Self-Taught Evaluators https://
huggingface.co/papers/2408.02
666
… Model-based evaluation is at the heart of successful model development — as a reward model for training, and as a replacement for human evaluation. To train such evaluators, the standard approach is to collect a large amount of
LLMS
-
Self-Taught Evaluators: Model-Based Evaluation Without Large Human Datasets
By
–
-
The Challenge of LLM Consistency and Truthfulness
By
–
The Problem with Lying is Keeping Track of All the Lies https://
bit.ly/3LhJkBa
#AI #MachineLearning #DeepLearning #LLMs #DataScience -
Claude Artefacts UI update changes default pop-up behavior
By
–
Claude Artefacts are now being opened in a half screen pop up by default, instead of a full screen. They still can be expanded to full afterwards. This feature is still in development
-
LoRA Adapter Refusal Vector Calculation Technique
By
–
I didn't calculate the refusal vector, this is a LoRA adapter
-
Language Models on Command Line: CLI Integration Guide
By
–
GitHub – simonw/language-models-on-the-command-line: Handout for a talk I gave about LLM and CLI tools https://
bit.ly/3Wgpg8p
#AI #MachineLearning #DeepLearning #LLMs #DataScience -
Flux 1.0 and GEN-3 New Super AI Combo
By
–
Flux 1.0 y GEN-3, el nuevo súper combo! 🔥 pic.twitter.com/bKSrvr4Cur
— Carlos Santana (@DotCSV) 5 août 2024Flux 1.0 y GEN-3, el nuevo súper combo!
-
Llama 3.1 Impact Grants Support Global Community Projects
By
–
We’re inspired by the diverse projects we’ve seen developers undertake around the world to positively impact their communities by building with Llama and we're excited to support a new wave of global community impact with the Llama 3.1 Impact Grants.
-
Llama 3.1 Impact Grants: $500K for Community Solutions
By
–
Today we're opening a call for applications for Llama 3.1 Impact Grants! Until Nov 22, teams can submit proposals for using Llama to address social challenges across their communities for a chance to be awarded a $500K grant. Details + application https://
go.fb.me/smw6xc -
Llama 3.1 Compatibility and Overlap with Previous Version
By
–
Yes, there seems to be a large overlap. Llama 3.1 is based on 3.0, so it's not too crazy but it's nice it works without requiring any tweaking
-
Turbo LoRA: Faster Fine-Tuning with Improved Inference Efficiency
By
–
Get ready for Turbo #LoRA, our new approach to parameter-efficient #finetuning. You get: 2-3x faster text generation throughput vs. base models Task-specific response quality on par with LoRA Improved inference cost efficiency
Read more: