🤖 LLMs - antidmg · Scour

Paper page - Large Language Models Explore by Latent Distilling ⚙️MLOps

huggingface.co·51m

Fine-tuning vs. In-context Learning in Large Language Models: A Formal Language Learning Perspective ⚙️MLOps

LLMs Explained: How Large Language Models Work (and Where They Commonly Fail) ⚙️MLOps

LLMs are the worlds most powerful autocomplete ⚙️Compilers

alfredvc.no·14h·Hacker News

Temporal Language Models ⚙️MLOps

calcifercomputing.com·2d·Hacker News

Why Model Collapse in LLMs is Inevitable With Self-Learning ⚙️MLOps

hackaday.com·13h·Hacker News

AmSach/kvquant: Drop-in KV cache compressor for local LLM inference - Run 70B models on 8GB RAM ⚙️MLOps

github.com·3h·DEV

Text Summarization with Scikit-LLM ⚙️MLOps

machinelearningmastery.com·3d

Adaptive Thinking: Large Language Models Know When to Think in Latent Space ⚙️MLOps

machinelearning.apple.com·1d

How to reliably connect LLMs to real-world data and systems ⚙️MLOps

·5h

Yet another experiment proves it's too damn simple to poison large language models ⚙️MLOps

theregister.com·22h

AutoSP: Long-Context LLM Training via Compiler-Based Sequence Parallelism ⚙️Compilers

pytorch.org·21h·Hacker News

The Recurrent Transformer: Greater Effective Depth and Efficient Decoding (5 minute read) λFunctional Programming

alphaxiv.org·1d

How to Build Your Own Language-Specific LLM [Full Handbook] ⚙️Compilers

freecodecamp.org·5d

Android Bench - Evaluating LLMs On The Android Platform ⚙️MLOps

i-programmer.info·21h

Vibe Training - Auto Train a Small Language Model for Your Use Case ⚙️MLOps

diamantai.substack.com·2d·Substack, r/LocalLLaMA

Machine Learning Developers: Why Most ML Projects Fail After the Model Stage ⚙️MLOps

artificialintelligence.oodles.io·3h·DEV

Friendlier LLMs tell users what they want to hear ⚙️MLOps

·23h

The Data Layer Tax for Robot Learning ⚙️MLOps

rerun.io·2h·Hacker News

Large Language Models in Communications ⚙️MLOps

databricks.com·1d

Log in to enable infinite scrolling