Instructions to use QuantFactory/MythoMax-L2-13b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use QuantFactory/MythoMax-L2-13b-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="QuantFactory/MythoMax-L2-13b-GGUF", filename="MythoMax-L2-13b.Q2_K.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 QuantFactory/MythoMax-L2-13b-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf QuantFactory/MythoMax-L2-13b-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/MythoMax-L2-13b-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 QuantFactory/MythoMax-L2-13b-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/MythoMax-L2-13b-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 QuantFactory/MythoMax-L2-13b-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/MythoMax-L2-13b-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 QuantFactory/MythoMax-L2-13b-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/MythoMax-L2-13b-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/MythoMax-L2-13b-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use QuantFactory/MythoMax-L2-13b-GGUF with Ollama:
ollama run hf.co/QuantFactory/MythoMax-L2-13b-GGUF:Q4_K_M
- Unsloth Studio new
How to use QuantFactory/MythoMax-L2-13b-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 QuantFactory/MythoMax-L2-13b-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 QuantFactory/MythoMax-L2-13b-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for QuantFactory/MythoMax-L2-13b-GGUF to start chatting
- Pi new
How to use QuantFactory/MythoMax-L2-13b-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf QuantFactory/MythoMax-L2-13b-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": "QuantFactory/MythoMax-L2-13b-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use QuantFactory/MythoMax-L2-13b-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 QuantFactory/MythoMax-L2-13b-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 QuantFactory/MythoMax-L2-13b-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use QuantFactory/MythoMax-L2-13b-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/MythoMax-L2-13b-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/MythoMax-L2-13b-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/MythoMax-L2-13b-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.MythoMax-L2-13b-GGUF-Q4_K_M
List all available models
lemonade list
QuantFactory/MythoMax-L2-13b-GGUF
This is quantized version of Gryphe/MythoMax-L2-13b created using llama.cpp
Original Model Card
With Llama 3 released, it's time for MythoMax to slowly fade away... Let's do it in style!
An improved, potentially even perfected variant of MythoMix, my MythoLogic-L2 and Huginn merge using a highly experimental tensor type merge technique. The main difference with MythoMix is that I allowed more of Huginn to intermingle with the single tensors located at the front and end of a model, resulting in increased coherency across the entire structure.
The script and the acccompanying templates I used to produce both can be found here.
This model is proficient at both roleplaying and storywriting due to its unique nature.
Quantized models are available from TheBloke: GGUF - GPTQ - AWQ (You're the best!)
Model details
The idea behind this merge is that each layer is composed of several tensors, which are in turn responsible for specific functions. Using MythoLogic-L2's robust understanding as its input and Huginn's extensive writing capability as its output seems to have resulted in a model that exceeds at both, confirming my theory. (More details to be released at a later time)
This type of merge is incapable of being illustrated, as each of its 363 tensors had an unique ratio applied to it. As with my prior merges, gradients were part of these ratios to further finetune its behaviour.
Prompt Format
This model primarily uses Alpaca formatting, so for optimal model performance, use:
<System prompt/Character Card>
### Instruction:
Your instruction or question here.
For roleplay purposes, I suggest the following - Write <CHAR NAME>'s next reply in a chat between <YOUR NAME> and <CHAR NAME>. Write a single reply only.
### Response:
license: other
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