Define a T-5 small RAG & Finetune it The code below will perform finetuning & provide a checkpoint for the best model weights saved during the process. Check this out
MACHINE LEARNING
-
Micromanagement vs Engineering Detail in AI Leadership
By
–
I loved this podcast with @elonmusk
. He’s just about right. I especially liked his comment about micromanagement. I too have been told this, but I feel micromanagement is often confused with paying attention to engineering detail. In AI and tech, VPs should be able to sit with -
Streamline Tech Operations with MLOps DataOps and AIOps
By
–
Streamline your tech operations with MLOps, DataOps, and AIOps! Follow @ingliguori for more tech insights. Elevate your strategies with 'The Digital Edge' : https://
kenovy.com/thedigitaledge/ #AI #DigitalTransformation #TechInnovation https://
x.com/ingliguori/sta
tus/1793360436865253576/photo/1
… -
LLM’s Primary Limitation: Unable to Distinguish Facts
By
–
The #1 limitation of an LLM is that it doesn't know what a fact is.
-
RAG Retrieval Optimization Through Representation Engineering and Adapters
By
–
took a while to groq but great clear explanation here! an analogy could be that this is @voooooogel style rep eng applied to rag retrieval queries instead of inference outputs? adapters for everything. you could maybe even do adapters for multimodal data! as chameleon style
-
App Startup Survival: Moving Beyond Ideas to Implementation
By
–
Beyond The Idea: The Key To App Startup Survival https://
forbes.com/sites/forbeste
chcouncil/2024/04/19/beyond-the-idea-the-key-to-app-startup-survival/?utm_source=dlvr.it&utm_medium=twitter
… #Innovation #ArtificialIntelligence #MachineLearning -
Attention as Pooling: Superior to Max Pooling in Neural Networks
By
–
Most people don't understand that attention is a pooling operator. A much better (more FLOPs) and less hard-coded and peaky compared to "max" operator in the traditional max pooling, because of soft-attention with learned matmuls and dot products. However, attention and pooling
-
Hugging Face Idefics2: 8B Vision-Language Model Revolution
By
–
Hugging Face Presents Idefics2: An 8B Vision-Language Model Revolution: Hugging Face’s latest offering, Idefics2 heralds a new era in multimodal AI models. With enhanced… https://
analyticsvidhya.com/blog/2024/04/h
ugging-face-presents-idefics-a-vision-language-model-revolution/?utm_source=dlvr.it&utm_medium=twitter
… #DataAnalytics #DataScience #DataDriven #CTO #IoT #AI #ITDirector #BigDataAnalytics -
Implicit Bias Revealed in GPT-4o and Latest LLMs
By
–
Many benchmarks today fail to observe bias in GPT-4o. But are such models truly unbiased? This new paper reveals implicit bias in the latest LLMs such as GPT-4o and LLama Author @baixuechunzi is on alphaXiv answering questions about her latest work: https://
alphaxiv.org/abs/2402.04105
v2
… -
Keras 3 Simplifies Model Parallelism Configuration
By
–
Every other model parallelism API I've seen has partitioning annotations in-lined in the model code and uses custom training logic — making it challenging to modify and maintain the partitioning config. Keras 3 dramatically simplifies this.