Instructions to use ycros/DonutHole-8x7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ycros/DonutHole-8x7B-GGUF 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 ycros/DonutHole-8x7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ycros/DonutHole-8x7B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ycros/DonutHole-8x7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ycros/DonutHole-8x7B-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 ycros/DonutHole-8x7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ycros/DonutHole-8x7B-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 ycros/DonutHole-8x7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ycros/DonutHole-8x7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ycros/DonutHole-8x7B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use ycros/DonutHole-8x7B-GGUF with Ollama:
```
ollama run hf.co/ycros/DonutHole-8x7B-GGUF:Q4_K_M
```

Unsloth Studio

How to use ycros/DonutHole-8x7B-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 ycros/DonutHole-8x7B-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 ycros/DonutHole-8x7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ycros/DonutHole-8x7B-GGUF to start chatting

Docker Model Runner
How to use ycros/DonutHole-8x7B-GGUF with Docker Model Runner:
```
docker model run hf.co/ycros/DonutHole-8x7B-GGUF:Q4_K_M
```

Lemonade

How to use ycros/DonutHole-8x7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ycros/DonutHole-8x7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.DonutHole-8x7B-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

DonutHole-8x7B

These are GGUF quantized versions of DonutHole-8x7B.

Bagel, Mixtral Instruct, Holodeck, LimaRP.

What mysteries lie in the hole of a donut?

Good with Alpaca prompt formats, also works with Mistral format. See usage details below.

This is similar to BagelMIsteryTour, but I've swapped out Sensualize for the new Holodeck. I'm not sure if it's better or not yet, or how it does at higher (8k+) contexts just yet.

Similar sampler advice applies as for BMT: minP (0.07 - 0.3 to taste) -> temp (either dynatemp 0-4ish, or like a temp of 3-4 with a smoothing factor of around 2.5ish). And yes, that's temp last. It does okay without rep pen up to a point, it doesn't seem to get into a complete jam, but it can start to repeat sentences, so you'll probably need some, perhaps 1.02-1.05 at a 1024 range seems okayish. (rep pen sucks, but there are better things coming).

I've mainly tested with LimaRP style Alpaca prompts (instruction/input/response), and briefly with Mistral's own format.

Full credit to all the model and dataset authors, I am but a derp with compute and a yaml file.

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

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using mistralai/Mixtral-8x7B-v0.1 as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

base_model: mistralai/Mixtral-8x7B-v0.1
models:
  - model: mistralai/Mixtral-8x7B-v0.1+Doctor-Shotgun/limarp-zloss-mixtral-8x7b-qlora
    parameters:
      density: 0.5
      weight: 0.2
  - model: KoboldAI/Mixtral-8x7B-Holodeck-v1
    parameters:
      density: 0.5
      weight: 0.2
  - model: mistralai/Mixtral-8x7B-Instruct-v0.1
    parameters:
      density: 0.6
      weight: 1.0
  - model: jondurbin/bagel-dpo-8x7b-v0.2
    parameters:
      density: 0.6
      weight: 0.5
merge_method: dare_ties
dtype: bfloat16