llm-foundry VS chat-ui

Related comment from gwern: https://news.ycombinator.com/item?id=38438859

Also - why qlora rather than a full finetune? Using LambdaLabs, It'd cost roughly the same as your quote. Cheaper I think if you're willing to gamble with fp8: https://github.com/mosaicml/llm-foundry/tree/main/scripts/tr.... And fewer hyperparameters to tune as well

Traditionally, training runs can "explode" and fail, but there are methods to incrementally back them up and resume when that happens, see https://www.mosaicml.com/blog/mpt-7b

MosaicML released the MPT-7B model, which has a context of 60k tokens, thanks to the ALiBi position encoding.

Most AI runners just ignore licensing and run LLaMA finetunes.

But if you want to avoid the non commercial LLaMA license, you have 3 good options for a base model.

- OpenLlama 13B

- MPT 30B

- Falcon 40B

Of these, Falcon 40B is very difficult to run (slow in 4 bit, basically requires a professional GPU, no good cpu offloading yet).

OpenLLaMA 13B only supports a context size of 2048 as of today... But that could change soon.

So you probably want MPT instruct 30B, specifically this one:

https://huggingface.co/TheBloke/mpt-30B-instruct-GGML

As the page says, you can try it out on a decent PC of your own with the OpenCL build of KoboldCPP. Change it to "instruct" mode, use the template on the page, offload as many layers as you can to your PC's dGPU, and run it in instruct mode. It may already work for your summarization needs.

If not, you can finetune it with MPT's code and summarization d

https://github.com/mosaicml/llm-foundry

Or train OpenLLaMA 13B with SuperHOT + summarization data using QLORA.

BTW. they finally merged a MPT patch to work with lora: https://github.com/mosaicml/llm-foundry/issues/304

Did you run into this error while using qlora on MPT30b?: https://github.com/mosaicml/llm-foundry/issues/413

Yes? Their github is under Apache, their base model is under apache, the training data is not theirs, and they provide scripts how to convert it for the pretrain step. They have scripts for pretraining and finetuning as well. Basically for everything.

mosaicml/llm-foundry: LLM training code for MosaicML foundation models (github.com)

MosaicML, a company that provides a platform for training and deploying large language models (LLMs), has recently released its second open-source foundation model called MPT-30B. The model is part of the MosaicML Foundation Series and comes after the smaller MPT-7B model that was launched in May 2023.

Zephyr 141B is a Mixtral 8x22B fine-tune. Here are some interesting details

- Base model: Mixtral 8x22B, 8 experts, 141B total params, 35B activated params

- Fine-tuned with ORPO, a new alignment algorithm with no SFT step (hence much faster than DPO/PPO)

- Trained with 7K open data instances -> high-quality, synthetic, multi-turn

- Apache 2

Everything is open:

- Final Model: https://huggingface.co/HuggingFaceH4/zephyr-orpo-141b-A35b-v...

- Base Model: https://huggingface.co/mistral-community/Mixtral-8x22B-v0.1

- Fine-tune data: https://huggingface.co/datasets/argilla/distilabel-capybara-...

- Recipe/code to train the model: https://huggingface.co/datasets/argilla/distilabel-capybara-...

- Open-source inference engine: https://github.com/huggingface/text-generation-inference

- Open-source UI code https://github.com/huggingface/chat-ui

Have fun!

As long as you have a free Hugging Face account, you can sign up and exploit HuggingChat, a web-based chat interface where you will find 5 large language models to play with (Mixtral-7B-it v0.1 and v0.2, Command R plus, Gemma 1.1-7B-it, Dolphin). You will also have the possibility to exploit several assistants made by the Hugging Face community, or even create your own!

I submitted something about this the other day (and it got flagged)- poked around a little bit and the only interesting thing I could find is this: https://github.com/huggingface/chat-ui/issues/254 and I don't really even understand what it is, it references the stuff the dude who wrote this is discussing. I had kinda written the whole thing off as someone with too much time on their hands and is just f'ing around with stuff for whatever reason.

I think they made this as well: https://chat.openai.com/g/g-KT4gusP3Y-a-l-i-s-t-a-i-r-e-earl... - it doesn't seem very useful.

*¯\_(ツ)_/¯ to me after spending an hr or so poking around, it seemed like a bored modern tech savvy young person playing around.

- https://github.com/huggingface/chat-ui

Hugging Face Chat - open-source repo powering Hugging Chat!

If you don't want to do coding, I think HuggingFace's chat-ui can come in handy with web retrieval RAG and llama-cpp running as a server. Please check their documentation on how to setup( See "Running your own models using a custom endpoint" section on their Github).

yes! it's proprietary, invasive, and harvests your data and use it for improving the AI, Ultman went to Israel weeks after Chatgpt was introduced, Israel like any other tech-giant-country needs to make sure that it has control over that data and/or use it to achieve its goals, so it's better to find offline FOSS alternatives (if you have a decent enough PC) or use HuggingChat as an online FOSS alternative, I find it better than GPT 3.5 in many aspects

I have categorized some of the smartphone brands by their parent company using HuggingChat based on RLHF, Google's Bard, ChatGPT, and Perplexity. All of them are powered by LLMs, and both ChatGPT and Perplexity use GPT-3.5.

I'm looking for something like https://huggingface.co/chat/ or OpenAssistant, but it should target OpenAI's api.