全站搜索
AI 模型 2119
- small100↗
alirezamsh
SMaLL-100 is a compact and fast massively multilingual machine translation model covering more than 10K language pairs, that achieves competitive results with M2M-100 while being much smaller and faster. It is introduced in this paper(accepted to EMNLP2022), and initially released in this repository. The model architecture and config are the same as M2M-100 implementation, but the tokenizer is modified to adjust language codes. So, you should load the tokenizer locally from tokenization_small100.py file for the moment. Afrikaans (af), Amharic (am), Arabic (ar), Asturian (ast), Azerbaijani (az), Bashkir (ba), Belarusian (be), Bulgarian (bg), Bengali (bn), Breton (br), Bosnian (bs), Catalan; Valencian (ca), Cebuano (ceb), Czech (cs), Welsh (cy), Danish (da), German (de), Greeek (el), English (en), Spanish (es), Estonian (et), Persian (fa), Fulah (ff), Finnish (fi), French (fr), Western Frisian (fy), Irish (ga), Gaelic; Scottish Gaelic (gd), Galician (gl), Gujarati (gu), Hausa (ha), Hebrew (he), Hindi (hi), Croatian (hr), Haitian; Haitian Creole (ht), Hungarian (hu), Armenian (hy), Indonesian (id), Igbo (ig), Iloko (ilo), Icelandic (is), Italian (it), Japanese (ja), Javanese (jv), Georgian (ka), Kazakh (kk), Central Khmer (km), Kannada (kn), Korean (ko), Luxembourgish; Letzeburgesch (lb), Ganda (lg), Lingala (ln), Lao (lo), Lithuanian (lt), Latvian (lv), Malagasy (mg), Macedonian (mk), Malayalam (ml), Mongolian (mn), Marathi (mr), Malay (ms), Burmese (my), Nepali (ne), Dutch; Flemish (nl), Norwegian (no), Northern Sotho (ns), Occitan (post 1500) (oc), Oriya (or), Panjabi; Punjabi (pa), Polish (pl), Pushto; Pashto (ps), Portuguese (pt), Romanian; Moldavian; Moldovan (ro), Russian (ru), Sindhi (sd), Sinhala; Sinhalese (si), Slovak (sk), Slovenian (sl), Somali (so), Albanian (sq), Serbian (sr), Swati (ss), Sundanese (su), Swedish (sv), Swahili (sw), Tamil (ta), Thai (th), Tagalog (tl), Tswana (tn), Turkish (tr), Ukrainian (uk), Urdu (ur), Uzbek (uz), Vietnamese (vi), Wolof (wo), Xhosa (xh), Yiddish (yi), Yoruba (yo), Chinese (zh), Zulu (zu)
- InternVL2_5-4B-MPO↗
OpenGVLab
We introduce InternVL2.5-MPO, an advanced multimodal large language model (MLLM) series that demonstrates superior overall performance. This series builds upon InternVL2.5 and Mixed Preference Optimization. As shown in the following figure, InternVL2.5-MPO retains the same model architecture as InternVL 2.5 and its predecessors, InternVL 1.5 and 2.0, following the "ViT-MLP-LLM" paradigm. In this new version, we integrate a newly incrementally pre-trained InternViT with various pre-trained LLMs, including InternLM 2.5 and Qwen 2.5, using a randomly initialized MLP projector. As in the previous version, we applied a pixel unshuffle operation, reducing the number of visual tokens to one-quarter of the original. Besides, we adopted a similar dynamic resolution strategy as InternVL 1.5, dividing images into tiles of 448×448 pixels. The key difference, starting from InternVL 2.0, is that we additionally introduced support for multi-image and video data.
- devanagari_PP-OCRv5_mobile_rec↗
PaddlePaddle
devanagari_PP-OCRv5_mobile_rec is one of the PP-OCRv5_rec that are the latest generation text line recognition models developed by PaddleOCR team. It aims to efficiently and accurately support the recognition of Devanagari. The key accuracy metrics are as follow: **Note**: If any character (including punctuation) in a line was incorrect, the entire line was marked as wrong. This ensures higher accuracy in practical applications. You can also integrate the model inference of the text recognition module into your project. Before running the following code, please download the sample image to your local machine.
- 808↗
Sadatsami
- Qwen-Image-2.1-viggle-turbo↗
Viggle
**Built with Qwen.** A few-step distilled version of Qwen/Qwen-Image-2.1 by Viggle. Text-to-image and instruction-driven editing with 1–3 reference images in **6 steps instead of 40**, with **no classifier-free guidance**. About **5× faster** than the 40-step base model end to end, and very competitive with it in quality: on the official Qwen examples the two are hard to tell apart on most prompts. The clearest gaps are small, dense text and complicated edits (Known limitations). Compare them yourself in the **Comparison** tab of the demo Space. * **6 steps, a different balance.** Against v0.2.1: less grain and cleaner surfaces, fine texture a little softer; diversity (still close to the base model's) and small-text accuracy about the same. It is not a strict upgrade: if you prefer the crisper look, v0.2.1 is still in the repository. * **New 9-step mode:** 7 turbo steps, then the LoRA is switched off and the base model finishes the last two. Finer detail, and small text comes out right more often (not always). It takes about 1.4–1.5× as long as 6 steps (still about 3.5× faster than the base model). It works in diffusers and the demo Space only (9 steps). * **We think 6 steps is close to its capacity.** Since v0.2.1, every gain we found at 6 steps cost something elsewhere: sharper came with more grain, less grain came with a softer look. Beyond this point, quality most likely has to be paid for with steps, which is what the 9-step mode does.
- indobert-base-p2↗
indobenchmark
IndoBERT was trained and evaluated by Bryan Wilie\*, Karissa Vincentio\*, Genta Indra Winata\*, Samuel Cahyawijaya\*, Xiaohong Li, Zhi Yuan Lim, Sidik Soleman, Rahmad Mahendra, Pascale Fung, Syafri Bahar, Ayu Purwarianti.
- taesdxl↗
madebyollin
TAESDXL is very tiny autoencoder which uses the same "latent API" as SDXL-VAE. TAESDXL is useful for real-time previewing of the SDXL generation process.
- Meta-Llama-3-8B-Instruct-abliterated-v3↗
failspy
This is meta-llama/Meta-Llama-3-8B-Instruct with orthogonalized bfloat16 safetensor weights, generated with a refined methodology based on that which was described in the preview paper/blog post: 'Refusal in LLMs is mediated by a single direction' which I encourage you to read to understand more. TL;DR: This model has had certain weights manipulated to "inhibit" the model's ability to express refusal. It is not in anyway _guaranteed_ that it won't refuse you, understand your request, it may still lecture you about ethics/safety, etc. It is tuned in all other respects the same as the original 70B instruct model was, just with the strongest refusal directions orthogonalized out. **TL;TL;DR;DR: It's uncensored in the purest form I can manage -- no new or changed behaviour in any other respect from the original model.**
- kokoro-inno-clone-tuner↗
remsky
Zero-shot voice tuner for Kokoro-82M. Outputs base Kokoro compatible voice packs @ `[510, 1, 256]`. (write-up) Same passage for every voice, enrolled from the references. LibriTTS-R speakers are `dev-clean` held out from training.
- Qwen3.5-9b-Claude-4.8-Opus-reasoning↗
TheCidSama