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AI 모델 85003
- alias3-micro-1026↗
aliasrobotics
- mgp-str-base↗
alibaba-damo
MGP-STR base-sized model is trained on MJSynth and SynthText. It was introduced in the paper Multi-Granularity Prediction for Scene Text Recognition and first released in this repository. MGP-STR is pure vision STR model, consisting of ViT and specially designed A^3 modules. The ViT module was initialized from the weights of DeiT-base, except the patch embedding model, due to the inconsistent input size. Images (32x128) are presented to the model as a sequence of fixed-size patches (resolution 4x4), which are linearly embedded. One also adds absolute position embeddings before feeding the sequence to the layers of the ViT module. Next, A^3 module selects a meaningful combination from the tokens of ViT output and integrates them into one output token corresponding to a specific character. Moreover, subword classification heads based on BPE A^3 module and WordPiece A^3 module are devised for subword predictions, so that the language information can be implicitly modeled. Finally, these multi-granularity predictions (character, subword and even word) are merged via a simple and effective fusion strategy.
- wav2vec2-BERT-cantonese↗
alvanlii
This model is a fine-tuned version of facebook/w2v-bert-2.0. This has a CER of 10.27 on Common Voice 16 (yue) test set (without punctuations). For training, three datasets were used: - Common Voice 16 `zh-HK` and `yue` Train Set - CantoMap: Winterstein, Grégoire, Tang, Carmen and Lai, Regine (2020) "CantoMap: a Hong Kong Cantonese MapTask Corpus", in Proceedings of The 12th Language Resources and Evaluation Conference, Marseille: European Language Resources Association, p. 2899-2906. - Cantonse-ASR: Yu, Tiezheng, Frieske, Rita, Xu, Peng, Cahyawijaya, Samuel, Yiu, Cheuk Tung, Lovenia, Holy, Dai, Wenliang, Barezi, Elham, Chen, Qifeng, Ma, Xiaojuan, Shi, Bertram, Fung, Pascale (2022) "Automatic Speech Recognition Datasets in Cantonese: A Survey and New Dataset", 2022. Link: https://arxiv.org/pdf/2201.02419.pdf
- GLM-5.3-DFlash2-GGUF↗
Anbeeld
- Claude Opus 5 (Xhigh)↗
Anthropic
- MioTTS-2.6B↗
Aratako
**MioTTS-2.6B** is a lightweight, high-speed Text-to-Speech (TTS) model based on an LLM architecture. It is designed to generate high-quality speech in **English and Japanese** while maintaining low latency and minimal resource usage. This model supports zero-shot voice cloning and is built on top of the efficient neural audio codec **MioCodec-25Hz-24kHz**. We offer a range of model sizes to suit different performance and resource requirements.
- nitec-resnet-18-int8-xnnpack-executorch↗
Arm
- bayon↗
attentionlab
- Darwin-180B-RSI-Mixed-Quant-GGUF↗
Baekpica
- Agnes-AI_Agnes-3.0-Qwen-GGUF↗
bartowski
**Model details:** - Parameter count: 180B (source checkpoint) - Input support: text, image (with mmproj file) - details - Speculative decoding: yes (MTP) - details - imatrix: yes - details **Don't know which to choose?** Grab Q4_K_M (127.09GB) - usually a good mix of size and performance. Download instructions available here **About these sizes:** 51.7 % of this model's weights (`per_layer_token_embd` (160 wide), `ffn_down_exps` (640 wide), `hc_attn_up` (320 wide), `hc_ffn_up` (320 wide) and 2 more) have row widths that llama.cpp's 256-wide K and IQ block types cannot use, so each size gives them one of the 32-wide types instead (Q4_0 to Q8_0, or IQ4_NL), whatever type its name asks for, and the files above are closer together in size than usual. These sizes were not made, each within 1 % of the size of another file of the same kind: Q4_K_L (0.9 % from Q5_K_S), IQ2_XXS (0.7 % from IQ2_XS).