Video wird geladen...

Video konnte nicht geladen werden

Zur Startseite

1/ 🔥 New paper: Differentiable Vector Quantization (DiVeQ) 🔥 Vector quantization (VQ) is a key building block in modern AI. It links continuous data like images and audio to discrete representations (tokens) used by transformers.

arnosolin's profile picture

Arno Solin

2,912 subscribers

78,359 Aufrufe • vor 1 Monat •via X (Twitter)

BildungNachrichten & PolitikWissenschaft & Technologie
LIVE

Anya Rossi• Live Now

Private livecam show

0 Kommentare

Keine Kommentare verfügbar

Kommentare vom Original-Post werden hier angezeigt

Ähnliche Videos

You used AI to build it. Use AI to secure it. Vector finds vulnerabilities before they turn into exploits.
0:11

You used AI to build it. Use AI to secure it. Vector finds vulnerabilities before they turn into exploits.

zauth

2,352,206 Aufrufe • vor 2 Monaten

`transformers` + `torchao` quantization + `torch.compile` for faster inference speed and less memory usage 🔥 Demo of "meta-llama/Meta-Llama-3.1-8B-Instruct" quantized in 4-bit weight-only :
0:11

`transformers` + `torchao` quantization + `torch.compile` for faster inference speed and less memory usage 🔥 Demo of "meta-llama/Meta-Llama-3.1-8B-Instruct" quantized in 4-bit weight-only :

Marc Sun

24,515 Aufrufe • vor 1 Jahr

Sketch got a new haircut on stream and looks like Vector 😭
0:10

Sketch got a new haircut on stream and looks like Vector 😭

yoxic

170,329 Aufrufe • vor 1 Jahr

Figma canvas to build AI agent workflows. Sim is a lightweight, user-friendly platform for building AI agent workflows in minutes. It natively supports all major LLMs, Vector DBs, etc. 100% open-source with 7k+ stars!
0:16

Figma canvas to build AI agent workflows. Sim is a lightweight, user-friendly platform for building AI agent workflows in minutes. It natively supports all major LLMs, Vector DBs, etc. 100% open-source with 7k+ stars!

Avi Chawla

79,322 Aufrufe • vor 9 Monaten

MLX MiniMax 2.5 running LOCALLY on a single M3 Ultra 512GB! Writing a poem on LLMs at 6bit quantization! 🔥 Let's start some coding, context and distributed tests! Generation: 40.2 tokens-per-sec Peak memory: 186 GB
0:11

MLX MiniMax 2.5 running LOCALLY on a single M3 Ultra 512GB! Writing a poem on LLMs at 6bit quantization! 🔥 Let's start some coding, context and distributed tests! Generation: 40.2 tokens-per-sec Peak memory: 186 GB

Ivan Fioravanti ᯅ

226,052 Aufrufe • vor 3 Monaten

Stokes' Theorem is a classic result from vector calculus. It tells us that the line integral of a vector field over a loop is equal to the surface integral of the curl of the vector field over some enclosed surface.
0:12

Stokes' Theorem is a classic result from vector calculus. It tells us that the line integral of a vector field over a loop is equal to the surface integral of the curl of the vector field over some enclosed surface.

Alec Helbling

354,621 Aufrufe • vor 4 Monaten

This is how you can make a shape vector illustration effect using figma & heatmap effect in Paper. Tutorial.
0:16

This is how you can make a shape vector illustration effect using figma & heatmap effect in Paper. Tutorial.

Praveen Kumar👾

65,912 Aufrufe • vor 8 Monaten

TELL HIM VECTOR 🗣️🔥
0:17

TELL HIM VECTOR 🗣️🔥

Funkin 🦉🐓

71,226 Aufrufe • vor 8 Monaten

Vector Database by Hand ✍️ Vector databases are revolutionizing how we search and analyze complex data. They have become the backbone of Retrieval Augmented Generation (#RAG). How do vector databases work? [1] Given ↳ A dataset of three sentences, each has 3 words (or tokens) ↳ In practice, a dataset may contain millions or billions of sentences. The max number of tokens may be tens of thousands (e.g., 32,768 mistral-7b). Process "how are you" [2] 🟨 Word Embeddings ↳ For each word, look up corresponding word embedding vector from a table of 22 vectors, where 22 is the vocabulary size. ↳ In practice, the vocabulary size can be tens of thousands. The word embedding dimensions are in the thousands (e.g., 1024, 4096) [3] 🟩 Encoding ↳ Feed the sequence of word embeddings to an encoder to obtain a sequence of feature vectors, one per word. ↳ Here, the encoder is a simple one layer perceptron (linear layer + ReLU) ↳ In practice, the encoder is a transformer or one of its many variants. [4] 🟩 Mean Pooling ↳ Merge the sequence of feature vectors into a single vector using "mean pooling" which is to average across the columns. ↳ The result is a single vector. We often call it "text embeddings" or "sentence embeddings." ↳ Other pooling techniques are possible, such as CLS. But mean pooling is the most common. [5] 🟦 Indexing ↳ Reduce the dimensions of the text embedding vector by a projection matrix. The reduction rate is 50% (4->2). ↳ In practice, the values in this projection matrix is much more random. ↳ The purpose is similar to that of hashing, which is to obtain a short representation to allow faster comparison and retrieval. ↳ The resulting dimension-reduced index vector is saved in the vector storage. [6] Process "who are you" ↳ Repeat [2]-[5] [7] Process "who am I" ↳ Repeat [2]-[5] Now we have indexed our dataset in the vector database. [8] 🟥 Query: "am I you" ↳ Repeat [2]-[5] ↳ The result is a 2-d query vector. [9] 🟥 Dot Products ↳ Take dot product between the query vector and database vectors. They are all 2-d. ↳ The purpose is to use dot product to estimate similarity. ↳ By transposing the query vector, this step becomes a matrix multiplication. [10] 🟥 Nearest Neighbor ↳ Find the largest dot product by linear scan. ↳ The sentence with the highest dot product is "who am I" ↳ In practice, because scanning billions of vectors is slow, we use an Approximate Nearest Neighbor (ANN) algorithm like the Hierarchical Navigable Small Worlds (HNSW).
0:17

Vector Database by Hand ✍️ Vector databases are revolutionizing how we search and analyze complex data. They have become the backbone of Retrieval Augmented Generation (#RAG). How do vector databases work? [1] Given ↳ A dataset of three sentences, each has 3 words (or tokens) ↳ In practice, a dataset may contain millions or billions of sentences. The max number of tokens may be tens of thousands (e.g., 32,768 mistral-7b). Process "how are you" [2] 🟨 Word Embeddings ↳ For each word, look up corresponding word embedding vector from a table of 22 vectors, where 22 is the vocabulary size. ↳ In practice, the vocabulary size can be tens of thousands. The word embedding dimensions are in the thousands (e.g., 1024, 4096) [3] 🟩 Encoding ↳ Feed the sequence of word embeddings to an encoder to obtain a sequence of feature vectors, one per word. ↳ Here, the encoder is a simple one layer perceptron (linear layer + ReLU) ↳ In practice, the encoder is a transformer or one of its many variants. [4] 🟩 Mean Pooling ↳ Merge the sequence of feature vectors into a single vector using "mean pooling" which is to average across the columns. ↳ The result is a single vector. We often call it "text embeddings" or "sentence embeddings." ↳ Other pooling techniques are possible, such as CLS. But mean pooling is the most common. [5] 🟦 Indexing ↳ Reduce the dimensions of the text embedding vector by a projection matrix. The reduction rate is 50% (4->2). ↳ In practice, the values in this projection matrix is much more random. ↳ The purpose is similar to that of hashing, which is to obtain a short representation to allow faster comparison and retrieval. ↳ The resulting dimension-reduced index vector is saved in the vector storage. [6] Process "who are you" ↳ Repeat [2]-[5] [7] Process "who am I" ↳ Repeat [2]-[5] Now we have indexed our dataset in the vector database. [8] 🟥 Query: "am I you" ↳ Repeat [2]-[5] ↳ The result is a 2-d query vector. [9] 🟥 Dot Products ↳ Take dot product between the query vector and database vectors. They are all 2-d. ↳ The purpose is to use dot product to estimate similarity. ↳ By transposing the query vector, this step becomes a matrix multiplication. [10] 🟥 Nearest Neighbor ↳ Find the largest dot product by linear scan. ↳ The sentence with the highest dot product is "who am I" ↳ In practice, because scanning billions of vectors is slow, we use an Approximate Nearest Neighbor (ANN) algorithm like the Hierarchical Navigable Small Worlds (HNSW).

Tom Yeh

191,919 Aufrufe • vor 2 Jahren

Excited to introduce #Paper2Agent: automatically transform passive paper📜 into active AI agent🤖! Research paper ➡️ paper MCP ➡️ chat agent applying tools/data in the paper to new projects. Key is to use multiple sub-agents to build robust MCP from paper/code. Examples in🧵
0:10

Excited to introduce #Paper2Agent: automatically transform passive paper📜 into active AI agent🤖! Research paper ➡️ paper MCP ➡️ chat agent applying tools/data in the paper to new projects. Key is to use multiple sub-agents to build robust MCP from paper/code. Examples in🧵

James Zou

35,197 Aufrufe • vor 8 Monaten

Drag-and-drop UI to build AI agents! Langflow is a powerful visual tool for building and deploying AI-powered agents and workflows—without writing any code. Supports all major LLMs, vector DBs, etc. 100% open-source, 62k+ stars 🌟
0:11

Drag-and-drop UI to build AI agents! Langflow is a powerful visual tool for building and deploying AI-powered agents and workflows—without writing any code. Supports all major LLMs, vector DBs, etc. 100% open-source, 62k+ stars 🌟

Akshay 🚀

207,932 Aufrufe • vor 1 Jahr

Offset vector paths now available in Figma Draw. You can now take any flattened vector path and offset it to expand or retract it. It even lets you round out those sharp edges.
0:12

Offset vector paths now available in Figma Draw. You can now take any flattened vector path and offset it to expand or retract it. It even lets you round out those sharp edges.

miggi from figgi

54,779 Aufrufe • vor 10 Monaten

K-Means is simple. Making it fast on GPU isn't. Flash-KMeans is an IO-aware implementation of exact k-means that rethinks the algorithm around modern GPU bottlenecks. By attacking the memory bottlenecks directly, Flash-KMeans achieves: - 30x speedup over cuML - 200x speedup over FAISS Using the same exact algorithm, just engineered for today’s hardware. At the million-scale, Flash-KMeans can complete a k-means iteration in milliseconds. Here's why this matters today: K-means has always been an offline primitive. Something you run once to preprocess data and move on. These speedups change that. ↳ Vector databases like FAISS use k-means to build search indices. Faster k-means means you can re-index dynamically as data changes, not batch it overnight. ↳ LLM quantization methods need k-means to find optimal weight codebooks, per layer, repeatedly. What takes hours could now take minutes. ↳ MoE models need fast token routing at inference time. Millisecond k-means makes it viable to run this inside the inference loop, not just in preprocessing. The 200x over FAISS is the number to internalize. FAISS is the industry standard. Most production vector search systems sit on top of it. Link to the paper and code in next tweet!
0:13

K-Means is simple. Making it fast on GPU isn't. Flash-KMeans is an IO-aware implementation of exact k-means that rethinks the algorithm around modern GPU bottlenecks. By attacking the memory bottlenecks directly, Flash-KMeans achieves: - 30x speedup over cuML - 200x speedup over FAISS Using the same exact algorithm, just engineered for today’s hardware. At the million-scale, Flash-KMeans can complete a k-means iteration in milliseconds. Here's why this matters today: K-means has always been an offline primitive. Something you run once to preprocess data and move on. These speedups change that. ↳ Vector databases like FAISS use k-means to build search indices. Faster k-means means you can re-index dynamically as data changes, not batch it overnight. ↳ LLM quantization methods need k-means to find optimal weight codebooks, per layer, repeatedly. What takes hours could now take minutes. ↳ MoE models need fast token routing at inference time. Millisecond k-means makes it viable to run this inside the inference loop, not just in preprocessing. The 200x over FAISS is the number to internalize. FAISS is the industry standard. Most production vector search systems sit on top of it. Link to the paper and code in next tweet!

Akshay 🚀

36,317 Aufrufe • vor 2 Monaten

This is how you can create a super viral 3D vector effect which makes few people question their existence. Built in Paper. Tutorial.
0:15

This is how you can create a super viral 3D vector effect which makes few people question their existence. Built in Paper. Tutorial.

Praveen Kumar

56,058 Aufrufe • vor 7 Monaten

K-Means is simple. Making it fast on GPU isn't. Flash-KMeans is an IO-aware implementation of exact k-means that rethinks the algorithm around modern GPU bottlenecks. By attacking the memory bottlenecks directly, Flash-KMeans achieves: - 30x speedup over cuML - 200x speedup over FAISS Using the same exact algorithm, just engineered for today’s hardware. At the million-scale, Flash-KMeans can complete a k-means iteration in milliseconds. Here's why this matters today: K-means has always been an offline primitive. Something you run once to preprocess data and move on. These speedups change that. ↳ Vector databases like FAISS use k-means to build search indices. Faster k-means means you can re-index dynamically as data changes, not batch it overnight. ↳ LLM quantization methods need k-means to find optimal weight codebooks, per layer, repeatedly. What takes hours could now take minutes. ↳ MoE models need fast token routing at inference time. Millisecond k-means makes it viable to run this inside the inference loop, not just in preprocessing. The 200x over FAISS is the number to internalize. FAISS is the industry standard. Most production vector search systems sit on top of it. Link to the paper and code in next tweet!
0:13

K-Means is simple. Making it fast on GPU isn't. Flash-KMeans is an IO-aware implementation of exact k-means that rethinks the algorithm around modern GPU bottlenecks. By attacking the memory bottlenecks directly, Flash-KMeans achieves: - 30x speedup over cuML - 200x speedup over FAISS Using the same exact algorithm, just engineered for today’s hardware. At the million-scale, Flash-KMeans can complete a k-means iteration in milliseconds. Here's why this matters today: K-means has always been an offline primitive. Something you run once to preprocess data and move on. These speedups change that. ↳ Vector databases like FAISS use k-means to build search indices. Faster k-means means you can re-index dynamically as data changes, not batch it overnight. ↳ LLM quantization methods need k-means to find optimal weight codebooks, per layer, repeatedly. What takes hours could now take minutes. ↳ MoE models need fast token routing at inference time. Millisecond k-means makes it viable to run this inside the inference loop, not just in preprocessing. The 200x over FAISS is the number to internalize. FAISS is the industry standard. Most production vector search systems sit on top of it. Link to the paper and code in next tweet!

Daily Dose of Data Science

23,748 Aufrufe • vor 1 Monat

Diffusion Transformers aren't just generative models, but also powerful multi-modal encoders. ConceptAttention creates rich heatmaps of text concepts in images from DiT representations. This even works on real images, and can be applied to tasks like segmentation! Demo 👇
0:14

Diffusion Transformers aren't just generative models, but also powerful multi-modal encoders. ConceptAttention creates rich heatmaps of text concepts in images from DiT representations. This even works on real images, and can be applied to tasks like segmentation! Demo 👇

Alec Helbling

24,380 Aufrufe • vor 1 Jahr

Fighter jet shows off its insane thrust vector🔥
0:13

Fighter jet shows off its insane thrust vector🔥

Defense Intelligence

30,557 Aufrufe • vor 10 Monaten

🚨BREAKING: THIS AI AGENT OUTPERFORMS HUMAN RESEARCHERS it gathers data, creates tables, generates graphs, incorporates images.. and occasionally even creates images — fully automated 8 insane examples you won't believe: 1. one-shots a research paper with illustrations and diagrams
0:16

🚨BREAKING: THIS AI AGENT OUTPERFORMS HUMAN RESEARCHERS it gathers data, creates tables, generates graphs, incorporates images.. and occasionally even creates images — fully automated 8 insane examples you won't believe: 1. one-shots a research paper with illustrations and diagrams

Nelly;

52,395 Aufrufe • vor 11 Monaten

New broadcast card on VECTOR is out. Flick through multiple trades in the same cycle LEFT to RIGHT, like a story on instagram. New way to express yourself as trader. New way to consume trades. Update to latest app and tell us wyt!
0:16

New broadcast card on VECTOR is out. Flick through multiple trades in the same cycle LEFT to RIGHT, like a story on instagram. New way to express yourself as trader. New way to consume trades. Update to latest app and tell us wyt!

ilmoi | Tensor 3.0 👨‍🍳

20,191 Aufrufe • vor 1 Jahr