The N Implementation Details of RLHF with PPO

RLHF / ChatGPT has been a popular research topic these days. In our quest to research more on RLHF, this blog post attempts to do a reproduction of OpenAI’s 2019 original RLHF codebase at openai/lm-human-preferences. Despite its “tensorflow-1.x-ness,” OpenAI’s original codebase is very well-evaluated and benchmarked, making it a good place to study RLHF implementation engineering details. We aim to: reproduce OAI’s results in stylistic tasks and match the learning curves of openai/lm-human-preferences. present a checklist of implementation details, similar […]

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Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora

In the fast-moving world of Natural Language Processing (NLP), we often find ourselves comparing different language models to see which one works best for specific tasks. This blog post is all about comparing three models: RoBERTa, Mistral-7b, and Llama-2-7b. We used them to tackle a common problem – classifying tweets about disasters. It is important to note that Mistral and Llama 2    

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SDXL in 4 steps with Latent Consistency LoRAs

Latent Consistency Models (LCM) are a way to decrease the number of steps required to generate an image with Stable Diffusion (or SDXL) by distilling the original model into another version that requires fewer steps (4 to 8 instead of the original 25 to 50). Distillation is a type of training procedure that attempts to replicate the outputs from a source model using a new one. The distilled model may be designed to be smaller (that’s the case of DistilBERT […]

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