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

Libraries

How to use nroggendorff/mayo with Transformers:

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

pipe = pipeline("text-generation", model="nroggendorff/mayo")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nroggendorff/mayo")
model = AutoModelForCausalLM.from_pretrained("nroggendorff/mayo")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use nroggendorff/mayo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nroggendorff/mayo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nroggendorff/mayo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nroggendorff/mayo

SGLang

How to use nroggendorff/mayo 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 "nroggendorff/mayo" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nroggendorff/mayo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "nroggendorff/mayo" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nroggendorff/mayo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nroggendorff/mayo with Docker Model Runner:
```
docker model run hf.co/nroggendorff/mayo
```

Mayonnaise LLM

Mayo is a language model fine-tuned on the Mayo dataset using Supervised Fine-Tuning (SFT) and Teacher Reinforced Learning (TRL) techniques. It is based on the Mistral 7b Model

Features

Utilizes SFT and TRL techniques for improved performance
Supports English language

Usage

To use the Mayo LLM, you can load the model using the Hugging Face Transformers library:

from transformers import pipeline

pipe = pipeline("text-generation", model="nroggendorff/mayo")

question = "What color is the sky?"
conv = [{"role": "user", "content": question}]

response = pipe(conv, max_new_tokens=32)[0]['generated_text'][-1]['content']
print(response)

License

This project is licensed under the MIT License.

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Safetensors

Model size

7B params

Tensor type

F32

Model tree for nroggendorff/mayo

Base model

mistralai/Mistral-7B-v0.3

Finetuned

mistralai/Mistral-7B-Instruct-v0.3

Quantized

(248)

this model

nroggendorff
/

mayo

Mayonnaise LLM

Features

Usage

License

Model tree for nroggendorff/mayo

Dataset used to train nroggendorff/mayo