alpaca-lora VS RWKV-LM

Here is a example: https://github.com/tloen/alpaca-lora/blob/main/finetune.py

In this blog post, I want to make it as simple as possible to fine-tune the LLaMA 2 - 7B model, using as little code as possible. We will be using the Alpaca Lora Training script, which automates the process of fine-tuning the model and for GPU we will be using Beam.

Implement the code in Llama LoRA repo in a script we can run locally

Hi all - relatively new to GitHub / programming in general, and I wanted to try to set up Alpaca Lora locally. Following the guide here: https://github.com/tloen/alpaca-lora

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.

If instead you want to apply a LoRa to a pytorch model, a lot of people use this script to apply to LoRa to the 16 bit model and then quantize it with a GPTQ program afterwards https://github.com/tloen/alpaca-lora/blob/main/export_hf_checkpoint.py

There are a few papers on watermarking LLM output, but from what I have seen they all use complex methods of detection to allow the watermark to go unseen by the end user, only to be detected by algorithm. I believe that a more overt system of watermarking might also be beneficial. One simple method that I have tried is character substitution. For this model, I LORA finetuned openlm-research/open_llama_3b on the alpaca_data_cleaned_archive.json dataset from https://github.com/tloen/alpaca-lora/ modified by replacing all instances of the "." character in the outputs with a "ι" The results are pretty good, with the correct the correct substitutions being generated by the model in most cases. It doesn't always work, but this was only a LORA training and for two epochs of 400 steps each, and 100% substitution isn't really required.

I am kind of new to finetuning LLM's and am not able to understand what this option exactly refers to. I guess it has the same meaning as the "train_on_inputs" parameter of alpacalora though.

Read the paper and also: https://github.com/tloen/alpaca-lora

https://github.com/BlinkDL/RWKV-LM#rwkv-discord-httpsdiscord... lists a number of implementations of various versions of RWKV.

https://github.com/BlinkDL/RWKV-LM#rwkv-parallelizable-rnn-w... :

> RWKV: Parallelizable RNN with Transformer-level LLM Performance (pronounced as "RwaKuv", from 4 major params: R W K V)

> RWKV is an RNN with Transformer-level LLM performance, which can also be directly trained like a GPT transformer (parallelizable). And it's 100% attention-free. You only need the hidden state at position t to compute the state at position t+1. You can use the "GPT" mode to quickly compute the hidden state for the "RNN" mode.

> So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding (using the final hidden state).

> "Our latest version is RWKV-6,*

That is not true. There are RNNs with transformer/LLM-like performance. See https://github.com/BlinkDL/RWKV-LM.

This is what RWKV (https://github.com/BlinkDL/RWKV-LM) was made for, and what it will be good at.

Wow. Pretty darn cool! <3 :'))))

Thanks for the support! Two weeks ago, I'd have said longer contexts on small on-device LLMs are at least a year away, but developments from last week seem to indicate that it's well within reach. Once the low hanging product features are done, I think it's a worthy problem to spend a couple of weeks or perhaps even months on. Speaking of context lengths, recurrent models like RWKV technically have infinite context lengths, but in practice the context slowly fades away after a few thousands of tokens.

This is the reason there are only a few AI labs, and they show little of the theoretical and scientific understanding you believe is required. Go check their code, there's nothing there. Even the transformer with it's heads and other architectural elements turns out to not do anything and it is less efficient than RNNs. (see https://github.com/BlinkDL/RWKV-LM)

I've been pondering the same thing, as simply extending the context window in a straightforward manner would lead to a significant increase in computational resources. I've had the opportunity to experiment with Anthropics' 100k model, and it's evident that they're employing some clever techniques to make it work, albeit with some imperfections. One interesting observation is that their prompt guide recommends placing instructions after the reference text when inputting lengthy text bodies. I noticed that the model often disregarded the instructions if placed beforehand. It's clear that the model doesn't allocate the same level of "attention" to all parts of the input across the entire context window.

Moreover, the inability to cache transformers makes the use of large context windows quite costly, as all previous messages must be sent with each call. In this context, the RWKV-LM project on GitHub (https://github.com/BlinkDL/RWKV-LM) might offer a solution. They claim to achieve performance comparable to transformers using an RNN, which could potentially handle a 100-page document and cache it, thereby eliminating the need to process the entire document with each subsequent query. However, I suspect RWKV might fall short in handling complex tasks that require maintaining multiple variables in memory, such as mathematical computations, but it should suffice for many scenarios.

On a related note, I believe Anthropics' Claude is somewhat underappreciated. In some instances, it outperforms GPT4, and I'd rank it somewhere between GPT4 and Bard overall.

> The only reason open-source LLMs have a heartbeat is they’re standing on Meta’s weights.

Not necessarily.

RWKV, for example, is a different architecture that wasn't based on Facebook's weights whatsoever. I don't know where BlinkDL (the author) got the training data, but they seem to have done everything mostly independently otherwise.

https://github.com/BlinkDL/RWKV-LM

disclaimer: I've been doing a lot of work lately on an implementation of CPU inference for this model, so I'm obviously somewhat biased since this is the model I have the most experience in.

I think the main concern is that, due to the resources put into LLM research for finding new ways to refine and improve them, that work can then be used by projects that do go the extra mile and create things that are more than just LLMs. For example, RWKV is similar to an LLM but will actually change its own model after every processed token, thus letting it remember things longer-term without the use of 'context tokens'.