AI Briefing#fine-tuning
The latest AI and developer news about #fine-tuning, with the original source and a short summary.
The latest AI and developer news about #fine-tuning, with the original source and a short summary.
This introduces a process for systematically evaluating and comparing the performance of fine-tuned open source models using LangSmith.
It covers how to fine-tune the Llama 2 70B model using PyTorch FSDP while solving memory and checkpoint saving issues.
This explains how to efficiently fine-tune Llama 2 through the DPO method, which addresses the complexity of RLHF.
A guide summarizing Hugging Face's open source LLM models, licenses, serving tools, and PEFT techniques.
This introduces a method for fine-tuning Stable Diffusion models by leveraging the AMX accelerator in Intel Sapphire Rapids CPUs.
A Kakao DKOS developer revealed the working principles of an award-winning AI chatbot from an internal contest and outlined future platform development plans.
Thanks to 4-bit quantization and QLoRA technology, large language models can now be efficiently fine-tuned even with limited memory.
Introduces how to fine-tune StarCoder with ChatML and DeepSpeed to build the conversational coding assistant StarChat.
Hugging Face released the StackLLaMA model and a guide for training LLaMA using RLHF.
Ubisoft used AI21 Labs to reduce repetitive game scenario work and data augmentation costs.
Tweet Hunter grew its tweet generation tool using AI21's fine-tuning.
The 🤗 PEFT library, which enables fine-tuning of large models with fewer resources, has been released.
It introduces a method of applying LoRA technology to Stable Diffusion to speed up training and dramatically reduce model size.
HuggingFace has released optimal settings for preventing overfitting and improving quality when training Dreambooth on Stable Diffusion using Diffusers.
This guide explains how to fine-tune the Whisper model on multilingual automatic speech recognition (ASR) datasets using Hugging Face Transformers.
rinna has released 'Japanese Stable Diffusion,' which reflects Japanese prompts and Japanese culture.
This explains a fine-tuning method that uses Hugging Face's XLS-R model to improve speech recognition (ASR) performance for low-resource languages.
Upstage's AskUp is deployed on the Sebasi website to handle inquiries about educational content.