Awesome-LLM
langchain
Awesome-LLM | langchain | |
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10 | 35 | |
14,654 | 84,427 | |
- | 4.6% | |
8.6 | 10.0 | |
8 days ago | 7 days ago | |
Python | ||
Creative Commons Zero v1.0 Universal | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Awesome-LLM
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XGen-7B, a new 7B foundational model trained on up to 8K length for 1.5T tokens
Here are some high level answers:
"7B" refers to the number of parameters or weights for a model. For a specific model, the versions with more parameters take more compute power to train and perform better.
A foundational model is the part of a ML model that is "pretrained" on a massive data set (and usually is the bulk of the compute cost). This is usually considered the "raw" model after which it is fine-tuned for specific tasks (turned into a chatbot).
"8K length" refers to the Context Window length (in tokens). This is basically an LLM's short term memory - you can think of it as its attention span and what it can generate reasonable output for.
"1.5T tokens" refers to the size of the corpus of the training set.
In general Wikipedia (or I suppose ChatGPT 4/Bing Chat with Web Browsing) is a decent enough place to start reading/asking basic questions. I'd recommend starting here: https://en.wikipedia.org/wiki/Large_language_model and finding the related concepts.
For those going deeper, there are lot of general resources lists like https://github.com/Hannibal046/Awesome-LLM or https://github.com/Mooler0410/LLMsPracticalGuide or one I like, https://sebastianraschka.com/blog/2023/llm-reading-list.html (there are a bajillion of these and you'll find more once you get a grasp on the terms you want to surf for). Almost everything is published on arXiv, and most is fairly readable even as a layman.
For non-ML programmers looking to get up to speed, I feel like Karpathy's Zero to Hero/nanoGPT or Jay Mody's picoGPT https://jaykmody.com/blog/gpt-from-scratch/ are alternative/maybe a better way to understand the basic concepts on a practical level.
- Couple of questions about a.i that can be run locally
- How to dive deeper into LLMs?
- [Hiring] Developer to build AI-powered chatbots with open source LLMs
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Creating a Wiki for all things Local LLM. What do you want to know?
Check out this repo, there should be some useful things worth noting https://github.com/Hannibal046/Awesome-LLM
- Large Language Model (LLM) Resources
- Curated list for LLMs: papers, training frameworks, tools to deploy, public APIs
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Performance of GPT-4 vs PaLM 2
First this is a pretty good starting point as a resource for learning about and finding open source models and the overall public history of progress of LLMs.
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FreedomGPT: AI with no censorship
This seems fishy as fuck. First red flag is a fishy installer instead of any huggingface link for the model. Upon further search I found this: https://desuarchive.org/g/thread/92686632/#92692092 There are posts in its own sub, r slash freedomgpt, raising concerns, and many new accounts with low karma replying to them(I don't think I can link other subs here, check them yourself), 100% some botting/astroturfing going on. Not touching this. Even in the best case scenario that this is legit with no funny business, this is supposed to be based on llama, which is substantially different tiny model(hence why it can be run on your computer at all). This is no Chatgpt equivalent eitherway. I would recommend getting something more reputable from github if you are interested in running LLMs yourself.
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Ask HN: Foundational Papers in AI
https://github.com/Hannibal046/Awesome-LLM has a curated list of LLM specific resources.
Not the creator, just happened upon it when researching LLMs today.
langchain
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Deploy LangServe Application to AWS
Limited by the current packaging method of Pluto, it does not yet support LangChain's Template Ecosystem. Coming soon
- Construyendo un asistente genAI de WhatsApp con Amazon Bedrock
- Show HN: SpRAG – Open-source RAG implementation for challenging real-world tasks
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Aider: AI pair programming in your terminal
Big fan of Aider.
We are interesting in integrating Aider as a tool for Dosu https://dosu.dev/ to help it navigate and modify a codebase on issues like this https://github.com/langchain-ai/langchain/issues/8263#issuec...
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🦙 Llama-2-GGML-CSV-Chatbot 🤖
Developed using Langchain and Streamlit technologies for enhanced performance.
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Building a WhatsApp generative AI assistant with Amazon Bedrock and Python
Tip: Kenton Blacutt, an AWS Associate Cloud App Developer, collaborated with Langchain, creating the Amazon Dynamodb based memory class that allows us to store the history of a langchain agent in an Amazon DynamoDB.
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👑 Top Open Source Projects of 2023 🚀
LangChain was first released in October 2022 as an open-source side project, a framework that makes developing AI applications more flexible. It got so popular that it was promptly turned into a startup.
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Fuck You, Show Me the Prompt
> Furthermore, the prompt has a spelling error (Let'w) and also overly focuses on the negative about identifying errors - which makes me skeptical that this prompt has been optimized or tested.
Fixed in https://github.com/langchain-ai/langchain/commit/7c6009b76f0...
- LangChain Repository Disappeared
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🆓 Local & Open Source AI: a kind ollama & LlamaIndex intro
Being able to plug third party frameworks (Langchain, LlamaIndex) so you can build complex projects
What are some alternatives?
langchain - âš¡ Building applications with LLMs through composability âš¡ [Moved to: https://github.com/langchain-ai/langchain]
llama_index - LlamaIndex is a data framework for your LLM applications
FreedomGPT - This codebase is for a React and Electron-based app that executes the FreedomGPT LLM locally (offline and private) on Mac and Windows using a chat-based interface
semantic-kernel - Integrate cutting-edge LLM technology quickly and easily into your apps
LLMZoo - âš¡LLM Zoo is a project that provides data, models, and evaluation benchmark for large language models.âš¡
haystack - :mag: LLM orchestration framework to build customizable, production-ready LLM applications. Connect components (models, vector DBs, file converters) to pipelines or agents that can interact with your data. With advanced retrieval methods, it's best suited for building RAG, question answering, semantic search or conversational agent chatbots.
LoRA - Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"
griptape - Modular Python framework for AI agents and workflows with chain-of-thought reasoning, tools, and memory.
dalai - The simplest way to run LLaMA on your local machine
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
gpt_index - LlamaIndex (GPT Index) is a project that provides a central interface to connect your LLM's with external data. [Moved to: https://github.com/jerryjliu/llama_index]
private-gpt - Interact with your documents using the power of GPT, 100% privately, no data leaks