Kalyan KS’ Post

𝐑𝐀𝐆-𝐅𝐥𝐨𝐰 : 𝐎𝐩𝐞𝐧-𝐒𝐨𝐮𝐫𝐜𝐞 𝐑𝐀𝐆 𝐄𝐧𝐠𝐢𝐧𝐞 RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding. RAGFlow - https://lnkd.in/ezgezmGB

This could be a great starting point for learning about LLMs, especially to understand the importance of fine-tuning and how to approach it. It provides a detailed guide on fine-tuning LLMs for domain-specific datasets. #Finetune_LLM #Deeplearning_AI

𝐋𝐋𝐌2𝐕𝐞𝐜 - 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦 𝐋𝐋𝐌𝐬 𝐢𝐧𝐭𝐨 𝐄𝐦𝐛𝐞𝐝𝐝𝐢𝐧𝐠 𝐌𝐨𝐝𝐞𝐥𝐬 LLM2Vec, a simple unsupervised approach that can transform any decoder-only LLM into a strong text encoder. LLM2Vec consists of three simple steps: 1) enabling bidirectional attention, 2) masked next token prediction, and 3) unsupervised contrastive learning. LLM2Vec not only outperforms encoder-only models on word-level tasks but also achieves new SOTA results on the MTEB benchmark. To summarize, LLM2Vec shows that without expensive adaptation or synthetic GPT-4 data, LLMs can be transformed into embedding models (universal text encoders) LLM2Vec paper -https://lnkd.in/geWcN9wf hashtag #llm2vec hashtag #embeddings hashtag #nlproc hashtag #llms hashtag #generativeai hashtag #deeplearning

🚀 Day 19/30: LeetCode DSA Today’s problem was Reverse Pairs—a challenging question that tests the efficiency of sorting algorithms in handling large datasets. The problem is a mix of merge sort and mathematical reasoning, which made it even more interesting! What I learned: How to modify merge sort to count pairs while maintaining the O(nlog⁡n)O(n \log n)O(nlogn) time complexity. Handling integer overflow by casting values to long long for safe comparisons, which is crucial when working with large numbers. 🔑 Key Insight: To avoid signed integer overflow, casting to a larger data type is essential when multiplying large integers, which saved me from runtime errors! #LeetCode #DSA #100DaysOfCode #CodingChallenge #ProblemSolving #TechJourney #Algorithms #DataStructures

I use the large language models daily. Using these new models to structure data is one of my favorite use cases.

Datasets for LLMs evaluation. Ensuring accurate model evaluations: open-sourced, cleaned datasets for models that reason and code https://lnkd.in/gQsb9CAY #datasets #llmsevaluation #responsibleAI #aiperformance

Output parsers are essential for converting raw, unstructured text from language models (LLMs) into structured formats, such as JSON or Pydantic models, making it easier for downstream tasks. While function or tool calling can automate this transformation in many LLMs, output parsers are still valuable for generating structured data or normalizing model outputs: https://lnkd.in/gQFB5gug #AnalyticsVidhya #GenerativeAI #Agents #LLMs

I have completed the "𝐈𝐦𝐩𝐫𝐨𝐯𝐢𝐧𝐠 𝐀𝐜𝐜𝐮𝐫𝐚𝐜𝐲 𝐨𝐟 𝐋𝐋𝐌 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬" course by DeepLearning.AI. It's an informative deep-dive into advanced techniques to enhance the performance and precision of large language models. 🔗 https://lnkd.in/dSX6qjaa #LLM #Accuracy #Finetuning #MemoryFineTuning #DataPreparation #Data #SQL #SQLQueryLLM #LLMApplications

When working with machine learning models, you'll often need to fine tune them for your own data. In this course, you'll learn how to fine tune Large Language Models. Krish teaches you about the LORA & QLORA techniques, quantization, gradients, & more.

Challenges in Building Large Language Models (LLMs) 1. Outdated Information: LLMs can often provide outdated information as they are trained on static datasets. Regular updates are necessary to maintain accuracy and right responses. 2. Incorrect Mathematical Answers: When it comes to Mathematical calcualtions LLMs often give incorrect responses as they often predict the next word. Integrating mathematical solvers can help mitigate this issue. 3. Hallucinated Responses: LLMs can generate incorrect or nonsensical answers when unsure. Detecting and correcting these "hallucinations" is crucial for reliability. #llms #hallucinations #buildllms