Instructions to use Heralax/MythoMakiseMerged-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Heralax/MythoMakiseMerged-13b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Heralax/MythoMakiseMerged-13b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Heralax/MythoMakiseMerged-13b") model = AutoModelForCausalLM.from_pretrained("Heralax/MythoMakiseMerged-13b") - llama-cpp-python
How to use Heralax/MythoMakiseMerged-13b with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Heralax/MythoMakiseMerged-13b", filename="MythoMakiseMerged-13b-q5km.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use Heralax/MythoMakiseMerged-13b with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Heralax/MythoMakiseMerged-13b:F16 # Run inference directly in the terminal: llama-cli -hf Heralax/MythoMakiseMerged-13b:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Heralax/MythoMakiseMerged-13b:F16 # Run inference directly in the terminal: llama-cli -hf Heralax/MythoMakiseMerged-13b:F16
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 Heralax/MythoMakiseMerged-13b:F16 # Run inference directly in the terminal: ./llama-cli -hf Heralax/MythoMakiseMerged-13b:F16
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 Heralax/MythoMakiseMerged-13b:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Heralax/MythoMakiseMerged-13b:F16
Use Docker
docker model run hf.co/Heralax/MythoMakiseMerged-13b:F16
- LM Studio
- Jan
- vLLM
How to use Heralax/MythoMakiseMerged-13b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Heralax/MythoMakiseMerged-13b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Heralax/MythoMakiseMerged-13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Heralax/MythoMakiseMerged-13b:F16
- SGLang
How to use Heralax/MythoMakiseMerged-13b with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Heralax/MythoMakiseMerged-13b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Heralax/MythoMakiseMerged-13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Heralax/MythoMakiseMerged-13b" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Heralax/MythoMakiseMerged-13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use Heralax/MythoMakiseMerged-13b with Ollama:
ollama run hf.co/Heralax/MythoMakiseMerged-13b:F16
- Unsloth Studio
How to use Heralax/MythoMakiseMerged-13b 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 Heralax/MythoMakiseMerged-13b 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 Heralax/MythoMakiseMerged-13b to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Heralax/MythoMakiseMerged-13b to start chatting
- Docker Model Runner
How to use Heralax/MythoMakiseMerged-13b with Docker Model Runner:
docker model run hf.co/Heralax/MythoMakiseMerged-13b:F16
- Lemonade
How to use Heralax/MythoMakiseMerged-13b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Heralax/MythoMakiseMerged-13b:F16
Run and chat with the model
lemonade run user.MythoMakiseMerged-13b-F16
List all available models
lemonade list
KEY DETAILS
Prompt format: SillyTavern Base model: MythoMax-L2-13b What's new: finetuned on the script of a visual novel that was processed and revamped by GPT-4 to make ~1300 high-quality training examples. The end goal was a model that could speak like a specific character from that game, but the end result was a model that seems to excel in banter, conversation, and roleplay overall. Note: compared to the original MythoMakise-13b, this model has 33% of MythoMax-L2-13b merged back into it, so that it better retains MythoMax's intelligence with MythoMakise's personality and style. The result of this seems to be pretty good so far. Ironcially, the model seems better at roleplaying characters other than the one it was originally created to mimic.
LONG FORM
A finetune of MythoMax-13b on lines extracted from the script of Steins;Gate. Rather than simply giving the model "previous line\nline to predict" a custom script was used to group conversations into training examples. Despite being finetuned on one character's lines from one visual novel, I've found (at least in my initial testing) that the model does an excellent job of roleplaying other characters too, probably because the creative writing GPT-4 did on top of the already-well-written Steins;Gate script was very high-quality. The model might be best at roleplaying characters if the personality of that character is similar to the character it was originally made to act like.
Besides being built for RP, I bet that this model could be used in any generic conversational role. Just don't expect it to be accurate, or good at anything other than talking.
The model is not censored.
This variation has MythoMax merged back into it with 33% weighting to make it more stable and intelligent while retaining its Kurisu-ness and better personality. In my experience, this seems to be the decisive change that led to higher-quality outputs.
Prompt format
I know it's wasteful as hell, don't judge me, this is the SillyTavern prompt format (discovered using the simple proxy for ST). I finetuned the model on this so that it would perform better on that frontend.
## {{charname}}:
- You're "{{charname}}" in this never-ending roleplay with "{{user}}".
### Input:\n
[user description (note, square brackets are a part of it)]
Description of the character's personality would go here (a 'character card')
### Response:
(OOC) Understood. I will take this info into account for the roleplay. (end OOC)
### New Roleplay:
### Instruction:
#### {{char}}:
whatever the char says, this is the chat history
#### {{user}}:
whatever the user says, this is the chat history
... repeated some number of times ...
### Response 2 paragraphs, engaging, natural, authentic, descriptive, creative):
#### {char}:
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