AnglE
llmware
| AnglE | llmware | |
|---|---|---|
| 12 | 9 | |
| 355 | 3,173 | |
| - | 6.7% | |
| 9.2 | 9.8 | |
| about 1 month ago | 5 days ago | |
| Python | Python | |
| MIT License | 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.
AnglE
- FLaNK Stack Weekly 22 January 2024
- Show HN: Sentence Embedding for Vector Search
- UAE: New Sentence Embeddings for RAG | SOTA on MTEB Leaderboard
- [P]UAE: New Sentence Embeddings for RAG | SOTA on MTEB Leaderboard
- [P] UAE: New Sentence Embeddings for RAG | SOTA on MTEB Leaderboard
- Show HN: SOTA Sentence Embeddings on MTEB Leaderboard
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 β 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 β Integrated Solution for RAG in Finance and Legal
- Llmware.ai β 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?
Finetune_LLMs - Repo for fine-tuning Casual LLMs
llm-client-sdk - SDK for using LLM
code-llama-for-vscode - Use Code Llama with Visual Studio Code and the Continue extension. A local LLM alternative to GitHub Copilot.
pinferencia - Python + Inference - Model Deployment library in Python. Simplest model inference server ever.
mteb - MTEB: Massive Text Embedding Benchmark
inference - A fast, easy-to-use, production-ready inference server for computer vision supporting deployment of many popular model architectures and fine-tuned models.
api-for-open-llm - Openai style api for open large language models, using LLMs just as chatgpt! Support for LLaMA, LLaMA-2, BLOOM, Falcon, Baichuan, Qwen, Xverse, SqlCoder, CodeLLaMA, ChatGLM, ChatGLM2, ChatGLM3 etc. εΌζΊε€§ζ¨‘εηη»δΈεη«―ζ₯ε£
openstatus - π The open-source synthetic & real user monitoring platform π
instructor-embedding - [ACL 2023] One Embedder, Any Task: Instruction-Finetuned Text Embeddings
megabots - π€ State-of-the-art, production ready LLM apps made mega-easy, so you don't have to build them from scratch π€― Create a bot, now π«΅
SimplyRetrieve - Lightweight chat AI platform featuring custom knowledge, open-source LLMs, prompt-engineering, retrieval analysis. Highly customizable. For Retrieval-Centric & Retrieval-Augmented Generation.
obsidian-copilot - π€ A prototype assistant for writing and thinking