:

huggingface-cli download BabaK07/Llama-3.3-70B-Instruct-GGUF --include "Llama-3.3-70B-Instruct-Q4_K_M.gguf" --local-dir ./

Downloads last month: 59

GGUF

Model size

71B params

Architecture

llama

Hardware compatibility

3-bit

4-bit

5-bit

Model tree for BabaK07/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

Collection including BabaK07/Llama-3.3-70B-Instruct-GGUF

Quantized Models

Collection

2 items • Updated Feb 12, 2025