Instructions to use QuantFactory/MN-12B-Mag-Mell-R1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QuantFactory/MN-12B-Mag-Mell-R1-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("QuantFactory/MN-12B-Mag-Mell-R1-GGUF", dtype="auto") - llama-cpp-python
How to use QuantFactory/MN-12B-Mag-Mell-R1-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="QuantFactory/MN-12B-Mag-Mell-R1-GGUF", filename="MN-12B-Mag-Mell-R1.Q4_0.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/MN-12B-Mag-Mell-R1-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/MN-12B-Mag-Mell-R1-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/MN-12B-Mag-Mell-R1-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/MN-12B-Mag-Mell-R1-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/MN-12B-Mag-Mell-R1-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/MN-12B-Mag-Mell-R1-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/MN-12B-Mag-Mell-R1-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/MN-12B-Mag-Mell-R1-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/MN-12B-Mag-Mell-R1-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/MN-12B-Mag-Mell-R1-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use QuantFactory/MN-12B-Mag-Mell-R1-GGUF with Ollama:
ollama run hf.co/QuantFactory/MN-12B-Mag-Mell-R1-GGUF:Q4_K_M
- Unsloth Studio new
How to use QuantFactory/MN-12B-Mag-Mell-R1-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/MN-12B-Mag-Mell-R1-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/MN-12B-Mag-Mell-R1-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/MN-12B-Mag-Mell-R1-GGUF to start chatting
- Docker Model Runner
How to use QuantFactory/MN-12B-Mag-Mell-R1-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/MN-12B-Mag-Mell-R1-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/MN-12B-Mag-Mell-R1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/MN-12B-Mag-Mell-R1-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.MN-12B-Mag-Mell-R1-GGUF-Q4_K_M
List all available models
lemonade list
QuantFactory/MN-12B-Mag-Mell-R1-GGUF
This is quantized version of inflatebot/MN-12B-Mag-Mell-R1 created using llama.cpp
Original Model Card
Welcome, brave one; you've come a long mile.
MN-12B-Mag-Mell-R1
This is a merge of pre-trained language models created using mergekit.
Merge Details
Multi-stage SLERP merge, DARE-TIES'd together. Intended to be a general purpose "Best of Nemo" model for any fictional, creative use case. Inspired by hyper-merges like Tiefighter and Umbral Mind.
Mag Mell is composed of 3 intermediate parts:
Hero (RP, kink/trope coverage): Chronos Gold, Sunrose.
Monk (Intelligence, groundedness): Bophades, Wissenschaft.
Deity (Prose, flair): Gutenberg v4, Magnum 2.5 KTO.
I've been dreaming about this merge since Nemo tunes started coming out in earnest. From our testing, Mag Mell demonstrates worldbuilding capabilities unlike any model in its class, comparable to old adventuring models like Tiefighter, and prose that exhibits minimal "slop" (not bad for no finetuning,) frequently devising electrifying metaphors that left us consistently astonished.
Use ChatML formatting. Early testing versions had a tendency to leak tokens, but this should be more or less hammered out.
I don't want to toot my own bugle though; I'm really proud of how this came out, but please leave your feedback, good or bad.
Special thanks as usual to Toaster for his feedback and Fizz for helping fund compute, as well as the KoboldAI Discord for their resources.
Merge Method
This model was merged using the DARE TIES merge method using IntervitensInc/Mistral-Nemo-Base-2407-chatml as a base.
Models Merged
The following models were included in the merge:
- IntervitensInc/Mistral-Nemo-Base-2407-chatml
- nbeerbower/mistral-nemo-bophades-12B
- nbeerbower/mistral-nemo-wissenschaft-12B
- elinas/Chronos-Gold-12B-1.0
- Fizzarolli/MN-12b-Sunrose
- nbeerbower/mistral-nemo-gutenberg-12B-v4
- anthracite-org/magnum-12b-v2.5-kto
Configuration
The following YAML configurations were used to produce this model:
Monk:
models:
- model: nbeerbower/mistral-nemo-bophades-12B
- model: nbeerbower/mistral-nemo-wissenschaft-12B
merge_method: slerp
base_model: nbeerbower/mistral-nemo-bophades-12B
parameters:
t: [0.1, 0.2, 0.4, 0.6, 0.6, 0.4, 0.2, 0.1]
dtype: bfloat16
tokenizer_source: base
Hero:
models:
- model: elinas/Chronos-Gold-12B-1.0
- model: Fizzarolli/MN-12b-Sunrose
merge_method: slerp
base_model: elinas/Chronos-Gold-12B-1.0
parameters:
t: [0.1, 0.2, 0.4, 0.6, 0.6, 0.4, 0.2, 0.1]
dtype: bfloat16
tokenizer_source: base
Deity:
models:
- model: nbeerbower/mistral-nemo-gutenberg-12B-v4
- model: anthracite-org/magnum-12b-v2.5-kto
merge_method: slerp
base_model: nbeerbower/mistral-nemo-gutenberg-12B-v4
parameters:
t: [0, 0.1, 0.2, 0.25, 0.25, 0.2, 0.1, 0]
dtype: bfloat16
tokenizer_source: base
Mag Mell:
models:
- model: monk
parameters:
density: 0.7
weight: 0.5
- model: hero
parameters:
density: 0.9
weight: 1
- model: deity
parameters:
density: 0.5
weight: 0.7
merge_method: dare_ties
base_model: IntervitensInc/Mistral-Nemo-Base-2407-chatml
tokenizer_source: base
In Irish mythology, Mag Mell (modern spelling: Magh Meall, meaning 'delightful plain') is one of the names for the Celtic Otherworld, a mythical realm achievable through death and/or glory... Never explicitly stated in any surviving mythological account to be an afterlife; rather, it is usually portrayed as a paradise populated by deities, which is occasionally visited by some adventurous mortals. In its island guise, it was visited by various legendary Irish heroes and monks, forming the basis of the adventure myth or echtrae...
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docker model run hf.co/QuantFactory/MN-12B-Mag-Mell-R1-GGUF: