Instructions to use kaitchup/TheMayonnaise with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kaitchup/TheMayonnaise with Transformers:

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

pipe = pipeline("text-generation", model="kaitchup/TheMayonnaise")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kaitchup/TheMayonnaise")
model = AutoModelForCausalLM.from_pretrained("kaitchup/TheMayonnaise", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kaitchup/TheMayonnaise with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/kaitchup/TheMayonnaise

SGLang

How to use kaitchup/TheMayonnaise 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/TheMayonnaise" \
    --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/TheMayonnaise",
		"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/TheMayonnaise" \
        --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/TheMayonnaise",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kaitchup/TheMayonnaise with Docker Model Runner:
```
docker model run hf.co/kaitchup/TheMayonnaise
```
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: kaitchup/Mayonnaise-4in1-02
    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.	74.94
AI2 Reasoning Challenge (25-Shot)	73.46
HellaSwag (10-Shot)	88.46
MMLU (5-Shot)	64.88
TruthfulQA (0-shot)	69.19
Winogrande (5-shot)	84.29
GSM8k (5-shot)	69.37

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Safetensors

Model size

7B params

Tensor type

F16

Model tree for kaitchup/TheMayonnaise

Quantizations

2 models

Spaces using kaitchup/TheMayonnaise 9

Collection including kaitchup/TheMayonnaise

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

73.460
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

88.460
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

64.880
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

69.190
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

84.290
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

69.370