Category: huggingface
Stable Diffusion XL on Mac with Advanced Core ML Quantization
Stable Diffusion XL was released yesterday and it’s awesome. It can generate large (1024×1024) high quality images; adherence to prompts has been improved with some new tricks; it can effortlessly produce very dark or very bright images thanks to
Read moreOpen-sourcing Knowledge Distillation Code and Weights of SD-Small and SD-Tiny
In recent times, the AI community has witnessed a remarkable surge in the development of larger and more performant language models, such as Falcon 40B, LLaMa-2 70B, Falcon 40B, MPT 30B, and in the
Read morePractical 3D Asset Generation: A Step-by-Step Guide
Generative AI has become an instrumental part of artistic workflows for game development. However, as detailed in my earlier post, text-to-3D lags behind 2D in terms of practical applicability. This is beginning to change. Today, we’ll be revisiting practical workflows for 3D Asset Generation and taking a step-by-step look at how to integrate Generative AI in a PS1-style 3D workflow.
Read moreTowards Encrypted Large Language Models with FHE
Large Language Models (LLM) have recently been proven as reliable tools for improving productivity in many areas such as programming, content creation, text analysis, web search, and distance learning. The Impact of Large Language Models on Users’ Privacy Despite
Read moreDeploy MusicGen in no time with Inference Endpoints
MusicGen is a powerful music generation model that takes in text prompt and an optional melody to output music. This blog post will guide you through generating music with MusicGen using Inference Endpoints. Inference Endpoints allow us to write
Read moreReleasing Swift Transformers: Run On-Device LLMs in Apple Devices
I have a lot of respect for iOS/Mac developers. I started writing apps for iPhones in 2007, when not even APIs or documentation existed. The new devices adopted some unfamiliar decisions in the constraint space, with a combination of power, screen real estate, UI idioms, network access, persistence, and latency that was different to what we were used to before. Yet, this
Read moreFine-tune Llama 2 with DPO
Reinforcement Learning from Human Feedback (RLHF) has become the de facto last training step of LLMs such as GPT-4 or Claude to ensure that the language model’s outputs are aligned with human expectations such as chattiness or safety features. However, it brings some of the complexity of RL into NLP: we need to build a good reward function, train the model to estimate the value of a state, and at the same time be careful not to strive too far […]
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