Instructions to use ycros/BagelMIsteryTour-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/BagelMIsteryTour-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/BagelMIsteryTour-8x7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ycros/BagelMIsteryTour-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/BagelMIsteryTour-8x7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ycros/BagelMIsteryTour-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/BagelMIsteryTour-8x7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ycros/BagelMIsteryTour-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/BagelMIsteryTour-8x7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ycros/BagelMIsteryTour-8x7B-GGUF:Q4_K_M

Use Docker

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

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

Unsloth Studio

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

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Atomic Chat

BagelMIsteryTour-8x7B-GGUF

New and improved GGUF quantized versions can be found at https://huggingface.co/Artefact2/BagelMIsteryTour-v2-8x7B-GGUF instead.

These are GGUF quantized versions of BagelMIsteryTour-8x7B

Bagel, Mixtral Instruct, with extra spices. Give it a taste. Works with Alpaca prompt formats, though the Mistral format should also work.

I started experimenting around seeing if I could improve or fix some of Bagel's problems. Totally inspired by seeing how well Doctor-Shotgun's Mixtral-8x7B-Instruct-v0.1-LimaRP-ZLoss worked (which is a LimaRP tune on top of base Mixtral, and then merged with Mixtral Instruct) - I decided to try some merges of Bagel with Mixtral Instruct as a result.

Somehow I ended up here, Bagel, Mixtral Instruct, a little bit of LimaRP, a little bit of Sao10K's Sensualize. So far in my testing it's working very well, and while it seems fairly unaligned on a lot of stuff, it's maybe a little too aligned on a few specific things (which I think comes from Sensualize) - so that's something to play with in the future, or maybe try to DPO out.

I've been running (temp last) minP 0.1, dynatemp 0.5-4, rep pen 1.02, rep range 1024. I've been testing Alpaca style Instruction/Response, and Instruction/Input/Response and those seem to work well, I expect Mistral's prompt format would also work well. You may need to add a stopping string on "{{char}}:" for RPs because it can sometimes duplicate those out in responses and waffle on. Seems to hold up and not fall apart at long contexts like Bagel and some other Mixtral tunes seem to, definitely doesn't seem prone to loopyness either. Can be pushed into extravagant prose if the scene/setting calls for it.

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: Sao10K/Sensualize-Mixtral-bf16
    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