Martineski

Abstract:

Since the first laser was invented, the pursuit of high-energy lasers (HELs) has always been enthusiastic. The first revolution of HELs was pushed by the fusion of laser and aerospace in the 1960s, with the chemical rocket engines giving fresh impetus to the birth of gas flow and chemical lasers, which finally turned megawatt lasers from dream into reality. Nowadays, the development of HELs has entered the age of electricity as well as the rocket engines. The properties of current electric rocket engines are highly consistent with HELs’ goals, including electrical driving, effective heat dissipation, little medium consumption and extremely light weight and size, which inspired a second fusion of laser and aerospace and motivated the exploration for potential HELs. As an exploratory attempt, a new configuration of diode pumped metastable rare gas laser was demonstrated, with the gain generator resembling an electric rocket-engine for improved power scaling ability.

Original title: Focused Transformer: Contrastive Training for Context Scaling

Large language models have an exceptional capability to incorporate new information in a contextual manner. However, the full potential of such an approach is often restrained due to a limitation in the effective context length. One solution to this issue is to endow an attention layer with access to an external memory, which comprises of (key, value) pairs. Yet, as the number of documents increases, the proportion of relevant keys to irrelevant ones decreases, leading the model to focus more on the irrelevant keys. We identify a significant challenge, dubbed the distraction issue, where keys linked to different semantic values might overlap, making them hard to distinguish. To tackle this problem, we introduce the Focused Transformer (FoT), a technique that employs a training process inspired by contrastive learning. This novel approach enhances the structure of the (key, value) space, enabling an extension of the context length. Our method allows for fine-tuning pre-existing, large-scale models to lengthen their effective context. This is demonstrated by our fine-tuning of 3B and 7B OpenLLaMA checkpoints. The resulting models, which we name LongLLaMA, exhibit advancements in tasks requiring a long context. We further illustrate that our LongLLaMA models adeptly manage a 256k context length for passkey retrieval.

The digital plane we traverse is volatile and ever-changing, evolving at a pace that leaves the old world gasping for breath. One of the newest entrants in the tech theatre is the use of AI in content creation, specifically television. Fable, a San Fransisco-based start-up, is pioneering this bold frontier. Their brainchild, aptly named the Showrunner AI technology, or ‘SHOW-1’, aims to generate TV shows with viewers as starring roles.

I just copy/pasted what's in the link so formatting may be broken:

GPT-4's details are leaked.

It is over.

Everything is here: twitter.com/i/web/status/1… Parameters count:

GPT-4 is more than 10x the size of GPT-3. We believe it has a total of ~1.8 trillion parameters across 120 layers. Mixture Of Experts - Confirmed.

OpenAI was able to keep costs reasonable by utilizing a mixture of experts (MoE) model. They utilizes 16 experts within their model, each is about ~111B parameters for MLP. 2 of these experts are routed to per forward pass. MoE Routing:

While the literature talks a lot about advanced routing algorithms for choosing which experts to route each token to, OpenAI’s is allegedly quite simple, for the current GPT-4 model.

There roughly ~55B shared parameters for attention. Inference:

Each forward pass inference (generation of 1 token) only utilizes ~280B parameters and ~560 TFLOPs. This contrasts with the ~1.8 trillion parameters and ~3,700 TFLOP that would be required per forward pass of a purely dense model. Dataset:

GPT-4 is trained on ~13T tokens.

These are not unique tokens, they count the epochs as more tokens as well.

Epoch number: 2 epochs for text-based data and 4 for code-based data.

There is millions of rows of instruction fine-tuning data from ScaleAI & internally. GPT-4 32K

There was an 8k context length (seqlen) for the pre-training phase. The 32k seqlen version of GPT-4 is based on fine-tuning of the 8k after the pre-training. Batch Size:

The batch size was gradually ramped up over a number of days on the cluster, but by the end, OpenAI was using a batch size of 60 million! This, of course, is “only” a batch size of 7.5 million tokens per expert due to not every expert seeing all tokens. For the real batch size: Divide this number by the seq len to get the real batch size. just stop with this misleading numbers already. Parallelism Strategies

To parallelize across all their A100s GPUs They utilized 8-way tensor parallelism as that is the limit for NVLink.

Beyond that, they are using 15-way pipeline parallelism.

(likely used ZeRo Stage 1. It is possible they used block-level FSDP) Training Cost

OpenAI’s training FLOPS for GPT-4 is ~2.15e25, on ~25,000 A100s for 90 to 100 days at about 32% to 36% MFU.

Part of this extremely low utilization is due to an absurd number of failures requiring checkpoints that needed to be restarted from. If their cost in the cloud was about $1 per A100 hour, the training costs for this run alone would be about $63 million.

(Today, the pre-training could be done with ~8,192 H100 in ~55 days for $21.5 million at $2 per H100 hour.) Mixture of Expert Tradeoffs

There are multiple MoE tradeoffs taken: For example, MoE is incredibly difficult to deal with on inference because not every part of the model is utilized on every token generation. This means parts may sit dormant when other parts are being used. When serving users, this really hurts utilization rates.

Researchers have shown that using 64 to 128 experts achieves better loss than 16 experts, but that’s purely research. There are multiple reasons to go with fewer experts. One reason for OpenAI choosing 16 experts is because more experts are difficult to generalize at many tasks. More experts can also be more difficult to achieve convergence with. With such a large training run, OpenAI instead chose to be more conservative on the number of experts. GPT-4 Inference Cost

GPT-4 costs 3x that of the 175B parameter Davinchi. This is largely due to the larger clusters required for GPT-4 and much lower utilization achieved. AN estimate of it's costs is $0.0049 cents per 1k tokens for 128 A100s to inference GPT-4 8k seqlen and $0.0021 cents per 1k tokens for 128 H100’s to inference GPT-4 8k seqlen. It should be noted, we assume decent high utilization, and keeping batch sizes high. Multi-Query Attention

OpenAI are using MQA just like everybody else. Because of that only 1 head is needed and memory capacity can be significantly reduced for the KV cache. Even then, the 32k seqlen GPT-4 definitely cannot run on 40GB A100s, and the 8k is capped on max bsz. Continuous batching

OpenAI implements both variable batch sizes and continuous batching. This is so as to allow some level of maximum latency as well optimizing the inference costs. Vision Multi-Modal

It is a separate vision encoder from the text encoder, with cross-attention. The architecture is similar to Flamingo. This adds more parameters on top of the 1.8T of GPT-4. It is fine-tuned with another ~2 trillion tokens, after the text only pre-training. On the vision model, OpenAI wanted to train it from scratch, but it wasn’t mature enough, so they wanted to derisk it by starting with text. One of the primary purposes of this vision capability is for autonomous agents able to read web pages and transcribe what’s in images and video. Some of the data they train on is joint data (rendered LaTeX/text), screen shots of web page, youtube videos: sampling frames, and run Whisper around it to get transcript.

[Dont want to say "I told you so" but..] Speculative Decoding

OpenAI might be using speculative decoding on GPT-4's inference. (not sure 100%)

The idea is to use a smaller faster model to decode several tokens in advance, and then feeds them into a large oracle model as a single batch. If the small model was right about its predictions – the larger model agrees and we can decode several tokens in a single batch. But if the larger model rejects the tokens predicted by the draft model then the rest of the batch is discarded. And we continue with the larger model. The conspiracy theory that the new GPT-4 quality had been deteriorated might be simply because they are letting the oracle model accept lower probability sequences from the speculative decoding model. Inference Architecture

The inference runs on a cluster of 128 GPUs.

There are multiple of these clusters in multiple datacenters in different locations.

It is done in 8-way tensor parallelism and 16-way pipeline parallelism.

Each node of 8 GPUs has only ~130B parameters, or… twitter.com/i/web/status/1… The model has 120, so it fits in 15 different nodes. [Possibly the there are less layers on the first node since it needs to also compute the embeddings] According to these numbers: OpenAI should have trained on 2x the tokens if they were trying to go by chinchilla's optimal.

[let alone surpass it like we do]

This goes to show that they are struggling to get high quality data. Why no FSDP?

A possible reason for this could be that some of the hardware infra they secured is of an older generation.

This is pretty common at local compute clusters as the organisation usually upgrade the infra in several "waves" to avoid a complete pause of operation.… twitter.com/i/web/status/1… Dataset Mixture

They trained on 13T tokens. CommonCrawl & RefinedWeb are both 5T.

Remove the duplication of tokens from multiple epochs and we get to a much reasonable number of "unaccounted for" tokens: The "secret" data. Which by this point we already get rumors that parts of it came from twitter, reddit & youtube.

[Rumors that start to become lawsuits]

Some speculations are:

• LibGen (4M+ books)
• Sci-Hub (80M+ papers)
• All of GitHub

My own opinion:

The missing dataset it a custom dataset of college textbooks collected by hand for as much courses as possible.

This is very easy to convert to txt file and than with self-instruct into instruction form. This creates the "illusion" that GPT-4 "is smart" no matter who use it.

Computer scientist? sure! it can help you with your questions about P!=NP Philosophy major? It can totally talk to you about epistemology.

Don't you see? It was trained on the textbooks. It is so obvious. There are also papers that try to extract by force memorized parts of books from GPT-4 to understand what it trained on.

There are some books it knows so well that it had seen them for sure.

Moreover, If i remember correctly: It even know the unique ids of project Euler exes.

Abstract:

We observe that pre-trained large language models (LLMs) are capable of autoregressively completing complex token sequences -- from arbitrary ones procedurally generated by probabilistic context-free grammars (PCFG), to more rich spatial patterns found in the Abstract Reasoning Corpus (ARC), a general AI benchmark, prompted in the style of ASCII art. Surprisingly, pattern completion proficiency can be partially retained even when the sequences are expressed using tokens randomly sampled from the vocabulary. These results suggest that without any additional training, LLMs can serve as general sequence modelers, driven by in-context learning. In this work, we investigate how these zero-shot capabilities may be applied to problems in robotics -- from extrapolating sequences of numbers that represent states over time to complete simple motions, to least-to-most prompting of reward-conditioned trajectories that can discover and represent closed-loop policies (e.g., a stabilizing controller for CartPole). While difficult to deploy today for real systems due to latency, context size limitations, and compute costs, the approach of using LLMs to drive low-level control may provide an exciting glimpse into how the patterns among words could be transferred to actions.

cross-posted from: https://lemmy.world/post/1750098

Introducing Llama 2 - Meta's Next Generation Free Open-Source Artificially Intelligent Large Language Model

It's incredible it's already here! This is great news for everyone in free open-source artificial intelligence.

Llama 2 unleashes Meta's (previously) closed model (Llama) to become free open-source AI, accelerating access and development for large language models (LLMs).

This marks a significant step in machine learning and deep learning technologies. With this move, a widely supported LLM can become a viable choice for businesses, developers, and entrepreneurs to innovate our future using a model that the community has been eagerly awaiting since its initial leak earlier this year.

• Meta Announcement
• Meta Overview
• Github
• Paper

Here are some highlights from the official Meta AI announcement:

Llama 2

In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases.

Our models outperform open-source chat models on most benchmarks we tested, and based on our human evaluations for helpfulness and safety, may be a suitable substitute for closedsource models. We provide a detailed description of our approach to fine-tuning and safety improvements of Llama 2-Chat in order to enable the community to build on our work and contribute to the responsible development of LLMs.

Llama 2 pretrained models are trained on 2 trillion tokens, and have double the context length than Llama 1. Its fine-tuned models have been trained on over 1 million human annotations.

Inside the Model

• Technical details

With each model download you'll receive:

• Model code
• Model Weights
• README (User Guide)
• Responsible Use Guide
• License
• Acceptable Use Policy
• Model Card

Benchmarks

Llama 2 outperforms other open source language models on many external benchmarks, including reasoning, coding, proficiency, and knowledge tests. It was pretrained on publicly available online data sources. The fine-tuned model, Llama-2-chat, leverages publicly available instruction datasets and over 1 million human annotations.

RLHF & Training

Llama-2-chat uses reinforcement learning from human feedback to ensure safety and helpfulness. Training Llama-2-chat: Llama 2 is pretrained using publicly available online data. An initial version of Llama-2-chat is then created through the use of supervised fine-tuning. Next, Llama-2-chat is iteratively refined using Reinforcement Learning from Human Feedback (RLHF), which includes rejection sampling and proximal policy optimization (PPO).

The License

Our model and weights are licensed for both researchers and commercial entities, upholding the principles of openness. Our mission is to empower individuals, and industry through this opportunity, while fostering an environment of discovery and ethical AI advancements.

Partnerships

We have a broad range of supporters around the world who believe in our open approach to today’s AI — companies that have given early feedback and are excited to build with Llama 2, cloud providers that will include the model as part of their offering to customers, researchers committed to doing research with the model, and people across tech, academia, and policy who see the benefits of Llama and an open platform as we do.

The/CUT

With the release of Llama 2, Meta has opened up new possibilities for the development and application of large language models. This free open-source AI not only accelerates access but also allows for greater innovation in the field.

Take Three:

• Video Game Analogy: Just like getting a powerful, rare (or previously banned) item drop in a game, Llama 2's release gives developers a powerful tool they can use and customize for their unique quests in the world of AI.
• Cooking Analogy: Imagine if a world-class chef decided to share their secret recipe with everyone. That's Llama 2, a secret recipe now open for all to use, adapt, and improve upon in the kitchen of AI development.
• Construction Analogy: Llama 2 is like a top-grade construction tool now available to all builders. It opens up new possibilities for constructing advanced AI structures that were previously hard to achieve.

Links

Here are the key resources discussed in this post:

• Meta Announcement
• Meta Overview
• Github
• Paper
• Technical details

Want to get started with free open-source artificial intelligence, but don't know where to begin?

Try starting here:

• FOSAI Welcome Message
• FOSAI Crash Course
• FOSAI Nexus Resource Hub

If you found anything else about this post interesting - consider subscribing to !fosai@lemmy.world where I do my best to keep you in the know about the most important updates in free open-source artificial intelligence.

This particular announcement is exciting to me because it may popularize open-source principles and practices for other enterprises and corporations to follow.

We should see some interesting models emerge out of Llama 2. I for one am looking forward to seeing where this will take us next. Get ready for another wave of innovation! This one is going to be big.