Parameter-Efficient Fine-Tuning of LLMs using LoRA (Low-Rank Adaptation)

Large Language Models (LLMs) are capable of extraordinary NLP feats, but are so large that they're too expensive for most organizations to train. The solution is Parameter-Efficient Fine-Tuning (PEFT) with Low-Rank Adaptation (LoRA).

This discussion comes in the wake of introducing models like Alpaca, Vicuña, GPT4All-J, and Dolly 2.0, which demonstrated the power of fine-tuning with thousands of instruction-response pairs.

Training LLMs, even those with tens of billions of parameters, can be prohibitively expensive and technically challenging. One significant issue is "catastrophic forgetting," where a model, after being retrained on new data, loses its ability to perform previously learned tasks. This challenge necessitates a more efficient approach to fine-tuning.

PEFT

By reducing the memory footprint and the number of parameters needed for training, PEFT methods like LoRA and AdaLoRA make it feasible to fine-tune large models on standard hardware. These techniques are not only space-efficient, with model weights requiring only megabytes of space, but they also avoid catastrophic forgetting, perform better with small data sets, and generalize better to out-of-training-set instructions. They can also be applied to other A.I. use cases — not just NLP — such as machine vision.

LoRA

LoRA stands out as a particularly effective PEFT method. It involves inserting low-rank decomposition matrices into each layer of a transformer model. These matrices represent data in a lower-dimensional space, simplifying computational processing. The key to LoRA's efficiency is freezing all original model weights except for the new low-rank matrices. This strategy reduces the number of trainable parameters by approximately 10,000 times and lowers the memory requirement for training by about three times. Remarkably, LoRA sometimes not only matches but even outperforms full-model training in certain scenarios. This efficiency does not come at the cost of effectiveness, making LoRA an attractive option for fine-tuning LLMs.

AdaLoRA

AdaLoRA, a recent innovation by researchers at Georgia Tech, Princeton, and Microsoft, builds on the foundations of LoRA. It differs by adaptively fine-tuning parts of the transformer architecture that benefit most from it, potentially offering enhanced performance over standard LoRA.

These developments in PEFT and the emergence of tools like LoRA and AdaLoRA mark an incredibly exciting and promising time for data scientists. With the ability to fine-tune large models efficiently, the potential for innovation and application in the field of AI is vast and continually expanding.

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Open-source “ChatGPT”: Alpaca, Vicuña, GPT4All-J, and Dolly 2.0

Want a GPT-4-style model on your own hardware and fine-tuned to your proprietary language-generation tasks? Today's episode covers the key open-source models (Alpaca, Vicuña, GPT4All-J, and Dolly 2.0) for doing this cheaply on a single GPU 🤯

We begin with a retrospective look at Meta AI's LLaMA model, which was introduced in episode #670. LLaMA, with its 13 billion parameters, achieves performance comparable to GPT-3 while being significantly smaller and more manageable. This efficiency makes it possible to train the model on a single GPU, democratizing access to advanced AI capabilities.

The focus then shifts to four models that surpass LLaMA in terms of power and sophistication: Alpaca, Vicuña, GPT4All-J, and Dolly 2.0. Each of these models presents a unique blend of innovation and practicality, pushing the boundaries of what's possible with AI:

Alpaca

Developed by Stanford researchers, Alpaca is an evolution of the 7 billion parameter LLaMA model, fine-tuned with 52,000 examples of instruction-following natural language. This model excels in mimicking GPT-3.5's instruction-following capabilities, offering high performance at a fraction of the cost and size.

Vicuña

Vicuña, a product of collaborative research across multiple institutions, builds on both the 7 billion and 13 billion parameter LLaMA models. It's fine-tuned on 70,000 user-shared ChatGPT conversations from the ShareGPT repository, achieving GPT-3.5-like performance with unique user-generated content.

GPT4All-J

GPT4All-J, released by Nomic AI, is based on EleutherAI's open source 6 billion parameter GPT-J model. It's fine-tuned with an extensive 800,000 instruction-response dataset, making it an attractive option for commercial applications due to its open-source nature and Apache license.

Dolly 2.0

Dolly 2.0, from database giant Databricks, builds upon EleutherAI's 12 billion parameter model. It's fine-tuned with 15,000 human-generated instruction response pairs, offering another open source, commercially viable option for AI applications.

These models represent a significant shift in the AI landscape, making it economically feasible for individuals and small teams to train and deploy powerful language models. With a few hundred to a few thousand dollars, it's now possible to create proprietary, ChatGPT-like models tailored to specific use cases.

The advancements in AI models that can be trained on a single GPU mark a thrilling era in data science. These developments not only showcase the rapid progression of AI technology but also significantly lower the barrier to entry, allowing a broader range of users to explore and innovate in the field of artificial intelligence.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.

LLaMA: GPT-3 performance, 10x smaller

By training (relatively) small LLMs for (much) longer, Meta AI's LLaMA architectures achieve GPT-3-like outputs at as little as a thirteenth of GPT-3's size. This means cost savings and much faster execution time.

LLaMA, a clever nod to LLMs (Large Language Models), is Meta AI's latest contribution to the AI world. Based on the Chinchilla scaling laws, LLaMA adopts a principle that veers away from the norm. Unlike its predecessors, which boasted hundreds of millions of parameters, LLaMA emphasizes training smaller models for longer durations to achieve enhanced performance.

The Chinchilla Principle in LLaMA

The Chinchilla scaling laws, introduced by Hoffmann and colleagues, postulate that extended training of smaller models can lead to superior performance. LLaMA, with its 7 billion to 65 billion parameter models, is a testament to this principle. For perspective, GPT-3 has 175 billion parameters, making the smallest LLaMA model just a fraction of its size.

Training Longer for Greater Performance

Meta AI's LLaMA pushes the boundaries by training these relatively smaller models for significantly longer periods than conventional approaches. This method contrasts with last year's top models like Chinchilla, GPT-3, and PaLM, which relied on undisclosed training data. LLaMA, however, uses entirely open-source data, including datasets like English Common Crawl, C4, GitHub, Wikipedia, and others, adding to its appeal and accessibility.

LLaMA's Remarkable Achievements

LLaMA's achievements are notable. The 13 billion parameter model (LLaMA 13B) outperforms GPT-3 in most benchmarks, despite having 13 times fewer parameters. This implies that LLaMA 13 can offer GPT-3 like performance on a single GPU. The largest LLaMA model, 65B, competes with giants like Chinchilla 70B and PaLM, even preceding the release of GPT-4.

This approach signifies a shift in the AI paradigm – achieving state-of-the-art performance without the need for enormous models. It's a leap forward in making advanced AI more accessible and environmentally friendly. The model weights, though intended for researchers, have been leaked and are available for non-commercial use, further democratizing access to cutting-edge AI.

LLaMA not only establishes a new benchmark in AI efficiency but also sets the stage for future innovations. Building on LLaMA's foundation, models like Alpaca, Vicuna, and GPT4ALL have emerged, fine-tuned on thoughtful datasets to exceed even LLaMA's performance. These developments herald a new era in AI, where size doesn't always equate to capability.

The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.