2.4-bit Exllama v2 quant of airoboros-gpt4-1.4.1 using new quant method
Simple quantization of original model. This model should fit on a single 24 GB VRAM GPU where Exlalama v2 is supported. Should also support full 4096 context on a single GPU, without dsktop apps also running on the same GPU. Ideally, the GPU would be completely empty of any desktop or apps.
Overview
Llama 2 70b fine tune using https://huggingface.co/datasets/jondurbin/airoboros-gpt4-1.4.1
See the previous llama 65b model card for info: https://hf.co/jondurbin/airoboros-65b-gpt4-1.4
Contribute
If you're interested in new functionality, particularly a new "instructor" type to generate a specific type of training data, take a look at the dataset generation tool repo: https://github.com/jondurbin/airoboros and either make a PR or open an issue with details.
To help me with the OpenAI/compute costs:
- https://bmc.link/jondurbin
- ETH 0xce914eAFC2fe52FdceE59565Dd92c06f776fcb11
- BTC bc1qdwuth4vlg8x37ggntlxu5cjfwgmdy5zaa7pswf
Licence and usage restrictions
Base model has a custom Meta license:
- See the meta-license/LICENSE.txt file attached for the original license provided by Meta.
- See also meta-license/USE_POLICY.md and meta-license/Responsible-Use-Guide.pdf, also provided by Meta.
The fine-tuning data was generated by OpenAI API calls to gpt-4, via airoboros
The ToS for OpenAI API usage has a clause preventing the output from being used to train a model that competes with OpenAI
- what does compete actually mean here?
- these small open source models will not produce output anywhere near the quality of gpt-4, or even gpt-3.5, so I can't imagine this could credibly be considered competing in the first place
- if someone else uses the dataset to do the same, they wouldn't necessarily be violating the ToS because they didn't call the API, so I don't know how that works
- the training data used in essentially all large language models includes a significant amount of copyrighted or otherwise non-permissive licensing in the first place
- other work using the self-instruct method, e.g. the original here: https://github.com/yizhongw/self-instruct released the data and model as apache-2
I am purposingly leaving this license ambiguous (other than the fact you must comply with the Meta original license for llama-2) because I am not a lawyer and refuse to attempt to interpret all of the terms accordingly.
Your best bet is probably to avoid using this commercially due to the OpenAI API usage.
Either way, by using this model, you agree to completely indemnify me.
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