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  1. Generative AI

Large Language Models (LLMs)

PreviousFairness, Accountability, and Transparency In PromptsNextVision

Last updated 11 months ago

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Articles

  1. (great)

  2. by OpenAI

  3. - The trade-off between model size and compute overhead and reveal there is significant room to reduce the compute-optimal model size with minimal compute overhead.

Papers

  1. - Alec et al. openAI

  2. - scaling LLMs with data is enough to make them few shot.

Models

  1. Databricks dolly

    1. Vicuna

    2. LLaMA

Instructor

Datasets

Tools

      • LLMs

        • Prompt Templates

        • Chains

        • Agents and Tools

        • Memory

        • Document Loaders

        • Indexes

Guardrails

Best Practices

Reinforcement Learning for LLM

Metrics

Use Cases

,

(using RLHF)

- "We introduce Instructor👨‍🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) by simply providing the task instruction, without any finetuning. Instructor achieves sota on 70 diverse embedding tasks!"

in by Patrick Loeber about

, , - is a UI for LangChain, designed with react-flow to provide an effortless way to experiment and prototype flows.

- PandasAI, asking data Qs using LLMs on Panda's DFs with two code lines. 𝚙𝚊𝚗𝚍𝚊𝚜_𝚊𝚒 = 𝙿𝚊𝚗𝚍𝚊𝚜𝙰𝙸(𝚕𝚕𝚖) & 𝚙𝚊𝚗𝚍𝚊𝚜_𝚊𝚒.𝚛𝚞𝚗(𝚍𝚏, 𝚙𝚛𝚘𝚖𝚙𝚝='𝚆𝚑𝚒𝚌𝚑 𝚊𝚛𝚎 𝚝𝚑𝚎 𝟻 𝚑𝚊𝚙𝚙𝚒𝚎𝚜𝚝 𝚌𝚘𝚞𝚗𝚝𝚛𝚒𝚎𝚜?')

- LlamaIndex (GPT Index) is a project that provides a central interface to connect your LLM's with external data.

- LLM training code for Databricks foundation models using MoasicML

- A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training

- The simplest, fastest repository for training/finetuning medium-sized GPTs.

(https://minigpt-4.github.io, https://minigpt-v2.github.io/)

- Implementing LLM Guardrails for Safe and Responsible Generative AI Deployment on Databricks

by Chip Huyen

- Reinforcement Learning from Human Feedback: Progress and Challenges

- a family of metrics that evaluate the performance of a LLM in text summarization, i.e., ROUGE-1, ROUGE-2, ROUGE-L, for unigrams, bi grams, LCS, respectively.

Version 1.0
Version 2.0
Huggingface
LLaMA
Bard
StabilityLM
Training language models to follow instructions with human feedback
Instructor model
Databricks' 15K QA for Dolly 2.0
Scikit-LLM
LangChain
An amazing tutorial
Youtube
Langchain in 13 minutes
ReAct & LangChain
LangFlow
Medium
HuggingFace
PandasAI
LLaMa Index
LLM-foundry
Awesome ChatGPT - Curated list of awesome tools, demos, docs for ChatGPT and GPT-3
GPT4 All Privacy-oriented software for chatting with large language models that run on your own computer.
MinGPT
NanoGPT
Open-sourced codes for MiniGPT-4 and MiniGPT-v2
GuardrailsAI
Safeguarding LLMs with Guardrails
Databricks GR
Best Practices for LLM Evaluation of RAG Applications
Announcing MLflow 2.8 LLM-as-a-judge metrics and Best Practices for LLM Evaluation of RAG Applications, Part 2
RLHF: Reinforcement Learning from Human Feedback
Yoav on RL
John Schulman
Understanding ROUGE
Enhancing ChatGPT With Infinite External Memory Using Vector Database and ChatGPT Retrieval Plugin
GPT4 can improve itself
Lil Weng - Prompt Engineering
Chip Huyen - Building LLM applicaations for production
How to generate text using different decoding methods for language generation with transformers
a gentle intro to LLMs and Langchain
LLMs can explain NN of other LLMs
Fine tuning LLMs
Model size vs Computer overhead
Language understanding by generative pre-training
LLM are few shot learners