Simon @kdbsimon
A.I Engineer Joined June 2023-
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🔍 🤖 Gemini Research Assistant A fullstack AI assistant that uses Gemini models and LangGraph to perform intelligent web research with reflective reasoning, continuously improving its search strategy. Explore the implementation 📚 github.com/google-gemini/…
[Financial Multi-Agent🦜🕸️] I built MultiAgent based on my experience when I was in stocks. Two React Agents and a Supervision agent help beginners with stocks @LangChainAI github.com/jh941213/multi… #langchain #langgraph #llm #agent #ai
QLoRA fine-tuning 4-bit Gemma 2B on iPhone 15 Pro with MLX Swift. A nice size for fine-tuning on device, getting 70-100 toks/sec depending on the batch. Guide here: github.com/ml-explore/mlx…
Llama 3 running locally on iPhone with MLX Built by @exolabs_ team @mo_baioumy h/t @awnihannun MLX & @Prince_Canuma for the port
HuggingFace Introduces Quanto: A Python Quantization Toolkit to Reduce the Computational and Memory Costs of Evaluating Deep Learning Models Quick read: marktechpost.com/2024/03/23/hug… Github: github.com/huggingface/qu… #ArtificialIntelligence
✨New Long-Form (5.5 hour) Retrieval Augmented Generation (RAG) tutorial is live! 🔨 What's inside? • Build a RAG pipeline from scratch (coding every line). • Create "NutriChat", chat w/ 1200 page nutrition textbook PDF. • Use free open-source models available on…
We've been working on generating high-quality & diverse synthetic QA pairs from documents. I've developed a paradigm we've implemented in Ragas. ⭐️ Here's how it works: 1/n github.com/explodinggradi…
🪆Matryoshka Retriever A recent blog post by @supabase described a new technique for higher performance retrieval, without compromising accuracy. So we've just released a new retriever in LangChain.js 🦜🔗 which implements this exactly! Use any vector store, and two embedding…
LoRA+: Efficient Low Rank Adaptation of Large Models 100s of LLM papers dropped on arXiv yesterday. This one caught my attention. It proposes LoRA+ which improves performance and finetuning speed (up to ∼ 2X speed up), at the same computational cost as LoRA. Lots of theory in…
The RAG wave is here to stay, but in practice, it's hard to retrieve the right docs w/ embdings, & better IR models are hard to use! Let's fix that: Introducing 🪤RAGatouille, a lib to train&use SotA retrieval model, ColBERT, in just a few lines of code! github.com/bclavie/RAGato…
"Do you really need an agent?" Shared some recent work at @ollama meetup using graphs to reliably express complex logical flows (self-RAG, corrective-RAG) fully local w/ @nomic_ai + @MistralAI + @ollama (w/ JSON mode). Tx @AlexReibman for video! x.com/AlexReibman/st…
"Do you really need an agent?" Shared some recent work at @ollama meetup using graphs to reliably express complex logical flows (self-RAG, corrective-RAG) fully local w/ @nomic_ai + @MistralAI + @ollama (w/ JSON mode). Tx @AlexReibman for video! x.com/AlexReibman/st… https://t.co/vlcoX7curH
Example followed: distilabel.argilla.io/latest/tutoria… Dataset I targeted (first 100): huggingface.co/datasets/jondu… Resulting dataset: huggingface.co/datasets/eren2… Link to the notebook: colab.research.google.com/drive/18WdkHIf… Cost: 1.08$ in openai credits.
과학데이터셋을 활용해서 reward 모델을 강화 할 수 있을까?
🚀 𝐇𝐢𝐐𝐀: 𝐀 𝐇𝐢𝐞𝐫𝐚𝐫𝐜𝐡𝐢𝐜𝐚𝐥 𝐂𝐨𝐧𝐭𝐞𝐱𝐭𝐮𝐚𝐥 𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐑𝐀𝐆 𝐟𝐨𝐫 𝐌𝐚𝐬𝐬𝐢𝐯𝐞 𝐃𝐨𝐜𝐮𝐦𝐞𝐧𝐭𝐬 𝐐𝐀🌟 💡 The usual RAG approach struggles with retrieval accuracy when faced with massive indistinguishable documents comprising text, tables,…
🚀LangGraph + Gemini Pro + Custom Tool + Streamlit = Multi-Agent Application Development Awesome tutorial (YouTube and blog) from Gao Dalie on how to build a multi-agent application with LangGraph and Gemini-Pro YouTube: youtube.com/watch?v=FWBnNc… Blog: levelup.gitconnected.com/langgraph-gemi…
The biggest Collection of Colab Based LLMs Fine tuning Notebooks ✅ Github : github.com/ashishpatel26/… 🌟1k🌟 🌟 Hit star button to save this repo in your profile 1. Efficiently Train Large Language Models with LoRA and Hugging Face 2. Fine-Tune Your Own Llama 2 Model in a…
Corrective RAG Corrective RAG (CRAG) is a recent paper that uses self-reflection to identify and correct problems in retrieval. It first uses a retrieval evaluator to assess the quality of retrieved documents relative to the query. It filters out irrelevant documents and…
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