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")

llama-cpp-python

How to use kaitchup/Mayonnaise-4in1-02 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="kaitchup/Mayonnaise-4in1-02",
	filename="FP16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

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

Atomic Chat new
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

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: 122

Safetensors

Model size

7B params

Tensor type

F16

Model tree for kaitchup/Mayonnaise-4in1-02

Quantizations

3 models

Spaces using kaitchup/Mayonnaise-4in1-02 8

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