Instructions to use InferenceIllusionist/ohno-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 InferenceIllusionist/ohno-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 InferenceIllusionist/ohno-8x7B-GGUF:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf InferenceIllusionist/ohno-8x7B-GGUF:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf InferenceIllusionist/ohno-8x7B-GGUF:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf InferenceIllusionist/ohno-8x7B-GGUF:Q5_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 InferenceIllusionist/ohno-8x7B-GGUF:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf InferenceIllusionist/ohno-8x7B-GGUF:Q5_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 InferenceIllusionist/ohno-8x7B-GGUF:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf InferenceIllusionist/ohno-8x7B-GGUF:Q5_K_M

Use Docker

docker model run hf.co/InferenceIllusionist/ohno-8x7B-GGUF:Q5_K_M

LM Studio
Jan
Ollama
How to use InferenceIllusionist/ohno-8x7B-GGUF with Ollama:
```
ollama run hf.co/InferenceIllusionist/ohno-8x7B-GGUF:Q5_K_M
```

Unsloth Studio

How to use InferenceIllusionist/ohno-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 InferenceIllusionist/ohno-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 InferenceIllusionist/ohno-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 InferenceIllusionist/ohno-8x7B-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use InferenceIllusionist/ohno-8x7B-GGUF with Docker Model Runner:
```
docker model run hf.co/InferenceIllusionist/ohno-8x7B-GGUF:Q5_K_M
```

Lemonade

How to use InferenceIllusionist/ohno-8x7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull InferenceIllusionist/ohno-8x7B-GGUF:Q5_K_M

Run and chat with the model

lemonade run user.ohno-8x7B-GGUF-Q5_K_M

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Model Card for ohno-8x7B-GGUF

Model creator: rAIfle
Original model: ohno-8x7B-fp16

ohno-8x7B quantized with love.

Upload Notes: Wanted to give this one a spin after seeing its unique merge recipe, was curious about how Mixtral-8x7B-v0.1 case-briefs affected the output.

Starting out with Q5_K_M, taking requests for any other quants. All quantizations based on original fp16 model.

Any feedback is greatly appreciated!

Original Model Card

ohno-8x7b

this... will either be my magnum opus... or terrible. no inbetweens!

Post-test verdict: It's mostly braindamaged. Might be my settings or something, idk. the ./output mentioned below is my own merge using identical recipe as Envoid/Mixtral-Instruct-ITR-8x7B.

output_merge2

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 Envoid/Mixtral-Instruct-ITR-8x7B as a base.

Models Merged

The following models were included in the merge:

./output/ + /ai/LLM/tmp/pefts/daybreak-peft/mixtral-8x7b
Envoid/Mixtral-Instruct-ITR-8x7B + Doctor-Shotgun/limarp-zloss-mixtral-8x7b-qlora
Envoid/Mixtral-Instruct-ITR-8x7B + retrieval-bar/Mixtral-8x7B-v0.1_case-briefs
NeverSleep/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: ./output/+/ai/LLM/tmp/pefts/daybreak-peft/mixtral-8x7b
    parameters:
      density: 0.66
      weight: 1.0
  - model: Envoid/Mixtral-Instruct-ITR-8x7B+retrieval-bar/Mixtral-8x7B-v0.1_case-briefs
    parameters:
      density: 0.1
      weight: 0.25
  - model: Envoid/Mixtral-Instruct-ITR-8x7B+Doctor-Shotgun/limarp-zloss-mixtral-8x7b-qlora
    parameters:
      density: 0.66
      weight: 0.5
  - model: NeverSleep/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss
    parameters:
      density: 0.15
      weight: 0.3
merge_method: dare_ties
base_model: Envoid/Mixtral-Instruct-ITR-8x7B
dtype: float16