large language models
See also generative-ai page. and smol-llm , transformer-math , LlamaIndex , RAG , and local-llm , llm-embedding
Generative AI exists because of the transformer – A visual story from Financial Times; Sept 2023.
Large language models, explained with a minimum of math and jargon
- The Novice’s LLM Training Guide ; copy
- Normcore LLM Reads by Vicki Boykis
- A Hackers’ Guide to Language Models - YouTube by jeremy-howard
- Transformer Math 101 | EleutherAI Blog
Hi, this is Lilian. I’m documenting my learning notes in this blog. Other than writing a ML blog, I’m leading Applied Research at OpenAI on the side.
- Finbarr Timbers – eg: Five years of GPT progress
- Sparks of Artificial General Intelligence: Early experiments with GPT-4. (2023) PDF – Bubeck, Sébastien, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, et al. “Sparks of Artificial General Intelligence : Early experiments with GPT-4 ,” 2023.
- SeamlessM4T—Massively Multilingual & Multimodal Machine Translation | Meta AI Research – Barrault, Loïc, Yu-An Chung, Mariano Cora Meglioli, David Dale, Ning Dong, Paul-Ambroise Duquenne, Hady Elsahar, et al. “ SeamlessM4T-Massively multilingual & multimodal machine translation,” 2023. https://arxiv.org/abs/2308.11596 .
- Brown, Tom B., Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, et al. “Language models are few-shot learners,” 2020. https://arxiv.org/abs/2005.14165 .
An observation on Generalization - YouTube by Ilya Sutskever (OpenAI); Aug 14, 2023.
- Supervised Learning - precise mathematical condition under which learning should succeed, which is - Low training error + more training data than “degrees of freedom” = low test error
- ChatGPT Prompt Engineering for Developers - DeepLearning.AI
- Prompt Engineering | Lil’Log – “This post only focuses on prompt engineering for autoregressive language models, so nothing with Cloze tests, image generation or multimodality models.”
- Controllable Neural Text Generation | Lil’Log – “How to steer a powerful unconditioned language model? In this post, we will delve into several approaches for controlled content generation with an unconditioned langage model. For example, if we plan to use LM to generate reading materials for kids, we would like to guide the output stories to be safe, educational and easily understood by children.”
- Replacing my best friends with an LLM trained on 500,000 group chat messages
A guidance language for controlling large language models.
Guidance programs allow you to interleave generation, prompting, and logical control into a single continuous flow matching how the language model actually processes the text.
Open source models
Stuff you can run on your computer
smol-ai/developer: with 100k context windows on the way, it’s now feasible for every dev to have their own smol developer
how can we run
local machines when the expectation is that large models need expensive
GPUS (eg: A100) to run
Code Llama, a state-of-the-art large language model for coding
Code Llama is a code-specialized version of
that was created by
further training Llama 2 on its code-specific datasets, sampling more
data from that same dataset for longer. Essentially, Code Llama features
enhanced coding capabilities, built on top of Llama 2. It can generate
code, and natural language about code, from both code and natural
language prompts (e.g., “Write me a function that outputs the fibonacci
sequence.”) It can also be used for code completion and debugging. It
supports many of the most popular languages being used today, including
Ask HN: Cheapest way to run local LLMs? | Hacker News
LLMs in your language
All languages are NOT created (tokenized) equal
Small Language Models
- Eldan, Ronen, and Yuanzhi Li. “ TinyStories : How Small Can Language Models Be and Still Speak Coherent English ?” 2023.
- A Simple but Powerful Method to Analyze Online PDFs with Bing Chat - AI Demos
- What’s new in Llama 2 & how to run it locally - AGI Sphere ; Aug 2023.
- Fine-tune your own Llama 2 to replace GPT-3.5/4 | Hacker News > Fine-tuning has one huge advantage though: it is far more effective at guiding a model’s behavior than prompting, so you can often get away with a much smaller model. That gets you faster responses and lower inference costs. A fine-tuned Llama 7B model is 50x cheaper than GPT-3.5 on a per-token basis, and for many use cases can produce results that are as good or better! > For example, classifying the 2M recipes at https://huggingface.co/datasets/corbt/all-recipes with GPT-4 would cost $23k. Even with GPT-3.5 it would cost over $1k. The model we fine-tuned performs similarly to GPT-4 and costs just $19 to run over the entire dataset. > OpenBMB/ToolBench: An open platform for training, serving, and evaluating large language model for tool learning.
LlamaIndex 🦙 0.8.13
Haystack | Haystack
Open-source LLM framework to build production-ready applications
> Use the latest LLMs: hosted models by OpenAI or Cohere, open-source
LLMs, or other pre-trained models > All tooling in one place:
preprocessing, pipelines, agents & tools, prompts, evaluation and
finetuning > Choose your favorite database: Elasticsearch,
OpenSearch, Weaviate, Pinecone, Qdrant, Milvus and more > Scale to
millions of documents: use Haystack’s proven retrieval architecture >
Compare it to
locally running, privacy-aware chatbot.
No GPU or internet
an AI proxy that lets you use a variety of providers (OpenAI, Anthropic, LLaMa2, Mistral, and others) behind a single interface w/ caching & API key management.
Compilation for Large Language Models (MLC LLM) is a high-performance
universal deployment solution that allows native deployment of any large
language models with native APIs with compiler acceleration. The mission
of this project is to enable everyone to develop, optimize and deploy AI
models natively on everyone’s devices with ML compilation
Project Overview of MLC LLM
OWASP | Top 10 for Large Language Models
- Why won’t Llama13B fit on my 4090_.pptx - Google Slides by Mark Saroufim
LLM Benchmark Report for: NousResearch/Redmond-Puffin-13B
- Shoggoth is a peer-to-peer, anonymous network for publishing and distributing open-source code, Machine Learning models, datasets, and research papers.
- What We Know About LLMs (Primer) #primer
- A comprehensive guide to running Llama 2 locally - Replicate – Replicate ; Jul 22, 2023.
- PaLM2 Technical Report #pdf #google
- dalai – Run LLaMA and Alpaca on your computer. ??
- ggerganov/llama.cpp: Port of Facebook’s LLaMA model in C/C++
- LlamaIndex - (GPT Index) is a project that provides a central interface to connect your LLM’s with external data. see https://twitter.com/gpt_index
- LLM Introduction: Learn Language Models
- Announcing OpenFlamingo: An open-source framework for training vision-language models with in-context learning | LAION
- become a 1000x engineer or die tryin’
- Simon Willison: LLMs on personal devices Series.
- How You Can Install A ChatGPT-like Personal AI On Your Own Computer And Run It With No Internet.
- How to run your own LLM (GPT) ; Apr 2023.
A complete chat app that transcribes audio in real-time, streams back a response from a language model, and synthesizes this response as natural-sounding speech. This repo is meant to serve as a starting point for your own language model-based apps, as well as a playground for experimentation.
- A brief history of LLaMA models - AGI Sphere
- See LangChainAI
- ray-project/llm-numbers: Numbers every LLM developer should know ; via HN
- Beginner’s guide to Llama models - AGI Sphere ; Aug 2023.
- The Mathematics of Training LLMs — with Quentin Anthony of Eleuther AI
- Google Gemini Eats The World – Gemini Smashes GPT-4 By 5X, The GPU-Poors ; Aug 2023.
- fast.ai - Can LLMs learn from a single example?