qlora VS wirehole

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 ” } ]

Shameless plug for an alternative?

> WireHole is a combination of WireGuard, Pi-hole, and Unbound in a docker-compose project with the intent of enabling users to quickly and easily create a personally managed full or split-tunnel WireGuard VPN with ad blocking capabilities thanks to Pi-hole, and DNS caching, additional privacy options, and upstream providers via Unbound.

https://github.com/IAmStoxe/wirehole

I have been running a home server for a little over a year now mostly for Plex and backups. I set up a Wireguard instance with unbound and pi-hole called Wirehole to remote to my services and keeping things secure on my phone and laptop at times.

https://github.com/IAmStoxe/wirehole is all you need