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Ryan Orban

Ryan Orban

Subject
7 entries

Hugging Face

Bookmarks

  1. Qwen3-TTS-12Hz-1.7B-CustomVoice: open custom voice TTS

    Qwen3-TTS-12Hz-1.7B-CustomVoice is Alibaba's compact open TTS model with custom voice cloning capability — 1.7B parameters, 12Hz token rate, available on Hugging Face. Part of the Qwen3 model family.

  2. How Hugging Face Accelerate Runs Very Large Models

    Hugging Face's technical guide to running very large models using Accelerate — covers device_map, model parallelism across GPUs, CPU offloading, and the mechanics of loading models that don't fit in a single GPU's VRAM. Essential reading for anyone self-hosting large LLMs.

  3. ROOTS Search Tool — BigScience

    A Hugging Face Space for searching ROOTS — the massive multilingual dataset used to train BLOOM, the BigScience open LLM. Lets researchers trace which training documents a model might have learned from.

  4. MagicPrompt-Stable-Diffusion

    MagicPrompt-Stable-Diffusion is a GPT-2-based model fine-tuned to generate effective prompts for Stable Diffusion. It solves the prompt engineering problem for image generation: given a simple idea, it produces elaborate prompt text that reliably produces better images.

  5. ONNX Stable Diffusion Exporter for Hugging Face Diffusers

    The pull request adding ONNX export and an ONNX inference pipeline to Hugging Face Diffusers — enabling Stable Diffusion to run via ONNX Runtime on hardware accelerators beyond CUDA, including DirectML for Windows GPUs and optimized CPU inference.

  6. BLOOM: Open Multilingual Large Language Model

    BLOOM is the first open, multilingual large language model trained transparently by a global coalition of AI researchers — 176B parameters, 46 languages, trained on the Jean Zay supercomputer in France. A direct counterpoint to GPT-3's closed access.

  7. Financial Text Summarization with Hugging Face and Keras

    A tutorial on fine-tuning distilled BART for financial news summarization using Hugging Face Transformers with Keras and Amazon SageMaker — generating headline-length summaries from longer articles. A practical demonstration of seq2seq fine-tuning on domain-specific data.

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