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AI 模型 450

• bert-base-german-cased↗

  google-bert

  **Update April 3rd, 2020**: we updated the vocabulary file on deepset's s3 to conform with the default tokenization of punctuation tokens. For details see the related FARM issue. If you want to use the old vocab we have also uploaded a "deepset/bert-base-german-cased-oldvocab" model. - We trained using Google's Tensorflow code on a single cloud TPU v2 with standard settings. - We trained 810k steps with a batch size of 1024 for sequence length 128 and 30k steps with sequence length 512. Training took about 9 days. - As training data we used the latest German Wikipedia dump (6GB of raw txt files), the OpenLegalData dump (2.4 GB) and news articles (3.6 GB). - We cleaned the data dumps with tailored scripts and segmented sentences with spacy v2.1. To create tensorflow records we used the recommended sentencepiece library for creating the word piece vocabulary and tensorflow scripts to convert the text to data usable by BERT. During training we monitored the loss and evaluated different model checkpoints on the following German datasets:

• janku-v5-nsfw-trained-noobai-rou-wei-illustrious-xl-v50-sdxl↗

  John6666

  Original model is here. This model created by janxd.

• DeepHat-V1-7B↗

  DeepHat

  DeepHat is a model series that can be used for offensive and defensive cybersecurity. Access at Deephat.ai or go to Kindo.ai to create agents. DeepHat is a finetune of Qwen2.5-Coder-7B, and inherits the following features: - Type: Causal Language Models - Training Stage: Pretraining & Post-training - Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias - Number of Parameters: 7.61B - Number of Paramaters (Non-Embedding): 6.53B - Number of Layers: 28 - Number of Attention Heads (GQA): 28 for Q and 4 for KV - Context Length: Full 131,072 tokens - Please refer to this section for detailed instructions on how to deploy Qwen2.5 for handling long texts. Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.

• LTX_2.3_Fantasy_Anime_Style_LoRa↗

  vrgamedevgirl84

  This LoRA creates a **cinematic fantasy anime cel-shaded illustration** look. **The dataset used to create this LoRA did not use any IP.** It is a **grounded high-fantasy anime illustration style**, not actual frames or images from any movies, TV shows, or existing franchises. The workflow used to create the sample videos can be found HERE. All sample videos are **text-to-video**, not image-to-video. Characters and environments appear as stylized fantasy anime illustrations. Scenes feature **hand-drawn linework**, **painted shading**, **expressive faces**, **stylized fantasy costumes**, **magical creatures**, **vivid atmospheric effects**, and **cinematic composition**. The result feels like a scene from a modern high-end fantasy anime film with immersive worlds and story-driven character moments.

• gemma-4-26B-A4B-it-qat-uncensored-heretic-UDmerge-GGUF↗

  EZForever

  **UPDATE 2026-07-20:** Download & use Google's new chat template from here for better speed and accurancy. See Unsloth's post for details. *WARNING: Created with heavy LLM assistance (Zoo Code + DeepSeek-V4-Flash). Use at your own discretion.* Cosplayed Frankenstein and grafted/"merged" [llmfan46's][QAT BF16] abliterated tensors onto [Unsloth's][QAT UD-Q4_K_XL] lossless Q4_0 quant. Should yield better accurancy and refusal rate than a naive abliterated Q4_0 quant.

• wavlm-large↗

  Microsoft

  The large model pretrained on 16kHz sampled speech audio. When using the model, make sure that your speech input is also sampled at 16kHz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out this blog for more in-detail explanation of how to fine-tune the model. - 60,000 hours of Libri-Light - 10,000 hours of GigaSpeech - 24,000 hours of VoxPopuli

• qwen3-8b-heretic↗

  DreamFast

  An abliterated version of Qwen's Qwen3-8B created using Heretic v1.2.0 (git master). This model has reduced refusals while maintaining model quality, making it suitable as an uncensored text encoder for image generation models like Klein 9B. Available in five quantization formats for ComfyUI: FP8, INT8 (ConvRot row-wise), INT4 (W4A4 ConvRot), NVFP4, and MXFP8, all produced with SVD-guided learned rounding for maximum fidelity. You can see the docker, scripts and configurations used to make these files on Heretic Docker Github.

• pony-v7-base↗

  purplesmartai

  Pony V7 is a versatile character generation model based on AuraFlow architecture. It supports a wide range of styles and species types (humanoid, anthro, feral, and more) and handles character interactions through natural language prompts. First, let me introduce Fictional - our multimodal platform where AI Characters come alive through text, images, voice, and (soon) video. Powered by PonyV7, V6, Chroma, Seedream 4, and other advanced models, Fictional lets you discover, create, and interact with characters who live their own lives and share their own stories. Fictional is also what enables the development of models like V7, so if you're excited about the future of multimodal AI characters, please download Fictional on iOS or Android and help shape our future!

• gpt-oss-safeguard-120b-MLX-MXFP4↗

  lmstudio-community

  _👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord_. **Model creator**: openai **Original model**: gpt-oss-safeguard-120b **MLX quantization**: provided by LM Studio team using mlx_lm LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.

• unispeech-large-1500h-cv↗

  Microsoft

  The large model pretrained on 16kHz sampled speech audio and phonetic labels. When using the model make sure that your speech input is also sampled at 16kHz and your text in converted into a sequence of phonemes. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out this blog for more in-detail explanation of how to fine-tune the model. Authors: Chengyi Wang, Yu Wu, Yao Qian, Kenichi Kumatani, Shujie Liu, Furu Wei, Michael Zeng, Xuedong Huang