Day 11/50: Building a small language from scratch: Introduction to the Attention Mechanism in Large Language Models (LLMs)
preview.redd.it·10h·
Discuss: r/LocalLLaMA
📝Text Parsing
MGAA: Multi-Granular Adaptive Allocation fof Low-Rank Compression of LLMs
arxiv.org·10h
🕸️Sparse Embeddings
Shrinking LLMs With Self-Compression
semiengineering.com·7h
🔢BitNet
Economics of Claude 3 Inference
lesswrong.com·20h
📊Model Serving Economics
Researchers Found a Better Way to Teach Large Language Models New Skills
news.ncsu.edu·20h·
Discuss: Hacker News
🏆LLM Benchmarking
A Conversation with Val Bercovici about Disaggregated Prefill / Decode
fabricatedknowledge.com·18h
📊Model Serving Economics
Using a Framework Desktop for local AI
frame.work·19h
🤖AI
AI cloud infrastructure gets faster and greener: NPU core improves inference performance by over 60%
techxplore.com·16h
📊Model Serving Economics
The Era of Exploration
yidingjiang.github.io·22h·
Discuss: Hacker News
🏆LLM Benchmarking
Efficient MultiModal Data Pipeline
huggingface.co·14h
💾Prompt Caching
Being a psychologist to your (over)thinking LLM
specy.app·2h·
Discuss: Hacker News
🏆LLM Benchmarking
[Tool Release] Finetune & Quantize 1–3B LLMs on 8GB RAM using LoFT CLI (TinyLlama + QLoRA + llama.cpp)
reddit.com·6h·
Discuss: r/LocalLLaMA
🔧Developer tools
Study could lead to LLMs that are better at complex reasoning
news.mit.edu·10h
🏆LLM Benchmarking
How I use LLMs to learn new subjects
seangoedecke.com·14h
🏆LLM Benchmarking
Re-implementing LangChain in 100 lines of code (2023)
blog.scottlogic.com·19h·
Discuss: Lobsters
🪄Prompt Engineering
Detailed Study of Performance Modeling For LLM Implementations At Scale (imec)
semiengineering.com·18h
🏆LLM Benchmarking
Towards Unified Neurosymbolic Reasoning on Knowledge Graphs
arxiv.org·10h
🔢BitNet Inference
Radial Attention: O(nlogn) Attention for Long Video Generation with 2-4× Speedup
hanlab.mit.edu·17h·
Discuss: Hacker News
⭐Content Scoring
Token Level Hallucination Detection via Variance in Language Models
arxiv.org·10h
🎯Qdrant
Energy-Based Transformers are Scalable Learners and Thinkers
lesswrong.com·24m
🔢BitNet