Instructions to use kaitchup/Mayonnaise-4in1-022 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kaitchup/Mayonnaise-4in1-022 with Transformers:

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

pipe = pipeline("text-generation", model="kaitchup/Mayonnaise-4in1-022")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kaitchup/Mayonnaise-4in1-022")
model = AutoModelForCausalLM.from_pretrained("kaitchup/Mayonnaise-4in1-022")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use kaitchup/Mayonnaise-4in1-022 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kaitchup/Mayonnaise-4in1-022"
# 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-4in1-022",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/kaitchup/Mayonnaise-4in1-022

SGLang

How to use kaitchup/Mayonnaise-4in1-022 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-4in1-022" \
    --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-4in1-022",
		"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-4in1-022" \
        --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-4in1-022",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kaitchup/Mayonnaise-4in1-022 with Docker Model Runner:
```
docker model run hf.co/kaitchup/Mayonnaise-4in1-022
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Warning: This model is ranked first on the Open LLM Leaderboard (among the 7B models) (January 28th, 2024). However, note that this model was produced from many merges. I didn't fine-tune any of the models that I merged and I couldn't confirm that none of them have been trained on the evaluation benchmarks.

Model Card for Model ID

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

Model Details

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: FelixChao/WestSeverus-7B-DPO-v2
    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

Downloads last month: 74

Safetensors

Model size

7B params

Tensor type

F16

Model tree for kaitchup/Mayonnaise-4in1-022

Merges

3 models

Quantizations