Instructions to use kaitchup/Mayonnaise-4in1-02 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kaitchup/Mayonnaise-4in1-02 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="kaitchup/Mayonnaise-4in1-02")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kaitchup/Mayonnaise-4in1-02")
model = AutoModelForCausalLM.from_pretrained("kaitchup/Mayonnaise-4in1-02", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use kaitchup/Mayonnaise-4in1-02 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 kaitchup/Mayonnaise-4in1-02:Q4_0
# Run inference directly in the terminal:
llama cli -hf kaitchup/Mayonnaise-4in1-02:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf kaitchup/Mayonnaise-4in1-02:Q4_0
# Run inference directly in the terminal:
llama cli -hf kaitchup/Mayonnaise-4in1-02:Q4_0

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 kaitchup/Mayonnaise-4in1-02:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf kaitchup/Mayonnaise-4in1-02:Q4_0

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 kaitchup/Mayonnaise-4in1-02:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf kaitchup/Mayonnaise-4in1-02:Q4_0

Use Docker

docker model run hf.co/kaitchup/Mayonnaise-4in1-02:Q4_0

LM Studio
Jan

vLLM

How to use kaitchup/Mayonnaise-4in1-02 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kaitchup/Mayonnaise-4in1-02"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kaitchup/Mayonnaise-4in1-02",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/kaitchup/Mayonnaise-4in1-02:Q4_0

SGLang

How to use kaitchup/Mayonnaise-4in1-02 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "kaitchup/Mayonnaise-4in1-02" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kaitchup/Mayonnaise-4in1-02",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "kaitchup/Mayonnaise-4in1-02" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kaitchup/Mayonnaise-4in1-02",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use kaitchup/Mayonnaise-4in1-02 with Ollama:
```
ollama run hf.co/kaitchup/Mayonnaise-4in1-02:Q4_0
```

Unsloth Studio

How to use kaitchup/Mayonnaise-4in1-02 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 kaitchup/Mayonnaise-4in1-02 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 kaitchup/Mayonnaise-4in1-02 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for kaitchup/Mayonnaise-4in1-02 to start chatting

Docker Model Runner
How to use kaitchup/Mayonnaise-4in1-02 with Docker Model Runner:
```
docker model run hf.co/kaitchup/Mayonnaise-4in1-02:Q4_0
```

Lemonade

How to use kaitchup/Mayonnaise-4in1-02 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull kaitchup/Mayonnaise-4in1-02:Q4_0

Run and chat with the model

lemonade run user.Mayonnaise-4in1-02-Q4_0

List all available models

lemonade list

Atomic Chat
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for Model ID

This is a mixture of experts created with mergekit and based on mistralai/Mistral-7B-v0.1.

Model Details

The model was created using a recipe detailed in this article: The Mayonnaise: Rank First on the Open LLM Leaderboard with TIES-Merging

Model Description

Developed by: The Kaitchup
Model type: Causal
Language(s) (NLP): English
License: Apache 2.0

Model Sources

Created with mergekit with this configuration:

models:
  - model: mncai/mistral-7b-dpo-v5
    # no parameters necessary for base model
  - model: flemmingmiguel/MBX-7B
    parameters:
      density: 0.5
      weight: 0.3
  - model: BarryFutureman/NeuralTurdusVariant1-7B
    parameters:
      density: 0.5
      weight: 0.5
merge_method: ties
base_model: mncai/mistral-7b-dpo-v5
parameters:
  normalize: true
dtype: float16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	75.21
AI2 Reasoning Challenge (25-Shot)	73.38
HellaSwag (10-Shot)	88.51
MMLU (5-Shot)	64.89
TruthfulQA (0-shot)	69.04
Winogrande (5-shot)	84.37
GSM8k (5-shot)	71.04

Downloads last month: 60

Safetensors

Model size

7B params

Tensor type

F16

Model tree for kaitchup/Mayonnaise-4in1-02

Quantizations

3 models

Spaces using kaitchup/Mayonnaise-4in1-02 9

Collection including kaitchup/Mayonnaise-4in1-02

The Mayonnaise

Collection

A collection of 7B models made with mergekit. • 6 items • Updated Mar 19, 2024

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

73.380
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

88.510
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

64.890
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

69.040
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

84.370
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

71.040