Instructions to use jondurbin/airoboros-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jondurbin/airoboros-7b with Transformers:

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

pipe = pipeline("text-generation", model="jondurbin/airoboros-7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jondurbin/airoboros-7b")
model = AutoModelForCausalLM.from_pretrained("jondurbin/airoboros-7b")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jondurbin/airoboros-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jondurbin/airoboros-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jondurbin/airoboros-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/jondurbin/airoboros-7b

SGLang

How to use jondurbin/airoboros-7b 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 "jondurbin/airoboros-7b" \
    --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": "jondurbin/airoboros-7b",
		"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 "jondurbin/airoboros-7b" \
        --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": "jondurbin/airoboros-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jondurbin/airoboros-7b with Docker Model Runner:
```
docker model run hf.co/jondurbin/airoboros-7b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Overview

This is a fine-tuned 7b parameter LlaMa model, using completely synthetic training data created by https://github.com/jondurbin/airoboros

I don't recommend using this model! The outputs aren't particularly great, and it may contain "harmful" data due to jailbreak

Please see one of the updated airoboros models for a much better experience.

Training data

This was an experiment to see if a "jailbreak" prompt could be used to generate a broader range of data that would otherwise have been filtered by OpenAI's alignment efforts.

The jailbreak did indeed work with a high success rate, and caused OpenAI to generate a broader range of topics and fewer refusals to answer questions/instructions of sensitive topics.

Usage and License Notices

All airoboros models and datasets are intended and licensed for research use only. I've used the 'cc-nc-4.0' license, but really it is subject to a custom/special license because:

the base model is LLaMa, which has it's own special research license
the dataset(s) were generated with OpenAI (gpt-4 and/or gpt-3.5-turbo), which has a clausing saying the data can't be used to create models to compete with openai

So, to reiterate: this model (and datasets) cannot be used commercially.

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