information-retrieval
llmware
| information-retrieval | llmware | |
|---|---|---|
| 3 | 9 | |
| 147 | 3,173 | |
| - | 5.3% | |
| 0.0 | 9.8 | |
| 9 months ago | about 11 hours ago | |
| Jupyter Notebook | Python | |
| - | Apache License 2.0 |
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information-retrieval
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[D] Generative vs embedding models
You can check out my repo https://github.com/kuutsav/information-retrieval. Here I've implemented some of the common embedding techniques from sbert from scratch.
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Bi-Encoder with BERT does not learn
I have a bunch of training scripts here that night help you figure out the bug(if any) https://github.com/kuutsav/information-retrieval
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[P] Created tutorials on Information Retrieval, specifically Semantic Search
Hi, I've created a repo which tries to cover the current progress in the world of information-retrieval using neural information retrievers / semantic search. Repo: https://github.com/kuutsav/information retrieval .
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
What are some alternatives?
elastic_transformers - Making BERT stretchy. Semantic Elasticsearch with Sentence Transformers
llm-client-sdk - SDK for using LLM
cherche - Neural Search
pinferencia - Python + Inference - Model Deployment library in Python. Simplest model inference server ever.
ttds-cw3-research-team - A Search Engine To Find Research Papers & Datasets
inference - A fast, easy-to-use, production-ready inference server for computer vision supporting deployment of many popular model architectures and fine-tuned models.
openstatus - 🏓 The open-source synthetic & real user monitoring platform 🏓
SimplyRetrieve - Lightweight chat AI platform featuring custom knowledge, open-source LLMs, prompt-engineering, retrieval analysis. Highly customizable. For Retrieval-Centric & Retrieval-Augmented Generation.
Wails - Create beautiful applications using Go
obsidian-copilot - 🤖 A prototype assistant for writing and thinking
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 🫵
vectorflow - VectorFlow is a high volume vector embedding pipeline that ingests raw data, transforms it into vectors and writes it to a vector DB of your choice.