Загрузка видео...

Не удалось загрузить видео

Возникла проблема при загрузке этого видео. Это может быть связано с временными проблемами сети или видео может быть недоступно.

На главную

LLMs with and without RAG!!

Manohar

2,727 subscribers

56,529 просмотров • 5 месяцев назад •via X (Twitter)

Наука и технологии

Anya Rossi• Live Now

Private livecam show

Комментарии: 16

Фото профиля Teja Karlapudi

Teja Karlapudi5 месяцев назад

Wrong, LLM would spit out random facts in this case. But funny reference though.

Фото профиля Ratna Sekhar ラトナ 🀄️

Ratna Sekhar ラトナ 🀄️5 месяцев назад

Hallucination lo edhoka random answer Cheppakunda silent untad enti Chiranjeevi 😭

Фото профиля Surya 🎬🏏🧡

Surya 🎬🏏🧡5 месяцев назад

@Sriramnaiduuuu Chinna doubt Ee LLM RAG ante enti

Фото профиля RG 🇦🇷

RG 🇦🇷5 месяцев назад

half knowledge tho esadu erripuk gadu

Фото профиля Voice 🏎️

Voice 🏎️5 месяцев назад

😂

Фото профиля Md Mateen Matey🇮🇳

Md Mateen Matey🇮🇳5 месяцев назад

@Riddhishree27 Sanjay Dutt born to play munna bhai, no one can match his aura. Ghatiya remake kare lage raho munna bhai ka

Фото профиля anthro

anthro5 месяцев назад

@RohanNTRCult 🤣

Фото профиля reddrodri

reddrodri5 месяцев назад

😭😭😭😭 found it very funny

Фото профиля guru

guru5 месяцев назад

@DattuClay best reference imo

Фото профиля Rohit 7 🇵🇹

Rohit 7 🇵🇹5 месяцев назад

Movie Name..

Фото профиля SaitejaX

SaitejaX5 месяцев назад

Crazy reference!! 🤣

Фото профиля Dr Shakeel 🇺🇸

Dr Shakeel 🇺🇸5 месяцев назад

😂😂😂

Фото профиля beinghuman18

beinghuman185 месяцев назад

😆😂

Фото профиля NeeVee

NeeVee5 месяцев назад

😂😂😭😂

Фото профиля Buzzzz

Buzzzz5 месяцев назад

🤣🤣🤣

Фото профиля Stallionpanda

Stallionpanda5 месяцев назад

With rag : exam ki chits tho veladam 😜 Without rag : exam ki hall ticket leka veldam 🫣

Похожие видео

Build AI Agents with RAG without writing a single line of Python Code. 100% Opensource and Nocode.

Build AI Agents with RAG without writing a single line of Python Code. 100% Opensource and Nocode.

Unwind AI

70,756 просмотров • 1 год назад

5 AI Building Blocks Every Engineer Should Know - LLMs - Embedding Models - RAG - Agent Harnesses - World Models

5 AI Building Blocks Every Engineer Should Know - LLMs - Embedding Models - RAG - Agent Harnesses - World Models

Alex Xu

22,200 просмотров • 21 дней назад

Fine-tune 100+ LLMs directly from a UI! LLaMA-Factory lets you train and fine-tune open-source LLMs and VLMs without writing any code. Supports 100+ models, multimodal fine-tuning, PPO, DPO, experiment tracking, and much more! 100% open-source with 50k stars!

Fine-tune 100+ LLMs directly from a UI! LLaMA-Factory lets you train and fine-tune open-source LLMs and VLMs without writing any code. Supports 100+ models, multimodal fine-tuning, PPO, DPO, experiment tracking, and much more! 100% open-source with 50k stars!

Avi Chawla

557,791 просмотров • 1 год назад

Build a local LLM app with RAG to chat with Google Drive data using Mistral or Llama-3 running locally on your computer without wiring a single line of Python Code (100% free and without internet):

Build a local LLM app with RAG to chat with Google Drive data using Mistral or Llama-3 running locally on your computer without wiring a single line of Python Code (100% free and without internet):

Shubham Saboo

189,778 просмотров • 2 лет назад

Prof. David Krakauer says intelligence is not knowledge. True intelligence is doing more with less. Solving problems without knowing everything. LLMs do the opposite. They do more with more. LLMs are libraries that know everything.

Prof. David Krakauer says intelligence is not knowledge. True intelligence is doing more with less. Solving problems without knowing everything. LLMs do the opposite. They do more with more. LLMs are libraries that know everything.

vitrupo

81,302 просмотров • 1 год назад

NeurIPS 2025 Paper: LLMs are Reinforcement Learners 🤯! Surprisingly, we show that LLMs can solve RL tasks without any external component! We introduce Prompted Policy Search (ProPS), an RL method based only LLMs and in-context learning. [Paper]

NeurIPS 2025 Paper: LLMs are Reinforcement Learners 🤯! Surprisingly, we show that LLMs can solve RL tasks without any external component! We introduce Prompted Policy Search (ProPS), an RL method based only LLMs and in-context learning. [Paper]

Heni Ben Amor

51,248 просмотров • 11 месяцев назад

cleaning Impulse Labs — since people asked. I just use a wet rag with soapy water and then a dry rag.

cleaning Impulse Labs — since people asked. I just use a wet rag with soapy water and then a dry rag.

Sam D'Amico

55,348 просмотров • 2 лет назад

Hugging Face just dropped AI sheets to build and enrich datasets without writing a single line of code. Works with Qwen, Kimi, Llama 3 and other opensource LLMs. 100% Free, local and Opensource.

Hugging Face just dropped AI sheets to build and enrich datasets without writing a single line of code. Works with Qwen, Kimi, Llama 3 and other opensource LLMs. 100% Free, local and Opensource.

Shubham Saboo

65,311 просмотров • 1 год назад

Introducing ModelHub! 📦 So I had been experimenting with a lot of local LLMs and was having an insanely difficult time managing the models and my laptop's storage! So I built ModelHub - It helps you discover, download, and manage LLMs from huggingface, Im studio, ollama, mlx, and Ilama.cpp - without ever leaving your menu bar.

Introducing ModelHub! 📦 So I had been experimenting with a lot of local LLMs and was having an insanely difficult time managing the models and my laptop's storage! So I built ModelHub - It helps you discover, download, and manage LLMs from huggingface, Im studio, ollama, mlx, and Ilama.cpp - without ever leaving your menu bar.

sabesh 📟

52,654 просмотров • 6 месяцев назад

The future of AI is open-source. And ollama is the easiest way to build AI applications with open-source LLMs. Here's how to build a free, private RAG app using open-source tools. We'll use: - Ollama for LLMs and embedding models - PostgreSQL for data storage and retrieval - pgai Vectorizer for embedding creation and sync (I use Nomic for embeddings and tinnyllama as my LLM but you can substitute them for any models on Ollama)

The future of AI is open-source. And ollama is the easiest way to build AI applications with open-source LLMs. Here's how to build a free, private RAG app using open-source tools. We'll use: - Ollama for LLMs and embedding models - PostgreSQL for data storage and retrieval - pgai Vectorizer for embedding creation and sync (I use Nomic for embeddings and tinnyllama as my LLM but you can substitute them for any models on Ollama)

Avthar

34,261 просмотров • 1 год назад

RAG is dead. long live RAG — index, generate embeddings and start querying in just a few steps with the new @cloudflaredev autoRAG

RAG is dead. long live RAG — index, generate embeddings and start querying in just a few steps with the new @cloudflaredev autoRAG

rita kozlov 🐀

108,052 просмотров • 1 год назад

Black Mountain Rag with the one and only Bryan Sutton 🔒

Black Mountain Rag with the one and only Bryan Sutton 🔒

Billy Strings

31,794 просмотров • 2 лет назад

We’re excited to introduce RAGs v2 - build, customize, and use multiple ChatGPTs over your data, all with natural language 💬 A huge upgrade vs. the initial launch: 💫 Easily create multiple RAG pipelines and save them 💫 Easily swap between and customize each one (e.g. over different data, or w/ different system prompts) 💫 Delete unused RAG pipelines 💫 (dev quality) added much-needed linting/CI Check out the video 🎥 for details. It’s super easy to setup and use. Some additional features: 🧠 Supports a lot of LLMs both for building RAG and within each RAG pipeline 🌐 Supports loading load files or web pages. Check out our repo here:

We’re excited to introduce RAGs v2 - build, customize, and use multiple ChatGPTs over your data, all with natural language 💬 A huge upgrade vs. the initial launch: 💫 Easily create multiple RAG pipelines and save them 💫 Easily swap between and customize each one (e.g. over different data, or w/ different system prompts) 💫 Delete unused RAG pipelines 💫 (dev quality) added much-needed linting/CI Check out the video 🎥 for details. It’s super easy to setup and use. Some additional features: 🧠 Supports a lot of LLMs both for building RAG and within each RAG pipeline 🌐 Supports loading load files or web pages. Check out our repo here:

LlamaIndex 🦙

124,202 просмотров • 3 лет назад

Fine-tune 100+ LLMs directly from a UI! LLaMA-Factory lets you train and fine-tune open-source LLMs and VLMs without writing any code. Supports 100+ models, multimodal fine-tuning, PPO, DPO, experiment tracking, and much more! 100% open-source, 51k+ stars 🌟

Fine-tune 100+ LLMs directly from a UI! LLaMA-Factory lets you train and fine-tune open-source LLMs and VLMs without writing any code. Supports 100+ models, multimodal fine-tuning, PPO, DPO, experiment tracking, and much more! 100% open-source, 51k+ stars 🌟

Akshay 🚀

60,627 просмотров • 1 год назад

Our new RAG CLI tool lets you RAG any file on your filesystem, using only on-device LLMs/embeddings! 💾🔎 Using LlamaIndex 🦙, Mistral-7B on ollama, and bge-m3, you have access to an LLM-powered grep that you can easily install, use, and customize. Can input any files including glob strings. LlamaPack: We also have the recipe for you to build your own RAG CLI from scratch. Check out this excellent guide by Sourabh Desai:

Our new RAG CLI tool lets you RAG any file on your filesystem, using only on-device LLMs/embeddings! 💾🔎 Using LlamaIndex 🦙, Mistral-7B on ollama, and bge-m3, you have access to an LLM-powered grep that you can easily install, use, and customize. Can input any files including glob strings. LlamaPack: We also have the recipe for you to build your own RAG CLI from scratch. Check out this excellent guide by Sourabh Desai:

LlamaIndex 🦙

40,124 просмотров • 2 лет назад

Making LLMs pause to think improves safety without sacrificing smarts. Yinpeng Dong's (Tsinghua) STAIR framework teaches LLMs to reason through risks step-by-step. Method: structured CoT→MCTS→test-time scaling. Application: DeepSeek R1→RealSafe R1 with major safety gains 👇

Making LLMs pause to think improves safety without sacrificing smarts. Yinpeng Dong's (Tsinghua) STAIR framework teaches LLMs to reason through risks step-by-step. Method: structured CoT→MCTS→test-time scaling. Application: DeepSeek R1→RealSafe R1 with major safety gains 👇

FAR.AI

21,770 просмотров • 1 год назад

🇷🇺 Russians enjoy vacationing in 🇹🇷Türkiye and hanging their tricolor rag around, but they don’t like the fact that people associate their rag with shit 😁

🇷🇺 Russians enjoy vacationing in 🇹🇷Türkiye and hanging their tricolor rag around, but they don’t like the fact that people associate their rag with shit 😁

Victoria

169,479 просмотров • 2 лет назад

Pictionary, but with open source LLMs

Pictionary, but with open source LLMs

Hassan

58,598 просмотров • 28 дней назад