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AI 模型 84755
- mms-tts-bul↗
facebook
This model is part of Facebook's Massively Multilingual Speech project, aiming to provide speech technology across a diverse range of languages. You can find more details about the supported languages and their ISO 639-3 codes in the MMS Language Coverage Overview, and see all MMS-TTS checkpoints on the Hugging Face Hub: facebook/mms-tts. VITS (**V**ariational **I**nference with adversarial learning for end-to-end **T**ext-to-**S**peech) is an end-to-end speech synthesis model that predicts a speech waveform conditional on an input text sequence. It is a conditional variational autoencoder (VAE) comprised of a posterior encoder, decoder, and conditional prior. A set of spectrogram-based acoustic features are predicted by the flow-based module, which is formed of a Transformer-based text encoder and multiple coupling layers. The spectrogram is decoded using a stack of transposed convolutional layers, much in the same style as the HiFi-GAN vocoder. Motivated by the one-to-many nature of the TTS problem, where the same text input can be spoken in multiple ways, the model also includes a stochastic duration predictor, which allows the model to synthesise speech with different rhythms from the same input text.
- mms-tts-xer↗
facebook
- sapiens-depth-1b↗
facebook
- rus-cyrl-100mb-after-ppt-Dp-100mb-packed-bfdiso-ckpt500_seed455↗
francesca9805
- tur-100mb-after-wc-loglinear-newlex-before-ckpt500-packed-bfdiso_seed3407↗
francesca9805
- z-image-cyberrealistic-v9-gguf↗
gimly13
The key difference between Z-Image CyberRealistic v9 and v8 is not merely that it is "another version," but rather a shift in prompting philosophy. V9 moves away from tag stacks toward descriptive language: it better understands natural phrasing about lighting, scene and materials, relies less on booster words like "masterpiece" or "8k", and delivers more natural-looking skin, consistent anatomy and realistic lighting. Below are six prompts that demonstrate these differences in practice. All examples were generated with the same seed and sampler settings on both v8 and v9 for a fair comparison. 💡 Tip: These PNGs have the **ComfyUI workflow and prompt embedded!** Don't just copy the image from the browser preview. Go to the Files and versions tab, open the image there, and click "Download" to get the original file with all metadata intact.
- bert_uncased_L-6_H-768_A-12↗
Google
This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking). We have shown that the standard BERT recipe (including model architecture and training objective) is effective on a wide range of model sizes, beyond BERT-Base and BERT-Large. The smaller BERT models are intended for environments with restricted computational resources. They can be fine-tuned in the same manner as the original BERT models. However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher. Our goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity.
- finetuned-bertweet-poskd3↗
GozdeA
- opus-mt-th-fr↗
Helsinki-NLP
- chinese-alpaca-2-13b-16k↗
hfl
**This is the full Chinese-LLaMA-2-13B-16K (context size 16K)，model，which can be loaded directly for inference and full-parameter training.** **Related models👇** * Long context base models (16K) * Chinese-LLaMA-2-7B-16K (full model) * Chinese-LLaMA-2-LoRA-7B-16K (LoRA model) * Chinese-LLaMA-2-13B-16K (full model) * Chinese-LLaMA-2-LoRA-13B-16K (LoRA model) * Long context Instruction/Chat models * Chinese-Alpaca-2-7B-16K (full model) * Chinese-Alpaca-2-LoRA-7B-16K (LoRA model) * Chinese-Alpaca-2-13B-16K (full model) * Chinese-Alpaca-2-LoRA-13B-16K (LoRA model) * Base models * Chinese-LLaMA-2-7B (full model) * Chinese-LLaMA-2-LoRA-7B (LoRA model) * Chinese-LLaMA-2-13B (full model) * Chinese-LLaMA-2-LoRA-13B (LoRA model) * Instruction/Chat models * Chinese-Alpaca-2-7B (full model) * Chinese-Alpaca-2-LoRA-7B (LoRA model) * Chinese-Alpaca-2-13B (full model) * Chinese-Alpaca-2-LoRA-13B (LoRA model) This project is based on the Llama-2, released by Meta, and it is the second generation of the Chinese LLaMA & Alpaca LLM project. We open-source Chinese LLaMA-2 (foundation model) and Alpaca-2 (instruction-following model). These models have been expanded and optimized with Chinese vocabulary beyond the original Llama-2. We used large-scale Chinese data for incremental pre-training, which further improved the fundamental semantic understanding of the Chinese language, resulting in a significant performance improvement compared to the first-generation models. The relevant models support a 4K context and can be expanded up to 18K+ using the NTK method.