IN TODAY'S SIGNAL |
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Read time: 3 min 34 sec |
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🎖️ Top News
📌 Gretel
⚡️ Trending Signals
🛠️ Top Repos
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Stanford Storm: Write Wikipedia-like articles with LLMs and retrieval methods.
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llm-graph-builder: Create knowledge graphs from unstructured data using LLMs.
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NVIDIA Warp: High-performance Python for simulations, integrates with PyTorch and JAX.
🧠 Tutorial
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TOP NEWS |
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Language Models |
FlashAttention-3: Making Attention 16x faster for Language Models |
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⇧ 1914 Likes |
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What's New |
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FlashAttention is widely used to accelerate Transformers, already making attention 4-8x faster, but has yet to take advantage of modern GPUs.
FlashAttention-3: achieves a 1.5-2x speedup and reaching up to 740 TFLOPS on FP16 and nearly 1.2 PFLOPS on FP8. This increases GPU utilization to 75% of the theoretical maximum on H100 GPUs, up from 35%.
Core Innovations and Techniques
FlashAttention-3 introduces three main techniques to boost performance:
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Overlapping computation and data movement
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Interleaving matrix multiplications (matmul)
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Softmax operations, and using low-precision FP8
These techniques leverage new hardware features of Hopper GPUs like WGMMA (Warpgroup Matrix Multiply-Accumulate) and TMA (Tensor Memory Accelerator), which enhance throughput and efficiency.
Overlapping Operations for Increased Efficiency
FlashAttention-3 uses asynchrony to perform multiple operations simultaneously. By overlapping the main operations (matrix multiplications and softmax), it ensures the GPU stays busy and efficient.
Warp specialization means different groups of GPU threads (warps) handle different tasks at the same time.
Key Highlights and Performance Metrics
- Speedup: 1.5-2 times faster than FlashAttention-2
- Throughput: Up to 740 TFLOPS on FP16, nearly 1.2 PFLOPS on FP8
- GPU Utilization: Increased to 75% on H100 GPUs
- Quantization Error Reduction: Up to 2.6 times lower with incoherent processing
Read the blog post |
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| CHECK THE REPO |
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Webinar: Learn How To Accelerate AI Development with Synthetic Data |
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Discover how synthetic data is revolutionizing AI development by providing access to high-quality synthetic data!
In this upcoming webinar, Alex Watson, Co-Founder and CPO of Gretel, and Gretel’s product team will dive into the pivotal role synthetic data plays in generative AI and preview the latest Gretel platform additions that make data generation easier than before.
This webinar will conclude with a live Q&A and open discussion with genAI and synthetic data experts. |
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partner with us |
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TRENDING SIGNALS |
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Language Models |
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⇧ 6104 Likes |
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Text-to-Speech |
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⇧ 260 Likes |
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Anthropic |
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⇧ 1429 Likes |
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No-Code |
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⇧ 166 Likes |
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AGI |
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⇧ 402 Likes |
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TOP OF GITHUB |
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Knowledge Curation |
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STORM helps you write Wikipedia-like articles by researching topics, generating outlines, and creating full-length reports with citations using large language models and retrieval methods like You.com and Bing Search. |
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☆ 5666 |
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Graphs |
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Turn Unstructured data (pdfs,docs,txt,youtube video,web pages,etc.) into a knowledge graph stored in Neo4j. It utilizes the power of Large language models (OpenAI,Gemini,etc.) to extract nodes. |
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☆ 556 |
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Simulations |
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Warp helps you write high-performance Python code for simulations and graphics, running efficiently on CPU or GPU. It supports physics, robotics, geometry, and integrates with PyTorch and JAX for ML. |
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☆ 3701 |
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TUTORIAL |
How to Embed 100M Docs with 300mb of Memory |
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GPU-poor and Memory-poor, and not having 500GB of memory to embed & index 100M docs?
DiskVectorIndex is a vector search solution for memory-constrained environments.
Traditional vector databases require about 500GB of memory for such tasks, but DiskVectorIndex uses advanced vector compression techniques, such as Product Quantization (PQ), to reduce this requirement to around 300MB.
It is ideal for applications with limited GPU and memory resources, enabling efficient participation in large-scale search tasks like TREC-RAG 2024. The system leverages faiss for memory-mapped Inverted File (IVF) indexing, ensuring fast search performance with minimal memory overhead. |
# pip install DiskVectorIndex
from DiskVectorIndex import DiskVectorIndex
# 114M embeddings with just 100MB of RAM
index = DiskVectorIndex("Cohere/trec-rag-2024-index")
while True:
query = input("
Enter a question: ")
docs = index.search(query, top_k=3)
for doc in docs:
print(doc)
print("========")
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LAST WEEK'S GREATEST HITS |
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Master unstructured data and deep document understanding with RAGFlow
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The ultimate course for mastering retrieval-augmented generation (RAG)
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Real-time Detection Transformer (RT-DETR) models now available
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