Instructions to use Lewdiculous/llama-3-Stheno-Mahou-8B-GGUF-IQ-Imatrix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Lewdiculous/llama-3-Stheno-Mahou-8B-GGUF-IQ-Imatrix with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Lewdiculous/llama-3-Stheno-Mahou-8B-GGUF-IQ-Imatrix:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Lewdiculous/llama-3-Stheno-Mahou-8B-GGUF-IQ-Imatrix:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Lewdiculous/llama-3-Stheno-Mahou-8B-GGUF-IQ-Imatrix:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Lewdiculous/llama-3-Stheno-Mahou-8B-GGUF-IQ-Imatrix: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 Lewdiculous/llama-3-Stheno-Mahou-8B-GGUF-IQ-Imatrix:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Lewdiculous/llama-3-Stheno-Mahou-8B-GGUF-IQ-Imatrix: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 Lewdiculous/llama-3-Stheno-Mahou-8B-GGUF-IQ-Imatrix:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Lewdiculous/llama-3-Stheno-Mahou-8B-GGUF-IQ-Imatrix:Q4_K_M

Use Docker

docker model run hf.co/Lewdiculous/llama-3-Stheno-Mahou-8B-GGUF-IQ-Imatrix:Q4_K_M

LM Studio
Jan
Ollama
How to use Lewdiculous/llama-3-Stheno-Mahou-8B-GGUF-IQ-Imatrix with Ollama:
```
ollama run hf.co/Lewdiculous/llama-3-Stheno-Mahou-8B-GGUF-IQ-Imatrix:Q4_K_M
```

Unsloth Studio

How to use Lewdiculous/llama-3-Stheno-Mahou-8B-GGUF-IQ-Imatrix 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 Lewdiculous/llama-3-Stheno-Mahou-8B-GGUF-IQ-Imatrix 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 Lewdiculous/llama-3-Stheno-Mahou-8B-GGUF-IQ-Imatrix to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Lewdiculous/llama-3-Stheno-Mahou-8B-GGUF-IQ-Imatrix to start chatting

Docker Model Runner
How to use Lewdiculous/llama-3-Stheno-Mahou-8B-GGUF-IQ-Imatrix with Docker Model Runner:
```
docker model run hf.co/Lewdiculous/llama-3-Stheno-Mahou-8B-GGUF-IQ-Imatrix:Q4_K_M
```

Lemonade

How to use Lewdiculous/llama-3-Stheno-Mahou-8B-GGUF-IQ-Imatrix with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Lewdiculous/llama-3-Stheno-Mahou-8B-GGUF-IQ-Imatrix:Q4_K_M

Run and chat with the model

lemonade run user.llama-3-Stheno-Mahou-8B-GGUF-IQ-Imatrix-Q4_K_M

List all available models

lemonade list

Atomic Chat

llama-3-Stheno-Mahou-8B-GGUF-IQ-Imatrix / README.md

Lewdiculous

add base model ref

fd9f34e verified over 1 year ago

preview code

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metadata

license: apache-2.0
language:
  - en
inference: false
tags:
  - roleplay
  - llama3
  - sillytavern
base_model:
  - nbeerbower/llama-3-Stheno-Mahou-8B

#roleplay #sillytavern #llama3

My GGUF-IQ-Imatrix quants for nbeerbower/llama-3-Stheno-Mahou-8B.

"A potential precious hidden gem, will you polish this rough diamond?"

This is a merge of two very interesting models, aimed at roleplaying usage.

Personal-support:
I apologize for disrupting your experience.
Currently I'm working on moving for a better internet provider.
If you want and you are able to...
You can spare some change over here (Ko-fi).

Author-support:
You can support the author at their own page.

Quantization process:
For future reference, these quants have been done after the fixes from #6920 have been merged.
Imatrix data was generated from the FP16-GGUF and the final conversions used BF16-GGUF for the quantization process.
This was a bit more disk and compute intensive but hopefully avoided any losses during conversion.
If you noticed any issues let me know in the discussions.

General usage:
Use the latest version of KoboldCpp.
Remember that you can also use --flashattention on KoboldCpp now even with non-RTX cards for reduced VRAM usage.
For 8GB VRAM GPUs, I recommend the Q4_K_M-imat quant for up to 12288 context sizes.
For 12GB VRAM GPUs, the Q5_K_M-imat quant will give you a great size/quality balance.

Resources:
You can find out more about how each quant stacks up against each other and their types here and here, respectively.

Presets:
Some compatible SillyTavern presets can be found here (Virt's Roleplay Presets), experiment with Llama-3 and ChatML.

Original model text information:

llama-3-Stheno-Mahou-8B

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the Model Stock merge method using flammenai/Mahou-1.2-llama3-8B as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: flammenai/Mahou-1.1-llama3-8B
  - model: Sao10K/L3-8B-Stheno-v3.1
merge_method: model_stock
base_model: flammenai/Mahou-1.2-llama3-8B
dtype: bfloat16