dolly VS RWKV-LM

For this tutorial, I am going to use Beam to deploy dolly-v2-7b, an open source large language model from Databricks, which responds similar to ChatGPT.

Dolly from databricks (https://www.databricks.com/blog/2023/03/24/hello-dolly-democratizing-magic-chatgpt-open-models.html)

22-Mar-2023 Adobe unveils creative generative AI model, Firefly, to aid content creation Google has begun rolling out early access to its Bard chatbot in the US and UK Data Breach At ChatGPT? Users Report Seeing Unknown Conversations On Their Screens GPT-4 is available in preview in Azure OpenAI Service AI-powered coding assistance REPL that pairs GPT-4 (https://github.com/jiggy-ai/pair) Open source alternative to ChatGPT (https://github.com/nichtdax/awesome-totally-open-chatgpt) Run 100B+ language models at home, BitTorrent‑style (https://petals.ml/) Find the most relevant piece of code context. Hover and highlight blocks of code, the tool will point you to the most relevant pieces of information on git, messaging, and ticketing systems. Finally, it provide a summary with the power of GPT.(https://www.watermelontools.com/) Why AI Won't Replace Software Engineers (https://softwarecomplexity.com/why-ai-wont-replace-software-engineers) 23-Mar-2023 'The iPhone Moment of AI' Nvidia to Rent Out Supercomputers Behind ChatGPT to Businesses for $37,000 a Month Bill Gates calls AI revolutionary, says it can reduce some of the world’s worst inequities AI pics of Donald Trump's arrest by 'cop' Joe Biden go viral. Will we no longer be able to tell what’s real vs what’s fake?” - Eluna AI New research shows we can only accurately identify AI writers about 50% of the time. (https://hai.stanford.edu/news/was-written-human-or-ai-tsu) FauxPilot - an open-source GitHub Copilot server(https://github.com/fauxpilot/fauxpilot) Flower , an open-source framework for training AI on distributed data. We move the model to the data instead of moving the data to the model. (https://flower.dev/) OpenAI-Integrated Microsoft Bing Outperforms Google in Page Visits (https://www.gadgets360.com/internet/news/openai-integrated-microsoft-bing-outperforms-google-page-visits-growth-3885069) GitHub Copilot X: GitHub Copilot is evolving to bring chat and voice interfaces, support pull requests, answer questions on docs, and adopt OpenAI’s GPT-4 for a more personalized developer experience. (https://github.blog/2023-03-22-github-copilot-x-the-ai-powered-developer-experience/) Moonshine – open-source, pretrained ML models for satellite (https://github.com/moonshinelabs-ai/moonshine) Mozilla.ai: A startup — and a community — that will build a trustworthy and independent open-source AI ecosystem. Mozilla.ai’s initial focus? Tools that make generative AI safer and more transparent. And, people-centric recommendation systems that don’t misinform or undermine our well-being. (https://blog.mozilla.org/en/mozilla/introducing-mozilla-ai-investing-in-trustworthy-ai/) OpenAI’s policies hinder reproducible research on language models (https://aisnakeoil.substack.com/p/openais-policies-hinder-reproducible) 24-Mar-2023 Adobe has added AI features to Photoshop and Illustrator, while Nvidia has unveiled ‘Picasso’ AI image generation service. ChatGPT-owner OpenAI fixes 'significant issue' exposing user chat titles.A bug in an open-source library caused ChatGPT to leak user conversation titles. Graphic design platform Canva introduces new generative AI tools Gmail for Android, Google Messages to Soon Get Features for AI-Generated Texts Apple: Transformer architecture optimized for Apple Silicon (https://github.com/apple/ml-ane-transformers) ChatGPT plugins, join waitlist (https://openai.com/blog/chatgpt-plugins) Microsoft's paper on OpenAI's GPT-4 had hidden information (https://twitter.com/DV2559106965076/status/1638769434763608064) how to use LoRA to fine-tune LLaMA using Alpaca training data (https://replicate.com/blog/fine-tune-alpaca-with-lora) Helicone: one-line integration logs the prompts, completions, latencies, and costs of your OpenAI requests (https://github.com/Helicone/helicone) RWKV is an RNN with Transformer-level LLM performance, which can also be directly trained like a GPT transformer (parallelizable). (https://github.com/BlinkDL/RWKV-LM) open-source retrieval plugin The open-source retrieval plugin enables ChatGPT to access personal or organizational information sources (with permission). It allows users to obtain the most relevant document snippets from their data sources, such as files, notes, emails or public documentation, by asking questions or expressing needs in natural language. Security considerations The retrieval plugin allows ChatGPT to search a vector database of content, and add the best results into the ChatGPT session. This means it doesn’t have any external effects, and the main risk is data authorization and privacy. Developers should only add content into their retrieval plugin that they are authorized to use and can share in users’ ChatGPT sessions. https://github.com/openai/chatgpt-retrieval-plugin 27-Mar-2023 Autodoc: Toolkit for auto-generating codebase documentation using LLMs (https://github.com/context-labs/autodoc) March 20 ChatGPT outage: Here’s what happened (https://openai.com/blog/march-20-chatgpt-outage) Facebook is going after LLaMA repos with DMCA's (https://twitter.com/theshawwn/status/1638925249709240322) ChatGPT + Wolfram is INSANE! (https://old.reddit.com/r/ChatGPT/comments/1205omc/chatgpt\_wolfram\_is\_insane/) Reproducing the Stanford Alpaca results using low-rank adaptation (LoRA) (https://github.com/chris-alexiuk/alpaca-lora) GOAT, a decentralized way to publish and download AI models.Powered by BitTorrent and Bitcoin.(https://ipfs.io/ipfs/QmYyucgBQVfs9JXZ2MtmkGPAhgUjNgyGE6rcJT1KybQHhp/index.html) Dolly from databricks (https://www.databricks.com/blog/2023/03/24/hello-dolly-democratizing-magic-chatgpt-open-models.html) AI powered Developer Tools 2.0. https://www.sequoiacap.com/article/ai-powered-developer-tools/ Turn your designs into production-ready front-end code for mobile apps and the web (https://www.locofy.ai/) Using ChatGPT Plugins with LLaMA (https://blog.lastmileai.dev/using-openais-retrieval-plugin-with-llama-d2e0b6732f14) 28-Mar-2023 Bing AI now allows 20 prompts per session and can make images for you ChatGPT Outperforms Crowd-Workers for Text-Annotation Tasks (https://arxiv.org/abs/2303.15056) ChatGPT or Grammarly? Evaluating ChatGPT on Grammatical Error Correction Benchmark (https://arxiv.org/abs/2303.13648) AI-controlled Linux Containers (https://github.com/fafrd/aquarium) Microsoft reportedly orders AI chatbot rivals to stop using Bing’s search data (https://www.theverge.com/2023/3/25/23656336/microsoft-chatbot-rivals-stop-using-bing-search-index) 29-Mar-2023 Text2Video-Zero Code and Weights Released by Picsart AI Research (12G VRAM).(https://github.com/Picsart-AI-Research/Text2Video-Zero) Pause Giant AI Experiments: An Open Letter. Huggingface's SF Open-Source AI Meetup officially has 2000 people registered. Cerebras open sources seven GPT-3 models from 111 million to 13 billion parameters. Trained using the Chinchilla formula, these models set new benchmarks for accuracy and compute efficiency.(https://www.cerebras.net/blog/cerebras-gpt-a-family-of-open-compute-efficient-large-language-models/) Independent implementation of LLaMA that is fully open source under the Apache 2.0 license (https://github.com/Lightning-AI/lit-llama) Bootstrap knowledge of LLMs (https://gist.github.com/rain-1/eebd5e5eb2784feecf450324e3341c8d) OPENFLAMINGO: AN OPEN-SOURCE FRAMEWORK FOR TRAINING VISION-LANGUAGE MODELS WITH IN-CONTEXT LEARNING (https://laion.ai/blog/open-flamingo/) gpt4all: a chatbot trained on a massive collection of clean assistant data including code, stories and dialogue (https://github.com/nomic-ai/gpt4all) 30-Mar-2022 Microsoft Security Copilot is a new GPT-4 AI assistant for cybersecurity (https://www.theverge.com/2023/3/28/23659711/microsoft-security-copilot-gpt-4-ai-tool-features) UK details ‘pro-innovation’ approach to AI regulation (https://www.artificialintelligence-news.com/2023/03/29/uk-details-pro-innovation-approach-ai-regulation/) Employees Are Feeding Sensitive Biz Data to ChatGPT, Raising Security Fears (https://www.darkreading.com/risk/employees-feeding-sensitive-business-data-chatgpt-raising-security-fears) In the Age of AI, Don't Let Your Skills Atrophy (https://www.cyberdemon.org/2023/03/29/age-of-ai-skill-atrophy.html) Now ChatGPT is being (mis)used to do #PeerReview (https://mstdn.science/@ukrio/110100752908161183) Bing Chat now has Ads! (https://twitter.com/debarghya\_das/status/1640892791923572737) Cerebras-GPT vs LLaMA AI Model Comparison (https://www.lunasec.io/docs/blog/cerebras-gpt-vs-llama-ai-model-comparison/) Arthur C. Clarke about the future of AI. — 21 September 1964 (https://twitter.com/Rainmaker1973/status/1640016339011076097) ColossalChat: An Open-Source Solution for Cloning ChatGPT With a Complete RLHF Pipeline (https://medium.com/@yangyou\_berkeley/colossalchat-an-open-source-solution-for-cloning-chatgpt-with-a-complete-rlhf-pipeline-5edf08fb538b) Create and Embed Custom AI Assistants with Libraria (https://libraria.dev/) 31-Mar-2023 Deranged New AI Has No Guardrails Whatsoever, Proudly Praises Hitler (https://futurism.com/deranged-ai-no-guardrails) Midjourney Kills Free AI Image Generator Access After Explosion of Deep Fakes (https://decrypt.co/124972/midjourney-free-ai-image-generation-stopped-over-deepfakes) Judge asks ChatGPT to decide bail in murder trial (https://nypost.com/2023/03/29/judge-asks-chatgpt-for-decision-in-murder-trial/) Should you use OpenAI's embeddings? Probably not, and here's why. (https://iamnotarobot.substack.com/p/should-you-use-openais-embeddings) Visual Studio Code and GitHub Copilot (https://code.visualstudio.com/blogs/2023/03/30/vscode-copilot) Llama Hub (https://llamahub.ai/) Finetuning LLMs on a Single GPU Using Gradient Accumulation (https://lightning.ai/pages/blog/gradient-accumulation/) Open source ETL framework for retrieval augmented generation (RAG). Sync data from your SaaS tools to a vector store, where they can be easily queried by GPT apps (https://github.com/ai-sidekick/sidekick) HALTT4LLM - Hallucination Trivia Test for Large Language Models (https://github.com/manyoso/haltt4llm) Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90%* ChatGPT Quality (https://vicuna.lmsys.org/) Iterate.ai Brings Generative AI Capabilities to Interplay, the Low-Code Platform Accelerating Customers’ Digital Innovation (https://www.indianweb2.com/2023/03/iterateai-brings-generative-ai.html) RFdiffusion is an open source method for structure generation, with or without conditional information (a motif, target etc). (https://github.com/RosettaCommons/RFdiffusion) Google denies training Bard on ChatGPT chats from ShareGPT

DataBricks's Dolly

Hi /u/meowkittykitty510, I'm embarking on a similar project, solely for teaching myself about finetuning LLMs. I initially chose Dolly (Pythia model) with modifications to the Dolly 2.0 trainer plus additional training data scraped from the web about The Expanse just as a fun way to test.

Ensemble de données: https://github.com/databrickslabs/dolly/tree/master/data

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'.