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AI 模型 84760
- voz↗
desert-ant-labs
- Qwen.Qwen3-235B-A22B-Instruct-2507-GGUF↗
DevQuasar
- Loyal-Macaroni-Maid-7B-imatrix-GGUF↗
duyntnet
- LFM2.5-24B-A2B-uncensored-GGUF↗
electroglyph
- Delveta-Qwen3-0.6B-PE-v1↗
eric-ml-nlp
- deit-base-patch16-224↗
facebook
Data-efficient Image Transformer (DeiT) model pre-trained and fine-tuned on ImageNet-1k (1 million images, 1,000 classes) at resolution 224x224. It was first introduced in the paper Training data-efficient image transformers & distillation through attention by Touvron et al. and first released in this repository. However, the weights were converted from the timm repository by Ross Wightman. Disclaimer: The team releasing DeiT did not write a model card for this model so this model card has been written by the Hugging Face team. The Vision Transformer (ViT) is a transformer encoder model (BERT-like) pre-trained and fine-tuned on a large collection of images in a supervised fashion, namely ImageNet-1k, at a resolution of 224x224 pixels.
- mms-tts-urd-script_arabic↗
facebook
This repository contains the **Urdu (urd-script_arabic)** language text-to-speech (TTS) model checkpoint. 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.
- wav2vec2-xls-r-2b↗
facebook
- Qwen-Qwen2.5-Coder-32B-Instruct-GGUF↗
featherless-ai-quants
- free-evo-qwen72b-v0.8-re↗
freewheelin
You need two models with the same architecture. - Choose one model and fine-tune it to create a gap between the original model and the fine-tuned one. It doesn't matter whether the evaluation score is higher or lower. - Merge the two models. - Evaluate the merged model. - Fine-tune a specific evaluation part of the model if you need to increase the score for that part. (It's unlikely to work as you think, but you can try it.) - Merge the models again. - Evaluate again. - Keep going until the average evaluation score is higher than the original one.