sparsegpt VS alpaca_lora_4bit

SparseGPT: Massive Language Models Can Be Accurately Pruned in One-Shot (https://arxiv.org/abs/2301.00774)

There is this : https://github.com/IST-DASLab/sparsegpt

Check the paper here, it's intersting: https://arxiv.org/abs/2301.00774

There's no chance that we've peeked from a bang for buck sense - we still haven't adequately investigated sparse networks.

Relevantish: https://arxiv.org/abs/2301.00774

The fact that we can reach those levels of sparseness with pruning also indicates that we're not doing a very good job of generating the initial network conditions.

Being able to come up with trainable initial settings for sparse networks across different topologies is hard, but given that we've had a degree of success with pre-trained networks, pre-training and pre-pruning might also allow for sparse networks with minimally compromised learning capabilities.

If it's possible to pre-train composable network modules, it might also be feasible to define trainable sparse networks with significantly relaxed topological constraints.

Training uses gradient descent, so you want to have good precision during that process. But once you have the overall structure of the network, https://arxiv.org/abs/2210.17323 (GPTQ) showed that you can cut down the precision quite a bit without losing a lot of accuracy. It seems you can cut down further for larger models. For the 13B Llama-based ones, going below 5 bit per parameter is noticeably worse, but for 30B models you can do 4 bits.

The same group did another paper https://arxiv.org/abs/2301.00774 which shows that in addition to reducing the precision of each parameter, you can also prune out a bunch of parameters entirely. It's harder to apply this optimization because models are usually loaded into RAM densely, but I hope someone figures out how to do it for popular models.

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.