llmware
pachyderm
llmware | pachyderm | |
---|---|---|
9 | 8 | |
3,173 | 6,078 | |
6.7% | 0.2% | |
9.8 | 9.8 | |
7 days ago | 1 day ago | |
Python | Go | |
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.
llmware
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More Agents Is All You Need: LLMs performance scales with the number of agents
I couldn't agree more. You should check out LLMWare's SLIM agents (https://github.com/llmware-ai/llmware/tree/main/examples/SLI...). It's focusing on pretty much exactly this and chaining multiple local LLMs together.
A really good topic that ties in with this is the need for deterministic sampling (I may have the terminology a bit incorrect) depending on what the model is indended for. The LLMWare team did a good 2 part video on this here as well (https://www.youtube.com/watch?v=7oMTGhSKuNY)
I think dedicated miniture LLMs are the way forward.
Disclaimer - Not affiliated with them in any way, just think it's a really cool project.
- FLaNK Stack Weekly 19 Feb 2024
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Show HN: LLMWare – Small Specialized Function Calling 1B LLMs for Multi-Step RAG
I've been building upon the LLMWare project - https://github.com/llmware-ai/llmware - for the past 3 months. The ability to run these models locally on standard consumer CPUs, along with the abstraction provided to chop and change between models and different processes is really cool.
I think these SLIM models are the start of something powerful for automating internal business processes and enhancing the use case of LLMs. Still kinda blows my mind that this is all running on my 3900X and also runs on a bog standard Hetzner server with no GPU.
- Show HN: LLMWare – Integrated Solution for RAG in Finance and Legal
- Llmware.ai – AI Tools for Financial, Legal and Compliance
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Open Source Advent Fun Wraps Up!
16. LLMWare by Ai Bloks | Github | tutorial
- FLaNK Stack Weekly 16 October 2023
- Strategy for PDF data extraction and Display
pachyderm
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Open Source Advent Fun Wraps Up!
20. Pachyderm | Github | tutorial
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Exploring Open-Source Alternatives to Landing AI for Robust MLOps
Pachyderm specializes in creating compliance-focused pipelines that integrate with enterprise-level storage solutions.
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Show HN: We scaled Git to support 1 TB repos
There are a couple of other contenders in this space. DVC (https://dvc.org/) seems most similar.
If you're interested in something you can self-host... I work on Pachyderm (https://github.com/pachyderm/pachyderm), which doesn't have a Git-like interface, but also implements data versioning. Our approach de-duplicates between files (even very small files), and our storage algorithm doesn't create objects proportional to O(n) directory nesting depth as Xet appears to. (Xet is very much like Git in that respect.)
The data versioning system enables us to run pipelines based on changes to your data; the pipelines declare what files they read, and that allows us to schedule processing jobs that only reprocess new or changed data, while still giving you a full view of what "would" have happened if all the data had been reprocessed. This, to me, is the key advantage of data versioning; you can save hundreds of thousands of dollars on compute. Being able to undo an oopsie is just icing on the cake.
Xet's system for mounting a remote repo as a filesystem is a good idea. We do that too :)
- pachyderm: Data-Centric Pipelines and Data Versioning
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Awesome list of VCs investing in commercial open-source startups
Pachyderm - License prevents competition.
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Airflow's Problem
I was at Airbnb when we open-sourced Airflow, it was a great solution to the problems we had at the time. It's amazing how many more use cases people have found for it since then. At the time it was pretty focused on solving our problem of orchestrating a largely static DAG of SQL jobs. It could do other stuff even then, but that was mostly what we were using it for. Airflow has become a victim of its success as it's expanded to meet every problem which could ever be considered a data workflow. The flaws and horror stories in the post and comments here definitely resonate with me. Around the time Airflow was opensource I starting working on data-centric approach to workflow management called Pachyderm[0]. By data-centric I mean that it's focused around the data itself, and its storage, versioning, orchestration and lineage. This leads to a system that feels radically different from a job focused system like Airflow. In a data-centric system your spaghetti nest of DAGs is greatly simplified as the data itself is used to describe most of the complexity. The benefit is that data is a lot simpler to reason about, it's not a living thing that needs to run in a certain way, it just exists, and because it's versioned you have strong guarantees about how it can change.
[0] https://github.com/pachyderm/pachyderm
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One secret tip for first-time OSS contributors. Shh! 🤫 don't tell anyone else
Here is a demo run of lgtm on pachyderm
- Dud: a tool for versioning data alongside source code, written in Go
What are some alternatives?
llm-client-sdk - SDK for using LLM
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
pinferencia - Python + Inference - Model Deployment library in Python. Simplest model inference server ever.
trivy - Find vulnerabilities, misconfigurations, secrets, SBOM in containers, Kubernetes, code repositories, clouds and more
inference - A fast, easy-to-use, production-ready inference server for computer vision supporting deployment of many popular model architectures and fine-tuned models.
dud - A lightweight CLI tool for versioning data alongside source code and building data pipelines.
openstatus - 🏓 The open-source synthetic & real user monitoring platform 🏓
beneath - Beneath is a serverless real-time data platform ⚡️
megabots - 🤖 State-of-the-art, production ready LLM apps made mega-easy, so you don't have to build them from scratch 🤯 Create a bot, now 🫵
typhoon-orchestrator - Create elegant data pipelines and deploy to AWS Lambda or Airflow
SimplyRetrieve - Lightweight chat AI platform featuring custom knowledge, open-source LLMs, prompt-engineering, retrieval analysis. Highly customizable. For Retrieval-Centric & Retrieval-Augmented Generation.
tsuru - Open source and extensible Platform as a Service (PaaS).