open_llama
evadb
open_llama | evadb | |
---|---|---|
52 | 27 | |
7,242 | 2,595 | |
0.3% | 1.0% | |
5.3 | 9.3 | |
11 months ago | 18 days ago | |
Python | ||
Apache License 2.0 | Apache License 2.0 |
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_llama
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How Open is Generative AI? Part 2
The RedPajama dataset was adapted by the OpenLLaMA project at UC Berkeley, creating an open-source LLaMA equivalent without Meta’s restrictions. The model's later version also included data from Falcon and StarCoder. This highlights the importance of open-source models and datasets, enabling free repurposing and innovation.
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GPT-4 API general availability
OpenLLaMA is though. https://github.com/openlm-research/open_llama
All of these are surmountable problems.
We can beat OpenAI.
We can drain their moat.
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Recommend me a computer for local a.i for 500 $
#1: 🌞 Open-source Reproduction of Meta AI’s LLaMA OpenLLaMA-13B released. (trained for 1T tokens) | 0 comments #2: 🎉 #1 on HuggingFace.co's Leaderboard Model Falcon 40B is now Free (Apache 2.0 License) | 0 comments #3: 😍 Have you seen this repo? "running LLMs on consumer-grade hardware. compatible models: llama.cpp, alpaca.cpp, gpt4all.cpp, rwkv.cpp, whisper.cpp, vicuna, koala, gpt4all-j, cerebras and many others!" | 0 comments
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Who is openllama from?
Trained OpenLLaMA models are from the OpenLM Research team in collaboration with Stability AI: https://github.com/openlm-research/open_llama
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Personal GPT: A tiny AI Chatbot that runs fully offline on your iPhone
I can't use Llama or any model from the Llama family, due to license restrictions. Although now there's also the OpenLlama family of models, which have the same architecture but were trained on an open dataset (RedPajama, the same dataset the base model in my app was trained on). I'd love to pursue the direction of extended context lengths for on-device LLMs. Likely in a month or so, when I've implemented all the product feature that I currently have on my backlog.
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XGen-7B, a new 7B foundational model trained on up to 8K length for 1.5T tokens
https://github.com/openlm-research/open_llama#update-0615202...).
XGen-7B is probably the superior 7B model, it's trained on more tokens and a longer default sequence length (although both presumably can adopt SuperHOT (Position Interpolation) to extend context), but larger models still probably perform better on an absolute basis.
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MosaicML Agrees to Join Databricks to Power Generative AI for All
Compare it to openllama. It github doesn't have a single script on how to do anything.
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Databricks Strikes $1.3B Deal for Generative AI Startup MosaicML
OpenLLaMA models up to 13B parameters have now been trained on 1T tokens:
https://github.com/openlm-research/open_llama
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Containerized AI before Apocalypse 🐳🤖
The deployed LLM binary, orca mini, has 3 billion parameters. Orca mini is based on the OpenLLaMA project.
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AI — weekly megathread!
OpenLM Research released its 1T token version of OpenLLaMA 13B - the permissively licensed open source reproduction of Meta AI's LLaMA large language model. [Details].
evadb
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Show HN: Stargazers Reloaded – LLM-Powered Analyses of Your GitHub Community
Hey friends!
We have built an app for getting insights about your favorite GitHub community using large language models.
The app uses LLMs to analyze the GitHub profiles of users who have starred the repository, capturing key details like the topics they are interested in. It takes screenshots of the stargazer's GitHub webpage, extracts text using an OCR model, and extracts insights embedded in the extracted text using LLMs.
This app is inspired by the “original” Stargazers app written by Spencer Kimball (CEO of CockroachDB). While the original app exclusively used the GitHub API, this LLM-powered app built using EvaDB additionally extracts insights from unstructured data obtained from the stargazers’ webpages.
Our analysis of the fast-growing GPT4All community showed that the majority of the stargazers are proficient in Python and JavaScript, and 43% of them are interested in Web Development. Web developers love open-source LLMs!
We found that directly using GPT-4 to generate the “golden” table is super expensive — costing $60 to process the information of 1000 stargazers. To maintain accuracy while also reducing cost, we set up an LLM model cascade in a SQL query, running GPT-3.5 before GPT-4, that lowers the cost to $5.5 for analyzing 1000 GitHub stargazers.
We’ve been working on this app for a month now and are excited to open source it today :)
Some useful links:
* Blog Post - https://medium.com/evadb-blog/stargazers-reloaded-llm-powere...
* GitHub Repository - https://github.com/pchunduri6/stargazers-reloaded/
* EvaDB - https://github.com/georgia-tech-db/evadb
Please let us know what you think!
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Language Model UXes in 2027
The discord link seems to be not working. Just a heads up.
The YOLO example on your Github page is super interesting. We are finding it easier to get LLMs to write functions with a more constrained function interface in EvaDB. Here is an example of an YOLO function in EvaDB: https://github.com/georgia-tech-db/evadb/blob/staging/evadb/....
Once the function is loaded, it can be used in queries in this way:
SELECT id, Yolo(data)
- EvaDB: Bring AI to your Database System
- Show HN: I wrote a RDBMS (SQLite clone) from scratch in pure Python
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Gorilla: Large Language Model Connected with APIs
Neat idea, @shishirpatil! We are developing EvaDB [1] for shipping simpler, faster, and cost-effective AI apps. Can you share your thoughts on transforming the output of the Gorilla LLM to functions in EvaDB apps -- like this function that uses the HuggingFace API -- https://evadb.readthedocs.io/en/stable/source/tutorials/07-o...?
[1] https://github.com/georgia-tech-db/eva
- PrivateGPT in SQL
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Eva AI-Relational Database System
Thanks for checking! Currently, we have a Docker image for deploying EVA [1]. We plan to release a Terraform config soon that will make it easier to deploy EVA DB on an AWS/Azure server with GPUs.
[1] https://github.com/georgia-tech-db/eva/tree/master/docker
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This week's top indie A.I projects, launches and resources
EVA AI-Relational Database System; build simpler and faster AI-powered apps
- Show HN: EVA – AI-Relational Database System
What are some alternatives?
FastChat - An open platform for training, serving, and evaluating large language models. Release repo for Vicuna and Chatbot Arena.
txtai - 💡 All-in-one open-source embeddings database for semantic search, LLM orchestration and language model workflows
llama.cpp - LLM inference in C/C++
emdash - 📚🧙♂️ Wisdom indexer — use AI to organize text snippets so you can actually remember & learn from what you read
RWKV-LM - RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.
jsonformer - A Bulletproof Way to Generate Structured JSON from Language Models
gpt4all - gpt4all: run open-source LLMs anywhere
MindsDB - The platform for customizing AI from enterprise data
gorilla - Gorilla: An API store for LLMs
gpt-json - Structured and typehinted GPT responses in Python
ggml - Tensor library for machine learning
mlc-llm - Universal LLM Deployment Engine with ML Compilation