StableLM VS alpaca_lora_4bit

https://github.com/Stability-AI/StableLM?tab=readme-ov-file#...

https://mistral.ai/news/announcing-mistral-7b/

looking at the 3b results (here https://github.com/Stability-AI/StableLM#stablelm-alpha-v2 ?), it looks like Mistral (which outperforms Llama-2 13b) is far more powerful

Does this mean Stability gave up on StableLM?

I notice that the repo hasn’t been updated since April, and a question asking for an update has been ignored for at least a month: https://github.com/Stability-AI/StableLM/issues/83

I'm not "ignoring" StableLM, if anything it's the impetus for my post. The alpha models were so bad and unusable that it seems they may have simply abandoned the project. It's clear they basically didn't know what they were doing, which is silly for a company of their size and specialization.

1) StableLM released a checkpoint at 800B for their 3B and 7B at 800B tokens with 4096 context size, but perform very poorly on different benchmarks and finetuning is discouraged with such a weak base model

It is the best open-source model currently available. Falcon-40B outperforms LLaMA, StableLM, RedPajama, MPT, etc. See the OpenLLM Leaderboard.

Someone shared this link on another recent post

Github: https://github.com/Stability-AI/StableLM

For training there is also https://github.com/johnsmith0031/alpaca_lora_4bit

I've been trying to fine tune an erotica model on some large context chat history (reverse proxy logs) and a literotica-instruct dataset I made, with a max context of 8k. The large context size eats a lot of VRAM so I've been trying to find the most efficient way to experiment considering I'd like to do multiple runs to test some ideas. So I'm going to try and use https://github.com/johnsmith0031/alpaca_lora_4bit, which is supposed to train faster and use less memory than qlora.

Follow up the popular work of u/tloen alpaca-lora, I wrapped the setup of alpaca_lora_4bit to add support for GPTQ training in form of installable pip packages. You can perform training and inference with multiple quantizations method to compare the results.

" Using LoRAs with GPTQ-for-LLaMa This requires using a monkey patch that is supported by this web UI: https://github.com/johnsmith0031/alpaca_lora_4bit"

4-bit GPTQ LoRA training was available since early April. I did not see any comparison to it in the QLoRA paper or even a mention, so it makes me think they were not aware it already existed.

https://github.com/johnsmith0031/alpaca_lora_4bit is still the SOTA - Faster than qlora, trains on a GPTQ base.

I would also like to know how one would finetune this in 4 bit? I think one could take the merged 8K PEFT with the LLaMA weights, and then quantize it to 4 bit, and then train with https://github.com/johnsmith0031/alpaca_lora_4bit ?

I was under the impression that you just git clone this repo into text-generation-webui/repositories (so you would have GPTQ_for_Llama and alpaca_lora_4bit in the folder), and then just load with monkey patch. Is that not correct? I also tried just downloading alpaca_lora_4bit on its own, git cloning text-gen-webui within it, and installing requirements.txt for both and running with monkey patch. I was following the sections of alpaca_lora_4bit, "Text Generation Webui Monkey Patch" and "monkey patch inside webui"

I dont have any familiarity with esxi, but I can say that there are quite a few posts about people doing it on proxmox. I've currently got a machine with 2x3090 passing through to VM's. When I'm training, I pass them both through to the same VM and can do lora 4-bit training on llama33 using https://github.com/johnsmith0031/alpaca_lora_4bit. Then, at inference time, I run a single card into a different VM, and have an extra card available for experimentation.