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Building on HF

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victor

huggingface

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victormustar

AI & ML interests

Building the UX of this website

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reacted to Benedictat's post with 🔥 20 minutes ago

Tencent HunyuanImage 3.0-Instruct is seriously impressive skyrocketed to 2nd place globally on the LMArena leaderboard, only trailing Google Nano-banana Pro. What excites me most is its newly launched image editing and multi-image fusion capabilities its semantic understanding is rock-solid this Instruct-following capability basically enables one-sentence end-to-end workflows, delivering a dimensionality-reducing boost in efficiency. Frankly, it nails the pain points of frontline creators: old photo restoration, text modification, even extracting people from multiple images to create group shots. Previously, tweaking the fusion quality took tons of effort, but now the out-of-the-box realism and emotional expression are top-tier zero cheap AI artifacts 👉 Repo: https://hunyuan.tencent.com/chat/HunyuanDefault?from=modelSquare&modelId=Hunyuan-Image-3.0-Instruct technical report：https://arxiv.org/abs/2509.23951

reacted to consome2's post with ❤️ 20 minutes ago

We’ve released two conversational speech datasets from oto on Hugging Face 🤗 Both are based on real, casual, full-duplex conversations, but with slightly different focuses. Dataset 1: Processed / curated subset https://huggingface.co/datasets/otoearth/otoSpeech-full-duplex-processed-141h * Full-duplex, spontaneous multi-speaker conversations * Participants filtered for high audio quality * PII removal and audio enhancement applied * Designed for training and benchmarking S2S or dialogue models Dataset 2: Larger raw(er) release https://huggingface.co/datasets/otoearth/otoSpeech-full-duplex-280h * Same collection pipeline, with broader coverage * More diversity in speakers, accents, and conversation styles * Useful for analysis, filtering, or custom preprocessing experiments We intentionally split the release to support different research workflows: clean and ready-to-use vs. more exploratory and research-oriented use. The datasets are currently private, but we’re happy to approve access requests — feel free to request access if you’re interested. If you’re working on speech-to-speech (S2S) models or are curious about full-duplex conversational data, we’d love to discuss and exchange ideas together. Feedback and ideas are very welcome!

reacted to raincandy-u's post with 🔥 20 minutes ago

🤗 Just released Rain-100M, an experimental ~97M-parameter Qwen3-style language model trained from random initialization. Repo: https://huggingface.co/raincandy-u/Rain-100M Data: https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu, ~3B tokens, English only Tokenizer: custom 16k BPE, context length 4096 Architecture: 12 Transformer layers, hidden size 768, 12 heads, MLP 2048, SiLU, bf16 Rain-100M is a raw base model (not instruction-tuned or safety-aligned), aimed at small-scale research, debugging training pipelines, and CPU/edge experiments. If you run evaluations, finetunes, or visualizations with it, I would be very interested in your results!

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