Instructions to use RDson/Llama-3-Magenta-Instruct-4x8B-MoE-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RDson/Llama-3-Magenta-Instruct-4x8B-MoE-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="RDson/Llama-3-Magenta-Instruct-4x8B-MoE-GGUF",
	filename="Llama-3-Magenta-Instruct-4x8B-MoE-F16.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 RDson/Llama-3-Magenta-Instruct-4x8B-MoE-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf RDson/Llama-3-Magenta-Instruct-4x8B-MoE-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf RDson/Llama-3-Magenta-Instruct-4x8B-MoE-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf RDson/Llama-3-Magenta-Instruct-4x8B-MoE-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf RDson/Llama-3-Magenta-Instruct-4x8B-MoE-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 RDson/Llama-3-Magenta-Instruct-4x8B-MoE-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf RDson/Llama-3-Magenta-Instruct-4x8B-MoE-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 RDson/Llama-3-Magenta-Instruct-4x8B-MoE-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RDson/Llama-3-Magenta-Instruct-4x8B-MoE-GGUF:Q4_K_M

Use Docker

docker model run hf.co/RDson/Llama-3-Magenta-Instruct-4x8B-MoE-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use RDson/Llama-3-Magenta-Instruct-4x8B-MoE-GGUF with Ollama:
```
ollama run hf.co/RDson/Llama-3-Magenta-Instruct-4x8B-MoE-GGUF:Q4_K_M
```

Unsloth Studio

How to use RDson/Llama-3-Magenta-Instruct-4x8B-MoE-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 RDson/Llama-3-Magenta-Instruct-4x8B-MoE-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 RDson/Llama-3-Magenta-Instruct-4x8B-MoE-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for RDson/Llama-3-Magenta-Instruct-4x8B-MoE-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use RDson/Llama-3-Magenta-Instruct-4x8B-MoE-GGUF with Docker Model Runner:
```
docker model run hf.co/RDson/Llama-3-Magenta-Instruct-4x8B-MoE-GGUF:Q4_K_M
```

Lemonade

How to use RDson/Llama-3-Magenta-Instruct-4x8B-MoE-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RDson/Llama-3-Magenta-Instruct-4x8B-MoE-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3-Magenta-Instruct-4x8B-MoE-GGUF-Q4_K_M

List all available models

lemonade list

GGUF files of Llama-3-Magenta-Instruct-4x8B-MoE

Llama-3-Magenta-Instruct-4x8B-MoE

You should also check out the updated Llama-3-Peach-Instruct-4x8B-MoE!

This is a experimental MoE using Mergekit, created from

Mergekit yaml file:

base_model: Meta-Llama-3-8B-Instruct
experts:
  - source_model: Meta-Llama-3-8B-Instruct
    positive_prompts:
    - "explain"
    - "chat"
    - "assistant"
    - "think"
    - "roleplay"
    - "versatile"
    - "helpful"
    - "factual"
    - "integrated"
    - "adaptive"
    - "comprehensive"
    - "balanced"
    negative_prompts:
    - "specialized"
    - "narrow"
    - "focused"
    - "limited"
    - "specific"
  - source_model: ChatQA-1.5-8B
    positive_prompts:
    - "python"
    - "math"
    - "solve"
    - "code"
    - "programming"
    negative_prompts:
    - "sorry"
    - "cannot"
    - "factual"
    - "concise"
    - "straightforward"
    - "objective"
    - "dry"
  - source_model: SFR-Iterative-DPO-LLaMA-3-8B-R
    positive_prompts:
    - "chat"
    - "assistant"
    - "AI"
    - "instructive"
    - "clear"
    - "directive"
    - "helpful"
    - "informative"
  - source_model: Llama3-8B-OpenHermes-DPO
    positive_prompts:
    - "analytical"
    - "accurate"
    - "logical"
    - "knowledgeable"
    - "precise"
    - "calculate"
    - "compute"
    - "solve"
    - "work"
    - "python"
    - "code"
    - "javascript"
    - "programming"
    - "algorithm"
    - "tell me"
    - "assistant"
    negative_prompts:
    - "creative"
    - "abstract"
    - "imaginative"
    - "artistic"
    - "emotional"
    - "mistake"
    - "inaccurate"
gate_mode: hidden
dtype: float16

Some inspiration for the Mergekit yaml file is from LoneStriker/Umbra-MoE-4x10.7-2.4bpw-h6-exl2.

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GGUF

Model size

25B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

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