chat-ui VS qlora

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.

Marx 3B V3 is StableLM 3B 4E1T instruction tuned on EverythingLM Data V3(ShareGPT Format) for 2 epochs using QLoRA.

https://github.com/artidoro/qlora

The tools and mechanisms to get a model to do what you want is ever so changing, ever so quickly. Build and understand a notebook yourself, and reduce dependencies. You will need to switch them.

My own project right now is still in raw generated form, and this now makes me think about trying qlora's scripts since this gives me some confidence I should be able to get it to turn out now that someone else has carved a path and charted the map. I was going to target llamatune which was mentioned here the other day.

Most papers I did read showed at least a thousand, even 10000 at several cases, so I assumed that to be the trend in the case of Low rank adapter(PEFT) training.(source: [2305.14314] QLoRA: Efficient Finetuning of Quantized LLMs (arxiv.org) , Stanford CRFM (Alpaca) and the minimum being openchat/openchat · Hugging Face ; There are a lot more examples)

4-bit quantization training mainly refers to qlora. Simply put, qlora quantizes the weights of the LLM into 4-bit for storage, while dequantizing them into 16-bit during the training process to ensure training precision. This method significantly reduces GPU memory overhead during training (the training speed should not vary much). This approach is highly suitable to be combined with parameter-efficient methods. However, the original paper was designed for single-modal LLMs and the code has already been wrapped in HuggingFace's library. Therefore, we extracted the core code from HuggingFace's library and migrated it into LaVIN's code. The main principle is to replace all linear layers in LLM with 4-bit quantized layers. Those interested can refer to our implementation in quantization.py and mm_adaptation.py, which is roughly a dozen lines of code.

There have been some new development like QLora which help fine-tune LLMs without updating all the weights.

This might be helpful: https://github.com/artidoro/qlora/issues/10

curious, since qlora paper only reports lora/qlora comparison for full fine-tuning for small 7B models.for 13B/33B/65B, it does not do so (table 4 in paper)it would be helpful if anyone can please provide links where I can read more on efficacy of lora or disadvantages of lora?

This might not be appropriate answer but did you take a look at this repository? https://github.com/artidoro/qlora With artidoro's repository it's pretty easy to train qlora. You just prepare your own dataset and run the following command: python qlora.py --model_name_or_path --dataset="path/to/your/dataset" --dataset_format="self-instruct" This is only available for several dataset formats. But every dataset format has to have input-output pairs. So the dataset json format has to be like this [ { “input”: “something ”, “output”:“something ” }, { “input”: “something ”, “output”:“something ” } ]