lit-llama VS llama

A pure Python library for running several open source LLMs locally, such as Mistral or StableLM. It also branched into subprojects for supporting specific LLM model-families, such as lit-llama. The projects goal are simplicity and optimization to run on several hardware. When used with 4bit quantization, it needs below 1GB to run a 3B model, and also CPU usage remains low.

For training: This is just an inherently technical process but looking at existing workflows helps pull the piece together and here is a good resource I got started with (https://github.com/philschmid/deep-learning-pytorch-huggingface). There is also a ton of resources on Github for scripts, walkthroughs and more (eg: https://github.com/Lightning-AI/lit-llama/blob/main/howto/train_redpajama.md)

Try this, very easy to follow and reproduce the results: https://github.com/lightning-AI/lit-llama

If you liked Karpathy's nano-gpt, you could checkout Lit-LLama, which is a Pytorch Lightning's reimplementation of LLama models, based on nano-gpt.

Independent implementation of LLaMA that is fully open source under the Apache 2.0 license (https://github.com/Lightning-AI/lit-llama)

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

Repos like https://github.com/tloen/alpaca-lora and https://github.com/Lightning-AI/lit-llama use LoRA as a method to fine-tune LLaMA models.

A high-end computer store where I could grab as many NVIDIA AI GPU's as possible to start running my own AI LLM at home.

derivative works thereof).”

https://github.com/meta-llama/llama/blob/b8348da38fde8644ef0...

Also even if you did use Llama for something, they could unilaterally pull the rug on you when you got 700 million years, AND anyone who thinks Meta broke their copyright loses their license. (Checking if you are still getting screwed is against the rules)

Therefore, Zuckerberg is accountable for explicitly anticompetitive conduct, I assumed an MMA fighter would appreciate the value of competition, go figure.

One thing I wanted to add and call attention to is the importance of licensing in open models. This is often overlooked when we blindly accept the vague branding of models as “open”, but I am noticing that many open weight models are actually using encumbered proprietary licenses rather than standard open source licenses that are OSI approved (https://opensource.org/licenses). As an example, Databricks’s DBRX model has a proprietary license that forces adherence to their highly restrictive Acceptable Use Policy by referencing a live website hosting their AUP (https://github.com/databricks/dbrx/blob/main/LICENSE), which means as they change their AUP, you may be further restricted in the future. Meta’s Llama is similar (https://github.com/meta-llama/llama/blob/main/LICENSE ). I’m not sure who can depend on these models given this flaw.

It looks like Llama 2 7B took 184,320 A100-80GB GPU-hours to train[1]. This one says it used a 96×H100 GPU cluster for 2 weeks, for 32,256 hours. That's 17.5% of the number of hours, but H100s are faster than A100s [2] and FP16/bfloat16 performance is ~3x better.

If they had tried to replicate Llama 2 identically with their hardware setup, it'd cost a little bit less than twice their MoE model.

[1] https://github.com/meta-llama/llama/blob/main/MODEL_CARD.md#...

Ironically, the LLaMA license text [1] this is lifted verbatim from is itself copyrighted [2] and doesn't grant you the permission to copy it or make changes like s/meta/dbrx/g lol.

[1] https://github.com/meta-llama/llama/blob/main/LICENSE#L65

This is kind of an epistemological debate at this level, and I make an effort to link to some source code [1] any time it seems contentious.

LLMs (of the decoder-only, generative-pretrained family everyone means) are next token predictors in a literal implementation sense (there are some caveats around batching and what not, but none that really matter to the philosophy of the thing).

But, they have some emergent behaviors that are a trickier beast. Probably the best way to think about a typical Instruct-inspired “chat bot” session is of them sampling from a distribution with a KL-style adjacency to the training corpus (sidebar: this is why shops that do and don’t train/tune on MMLU get ranked so differently than e.g. the arena rankings) at a response granularity, the same way a diffuser/U-net/de-noising model samples at the image batch (NCHW/NHWC) level.

The corpus is stocked with everything from sci-fi novels with computers arguing their own sentience to tutorials on how to do a tricky anti-derivative step-by-step.

This mental model has adequate explanatory power for anything a public LLM has ever been shown to do, but that only heavily implies it’s what they’re doing.

There is active research into whether there is more going on that is thus far not conclusive to the satisfaction of an unbiased consensus. I personally think that research will eventually show it’s just sampling, but that’s a prediction not consensus science.

They might be doing more, there is some research that represents circumstantial evidence they are doing more.

[1] https://github.com/meta-llama/llama/blob/54c22c0d63a3f3c9e77...

Predicting subsequent text is pretty much exactly what they do. Lots of very cool engineering that’s a real feat, but at its core it’s argmax(P(token|token,corpus)):

https://github.com/facebookresearch/llama/blob/main/llama/ge...

The engineering feats are up there with anything, but it’s a next token predictor.

https://github.com/facebookresearch/llama/pull/947/

> Instead, it turns out a few hundred lines of Python is genuinely enough to train a basic version!

actually its not just a basic version. Llama 1/2's model.py is 500 lines: https://github.com/facebookresearch/llama/blob/main/llama/mo...

Mistral (is rumored to have) forked llama and is 369 lines: https://github.com/mistralai/mistral-src/blob/main/mistral/m...

and both of these are SOTA open source models.

In transformers, they tried really hard to have a single function or method to deal with both self and cross attention mechanisms, masking, positional and relative encodings, interpolation etc. While it allows a user to use the same function/method for any model, it has led to severe parameter bloat. Just compare the original implementation of llama by FAIR with the implementation by HF to get an idea.