LLMs-from-scratch
qdrant
LLMs-from-scratch | qdrant | |
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11 | 143 | |
18,902 | 18,501 | |
- | 3.0% | |
9.6 | 9.9 | |
7 days ago | 5 days ago | |
Jupyter Notebook | Rust | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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LLMs-from-scratch
- Evaluating LLMs locally, on a laptop, with Llama 3 and Ollama
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Ask HN: What are some books/resources where we can learn by building
By happenchance today I learned that Manning recently started working on publishing a X From Scratch series, which currently includes:
* Container Orchestrator: https://www.manning.com/books/build-an-orchestrator-in-go-fr...
* LLM : https://www.manning.com/books/build-a-large-language-model-f...
* Frontend Framework: https://www.manning.com/books/build-a-frontend-web-framework...
- Finetuning an LLM-Based Spam Classifier with LoRA from Scratch
- Finetune a GPT Model for Spam Detection on Your Laptop in Just 5 Minutes
- Insights from Finetuning LLMs for Classification Tasks
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Ask HN: Textbook Regarding LLMs
https://www.manning.com/books/build-a-large-language-model-f...
- Comparing 5 ways to implement Multihead Attention in PyTorch
- FLaNK Stack 29 Jan 2024
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Implementing a ChatGPT-like LLM from scratch, step by step
The attention mechanism we implement in this book* is specific to LLMs in terms of the text inputs, but it's fundamentally the same attention mechanism that is used in vision transformers. The only difference is that in LLMs, you turn text into tokens, and convert these tokens into vector embeddings that go into an LLM. In vision transformers, instead of regarding images as tokens, you use an image patch as a token and turn those into vector embeddings (a bit hard to explain without visuals here). In both text or vision context, it's the same attention mechanism, and it both cases it receives vector embeddings.
(*Chapter 3, already submitted last week and should be online in the MEAP soon, in the meantime the code along with the notes is also available here: https://github.com/rasbt/LLMs-from-scratch/blob/main/ch03/01...)
qdrant
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WizSearch: 🏆 Winning My First AI Hackathon 🚀
Vector Databases: Qdrant for efficient data storage and retrieval.
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How to Build a Chat App with Your Postgres Data using Agent Cloud
AgentCloud uses Qdrant as the vector store to efficiently store and manage large sets of vector embeddings. For a given user query the RAG application fetches relevant documents from vector store by analyzing how similar their vector representation is compared to the query vector.
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Hindi-Language AI Chatbot for Enterprises Using Qdrant, MLFlow, and LangChain
Great. Now that we have the embeddings, we need to store them in a vector database. We will be using Qdrant for this purpose. Qdrant is an open-source vector database that allows you to store and query high-dimensional vectors. The easiest way to get started with the Qdrant database is using the docker.
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Boost Your Code's Efficiency: Introducing Semantic Cache with Qdrant
I took Qdrant for this project. The reason was that Qdrant stands for high-performance vector search, the best choice against use cases like finding similar function calls based on semantic similarity. Qdrant is not only powerful but also scalable to support a variety of advanced search features that are greatly useful to nuanced caching mechanisms like ours.
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Ask HN: Has Anyone Trained a personal LLM using their personal notes?
I'm currently looking to implement locally, using QDrant [1] for instance.
I'm just playing around, but it makes sense to have a runnable example for our users at work too :) [2].
[1]. https://qdrant.tech/
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Show HN: A fast HNSW implementation in Rust
Also compare with qdrant's Rust implementation; they tout their performance. https://github.com/qdrant/qdrant/tree/master/lib/segment/src...
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pgvecto.rs alternatives - qdrant and Weaviate
3 projects | 13 Mar 2024
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Open-source Rust-based RAG
There are much better known examples, such as https://qdrant.tech/ and https://github.com/lancedb/lancedb
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Qdrant 1.8.0 - Major Performance Enhancements
For more information, see our release notes. Qdrant is an open source project. We welcome your contributions; raise issues, or contribute via pull requests!
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Perform Image-Driven Reverse Image Search on E-Commerce Sites with ImageBind and Qdrant
Initialize the Qdrant Client with in-memory storage. The collection name will be “imagebind_data” and we will be using cosine distance.
What are some alternatives?
s4 - Structured state space sequence models
Milvus - A cloud-native vector database, storage for next generation AI applications
Weaviate - Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database​.
faiss - A library for efficient similarity search and clustering of dense vectors.
pgvector - Open-source vector similarity search for Postgres
Elasticsearch - Free and Open, Distributed, RESTful Search Engine
towhee - Towhee is a framework that is dedicated to making neural data processing pipelines simple and fast.
vespa - AI + Data, online. https://vespa.ai
hnswlib - Header-only C++/python library for fast approximate nearest neighbors
awesome-vector-search - Collections of vector search related libraries, service and research papers
google-research - Google Research
khoj - Your AI second brain. Get answers to your questions, whether they be online or in your own notes. Use online AI models (e.g gpt4) or private, local LLMs (e.g llama3). Self-host locally or use our cloud instance. Access from Obsidian, Emacs, Desktop app, Web or Whatsapp.