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

Libraries

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

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="steampunque/Llama-3.3-70B-Instruct-MP-GGUF",
	filename="Llama-3.3-70B-Instruct.Q3_K_H.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use steampunque/Llama-3.3-70B-Instruct-MP-GGUF with llama.cpp:

Install from brew

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

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/steampunque/Llama-3.3-70B-Instruct-MP-GGUF

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

Unsloth Studio new

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

Using HuggingFace Spaces for Unsloth

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

Pi new

How to use steampunque/Llama-3.3-70B-Instruct-MP-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf steampunque/Llama-3.3-70B-Instruct-MP-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": "steampunque/Llama-3.3-70B-Instruct-MP-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use steampunque/Llama-3.3-70B-Instruct-MP-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 steampunque/Llama-3.3-70B-Instruct-MP-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 steampunque/Llama-3.3-70B-Instruct-MP-GGUF

Run Hermes

hermes

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Mixed Precision GGUF layer quantization of Llama 3.3 70B Instruct by meta-llama

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

The hybrid quant employs different quantization levels on a per layer basis to enable both high performance and small file size at the same time. The quants employed are all K to avoid slow CPU or older GPU processing of IQ quants. Three quants are available for the model as follows:

Q3_S_H : Smallest Q3_K based quant available

  LAYER_TYPES='[
   [0 ,"Q4_K_M"],[1 ,"Q3_K_L"],[2 ,"Q3_K_M"],[3 ,"Q3_K_S"],[4 ,"Q3_K_S"],[5 ,"Q3_K_S"],[6 ,"Q3_K_S"],[7 ,"Q3_K_S"],
   [8 ,"Q3_K_S"],[9 ,"Q3_K_S"],[10,"Q3_K_S"],[11,"Q3_K_S"],[12,"Q3_K_S"],[13,"Q3_K_S"],[14,"Q3_K_S"],[15,"Q3_K_S"],
   [16,"Q3_K_S"],[17,"Q3_K_S"],[18,"Q3_K_S"],[19,"Q3_K_S"],[20,"Q3_K_S"],[21,"Q3_K_S"],[22,"Q3_K_S"],[23,"Q3_K_S"],
   [24,"Q3_K_S"],[25,"Q3_K_S"],[26,"Q3_K_S"],[27,"Q3_K_S"],[28,"Q3_K_S"],[29,"Q3_K_S"],[30,"Q3_K_S"],[31,"Q3_K_S"],
   [32,"Q3_K_S"],[33,"Q3_K_S"],[34,"Q3_K_S"],[35,"Q3_K_S"],[36,"Q3_K_S"],[37,"Q3_K_S"],[38,"Q3_K_S"],[39,"Q3_K_S"],
   [40,"Q3_K_M"],[41,"Q3_K_S"],[42,"Q3_K_M"],[43,"Q3_K_S"],[44,"Q3_K_M"],[45,"Q3_K_S"],[46,"Q3_K_M"],[47,"Q3_K_S"],
   [48,"Q3_K_M"],[49,"Q3_K_S"],[50,"Q3_K_M"],[51,"Q3_K_S"],[52,"Q3_K_M"],[53,"Q3_K_S"],[54,"Q3_K_M"],[55,"Q3_K_S"],
   [56,"Q3_K_M"],[57,"Q3_K_S"],[58,"Q3_K_M"],[59,"Q3_K_S"],[60,"Q3_K_M"],[61,"Q3_K_S"],[62,"Q3_K_M"],[63,"Q3_K_S"],
   [64,"Q3_K_M"],[65,"Q3_K_M"],[66,"Q3_K_M"],[67,"Q3_K_M"],[68,"Q3_K_M"],[69,"Q3_K_M"],[70,"Q3_K_M"],[71,"Q3_K_M"],
   [72,"Q3_K_M"],[73,"Q3_K_M"],[74,"Q3_K_M"],[75,"Q3_K_M"],[76,"Q3_K_M"],[77,"Q3_K_L"],[78,"Q4_K_S"],[79,"Q4_K_M"]
   ]'
  FLAGS="--token-embedding-type Q4_K --output-tensor-type Q5_K --layer-types-high"

Q3_K_H : Slightly larger Q3_K based quant

   LAYER_TYPES='[
   [0 ,"Q4_K_M"],[1 ,"Q3_K_L"],[2 ,"Q3_K_M"],[3 ,"Q3_K_M"],[4 ,"Q3_K_S"],[5 ,"Q3_K_M"],[6 ,"Q3_K_S"],[7 ,"Q3_K_M"],
   [8 ,"Q3_K_S"],[9 ,"Q3_K_M"],[10,"Q3_K_S"],[11,"Q3_K_M"],[12,"Q3_K_S"],[13,"Q3_K_M"],[14,"Q3_K_S"],[15,"Q3_K_M"],
   [16,"Q3_K_M"],[17,"Q3_K_S"],[18,"Q3_K_M"],[19,"Q3_K_S"],[20,"Q3_K_M"],[21,"Q3_K_S"],[22,"Q3_K_M"],[23,"Q3_K_S"],
   [24,"Q3_K_M"],[25,"Q3_K_S"],[26,"Q3_K_M"],[27,"Q3_K_S"],[28,"Q3_K_M"],[29,"Q3_K_S"],[30,"Q3_K_M"],[31,"Q3_K_S"],
   [32,"Q3_K_M"],[33,"Q3_K_S"],[34,"Q3_K_M"],[35,"Q3_K_S"],[36,"Q3_K_M"],[37,"Q3_K_S"],[38,"Q3_K_M"],[39,"Q3_K_S"],
   [40,"Q3_K_M"],[41,"Q3_K_S"],[42,"Q3_K_M"],[43,"Q3_K_S"],[44,"Q3_K_M"],[45,"Q3_K_S"],[46,"Q3_K_M"],[47,"Q3_K_S"],
   [48,"Q3_K_M"],[49,"Q3_K_S"],[50,"Q3_K_M"],[51,"Q3_K_S"],[52,"Q3_K_M"],[53,"Q3_K_S"],[54,"Q3_K_M"],[55,"Q3_K_S"],
   [56,"Q3_K_M"],[57,"Q3_K_S"],[58,"Q3_K_M"],[59,"Q3_K_S"],[60,"Q3_K_M"],[61,"Q3_K_S"],[62,"Q3_K_M"],[63,"Q3_K_S"],
   [64,"Q3_K_M"],[65,"Q3_K_M"],[66,"Q3_K_M"],[67,"Q3_K_M"],[68,"Q3_K_M"],[69,"Q3_K_M"],[70,"Q3_K_M"],[71,"Q3_K_M"],
   [72,"Q3_K_M"],[73,"Q3_K_M"],[74,"Q3_K_M"],[75,"Q3_K_M"],[76,"Q3_K_L"],[77,"Q3_K_L"],[78,"Q4_K_S"],[79,"Q4_K_M"]
   ]'
  FLAGS="--token-embedding-type Q4_K --output-tensor-type Q5_K --layer-types-high"

Q4_K_H : Largest and best performance quant

  LAYER_TYPES='[
   [0 ,"Q4_K_M"],[1 ,"Q4_K_M"],[2 ,"Q4_K_S"],[3 ,"Q4_K_S"],[4 ,"Q3_K_M"],[5 ,"Q3_K_L"],[6 ,"Q3_K_M"],[7 ,"Q3_K_L"],
   [8 ,"Q3_K_M"],[9 ,"Q3_K_L"],[10,"Q3_K_M"],[11,"Q3_K_L"],[12,"Q3_K_M"],[13,"Q3_K_L"],[14,"Q3_K_M"],[15,"Q3_K_L"],
   [16,"Q3_K_L"],[17,"Q3_K_M"],[18,"Q3_K_L"],[19,"Q3_K_M"],[20,"Q3_K_L"],[21,"Q3_K_M"],[22,"Q3_K_L"],[23,"Q3_K_M"],
   [24,"Q3_K_L"],[25,"Q3_K_M"],[26,"Q3_K_L"],[27,"Q3_K_M"],[28,"Q3_K_L"],[29,"Q3_K_M"],[30,"Q3_K_L"],[31,"Q3_K_M"],
   [32,"Q3_K_L"],[33,"Q3_K_M"],[34,"Q3_K_L"],[35,"Q3_K_M"],[36,"Q3_K_L"],[37,"Q3_K_M"],[38,"Q3_K_L"],[39,"Q3_K_M"],
   [40,"Q3_K_L"],[41,"Q3_K_M"],[42,"Q3_K_L"],[43,"Q3_K_M"],[44,"Q3_K_L"],[45,"Q3_K_M"],[46,"Q3_K_L"],[47,"Q3_K_M"],
   [48,"Q3_K_L"],[49,"Q3_K_M"],[50,"Q3_K_L"],[51,"Q3_K_M"],[52,"Q3_K_L"],[53,"Q3_K_M"],[54,"Q3_K_L"],[55,"Q3_K_M"],
   [56,"Q3_K_L"],[57,"Q3_K_M"],[58,"Q3_K_L"],[59,"Q3_K_M"],[60,"Q3_K_L"],[61,"Q3_K_M"],[62,"Q3_K_L"],[63,"Q3_K_M"],
   [64,"Q4_K_S"],[65,"Q3_K_L"],[66,"Q4_K_S"],[67,"Q3_K_L"],[68,"Q4_K_S"],[69,"Q3_K_L"],[70,"Q4_K_S"],[71,"Q3_K_L"],
   [72,"Q4_K_S"],[73,"Q4_K_S"],[74,"Q4_K_M"],[75,"Q4_K_S"],[76,"Q4_K_M"],[77,"Q5_K_S"],[78,"Q5_K_M"],[79,"Q6_K"  ]
   ]'
   FLAGS="--token-embedding-type Q4_K --output-tensor-type Q6_K"

All three quants were optimized to maintain knowledge preservation and reasoning performance using a small set of curated test/evaluation prompts. All three quants score 100% on the eval prompts but the Q3 quants sometimes get a little goofy, giving wrong answer then correcting itself with the right one, or adding some non sequiter with the answer etc. Q4_K_H is rock solid. Note that use of Q2_K or Q2_K_S was not possible with this model since any Q2 use even at deep layers threw the model immediately into either noncoherence or large knowledge loss.

Comparison:

Quant	size	PPL	Comment
Q3_S_H	32.6e9	4.8	Q3_K dominant with Q4_K embedding
Q3_K_H	33.4e9	4.8	" "
Q3_K_M	34.3e9	4.9	Fails parts of eval prompt set
Q4_K_H	37.5e9	4.5	Best available quant
IQ4_XS	38.3e9	4.4	Q4_K embedding Q6_K output

Usage:

This model may be used together with fixie-ai ultravox-v0_5-llama-3_3-70b or ultravox-v0_6-llama-3_3-70b to enable it to process audio (.mp3 and .wav files) and text inputs and generate text outputs. The mmproj file are made available here: https://huggingface.co/steampunque/ultravox-v0_5-llama-3_3-70b-Hybrid-GGUF , https://huggingface.co/steampunque/ultravox-v0_6-llama-3_3-70b-MP-GGUF More information about running multimedia may be found in the docs in the mtmd readme in the tools directory of the llama.cpp source tree https://github.com/ggml-org/llama.cpp/blob/master/tools/mtmd/README.md.

Benchmarks:

A partial set of benchmarks for the model will eventually be given here: https://huggingface.co/spaces/steampunque/benchlm

Download the file from below:

Link	Type	Size/e9 B	Notes
Llama-3.3-70B-Instruct.Q3_S_H.gguf	Q3_S_H	32.6e9 B	1.7B smaller than Q3_K_M
Llama-3.3-70B-Instruct.Q3_K_H.gguf	Q3_K_H	33.4e9 B	0.9B smaller than Q3_K_M
Llama-3.3-70B-Instruct.Q4_K_H.gguf	Q4_K_H	37.5e9 B	0.8B smaller than IQ4_XS
ultravox-v0_5-llama-3_3-70b.mmproj.gguf	mmproj	1.38e9 B	multimedia projector
ultravox-v0_6-llama-3_3-70b.mmproj.gguf	mmproj	1.38e9 B	multimedia projector