open-llms
awesome-ml
open-llms | awesome-ml | |
---|---|---|
22 | 27 | |
10,168 | 1,402 | |
- | - | |
7.7 | 8.8 | |
about 1 month ago | 9 days ago | |
Apache License 2.0 | 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.
open-llms
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7 SAAS ideas 💡 you can steal
Everyone knows about ChatGPT by now, but did you know there are other models like "Mistral" or "Falcon" - you can view a full list of open-source models here or on huggingface.
- eugeneyan/open-llms
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GPT-4 API general availability
This is the most well-maintained list of commercially usable open LLMs: https://github.com/eugeneyan/open-llms
MPT, OpenLLaMA, and Falcon are probably the most generally useful.
For code, Replit Code (specifically replit-code-instruct-glaive) and StarCoder (WizardCoder-15B) are the current top open models and both can be used commercially.
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Local LLMs: After Novelty Wanes
There's also MPT, which has a 7B, and Falcon, with a 7B and 40B although they have not had the inference tuning in community projects that the llamas have had. This is a good repo for reviewing what's available atm: https://github.com/eugeneyan/open-llms
- How to keep track of all the LLMs out there?
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How do I learn AI/Machine Learning?
If I was going to do the same I would at least build off of something, check out https://github.com/eugeneyan/open-llms, you should at least have a decent understanding of artificial neural networks (ANNs) and this link is pretty good on the basic concepts you need inc classification and learning types, good luck friend.
- LLM and privacy
- Local LLM to learn, explore and use for commercial purpose
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Best instruct model recommendations to use with T4?
This list might help: https://github.com/eugeneyan/open-llms
- [D] What is the best open source LLM so far?
awesome-ml
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AI Infrastructure Landscape
I do something like that for open source:
https://github.com/underlines/awesome-ml
But it lost a bit of traction lately.
It needs re-work for the categories, or better, a tagging system, because these products and libraries can sit in more than one space.
Plus it either needs massive collaboration, or some form of automation (with an LLM and indexer), as I can't keep up with it.
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OpenVoice: Versatile Instant Voice Cloning
This aera is barely new. Look at how old some of the projects are:
https://github.com/underlines/awesome-ml/blob/master/audio-a...
The thing that changes is the complexity to run it. I was training my wife's voice and my voice for fun and needed 15min of audio and trained on my 3080 for 40 minutes.
Now it's 2 Minutes.
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Show HN: Floneum, a graph editor for local AI workflows
Thanks for your clarifications. I added it to my awesome list:
https://github.com/underlines/awesome-marketing-datascience/...
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AI for AWS Documentation
RAG is very difficult to do right. I am experimenting with various RAG projects from [1]. The main problems are:
- Chunking can interfer with context boundaries
- Content vectors can differ vastly from question vectors, for this you have to use hypothetical embeddings (they generate artificial questions and store them)
- Instead of saving just one embedding per text-chuck you should store various (text chunk, hypothetical embedding questions, meta data)
- RAG will miserably fail with requests like "summarize the whole document"
- to my knowledge, openAI embeddings aren't performing well, use a embedding that is optimized for question answering or information retrieval and supports multi language. Also look into instructor embeddings: https://github.com/embeddings-benchmark/mteb
1 https://github.com/underlines/awesome-marketing-datascience/...
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Explore and compare the parameters of top-performing LLMs
I do the same and with currently with 700+ github stars people seem to like it, but it's still curated/manual, because the hf search API is so limited and I don't have the time to create a scraper.
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Vicuna v1.3 13B and 7B released, trained with twice the amount of ShareGPT data
Added to the list
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Useful Links and Info
I keep mine fairly up to date as well, almost daily: https://github.com/underlines/awesome-marketing-datascience/blob/master/README.md
- How to keep track of all the LLMs out there?
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Run and create custom ChatGPT-like bots with OpenChat
Disclaimer: I am curating LLM-tools on github [1]
A few thoughts:
* allow for custom endpoint URLs, this way people can use open source LLMs with a fake openAI API backend like basaran[2] or llama-api-server[3]
* look into better embedding methods for info-retrieval like InstructorEmbeddings or Document Summary Index
* Don't use a single embedding per content item, use multiple to increase retrieval quality
1 https://github.com/underlines/awesome-marketing-datascience/...
2 https://github.com/hyperonym/basaran
3 https://github.com/iaalm/llama-api-server
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Seeking clarification about LLM's, Tools, etc.. for developers.
Oobabooga isn't a wrapper for llama.cpp, but it can act as such. A usual Oobabooga installation on windows will use a GPTQ wheel (binary) compiled for cuda/windows, or alternatively use llama.cpp's API and act as a GUI. On Linux you had the choice to use the triton or cuda branch for GPTQ, but I don't know if that is still the case. You can also go the route to use virtualized and hardware accelerated WSL2 Ubuntu on Windows and use anything similar to linux. See my guide
What are some alternatives?
SillyTavern - LLM Frontend for Power Users.
anything-llm - The all-in-one Desktop & Docker AI application with full RAG and AI Agent capabilities.
SillyTavern-Extras - Extensions API for SillyTavern.
OpenChat - LLMs custom-chatbots console âš¡
FastChat - An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.
AGiXT - AGiXT is a dynamic AI Agent Automation Platform that seamlessly orchestrates instruction management and complex task execution across diverse AI providers. Combining adaptive memory, smart features, and a versatile plugin system, AGiXT delivers efficient and comprehensive AI solutions.
llm-jeopardy - Automated prompting and scoring framework to evaluate LLMs using updated human knowledge prompts
llama-mps - Experimental fork of Facebooks LLaMa model which runs it with GPU acceleration on Apple Silicon M1/M2
azure-search-openai-demo - A sample app for the Retrieval-Augmented Generation pattern running in Azure, using Azure AI Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences.
mnotify - A matrix cli client
panml - PanML is a high level generative AI/ML development and analysis library designed for ease of use and fast experimentation.
mteb - MTEB: Massive Text Embedding Benchmark