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AI 模型 449

  • Anima-Quantized↗

    silveroxides

    Anima is a 2 billion parameter text-to-image model created via a collaboration between CircleStone Labs and Comfy Org. It is focused mainly on anime concepts, characters, and styles, but is also capable of generating a wide variety of other non-photorealistic content. The model is designed for making illustrations and artistic images, and will not work well at realism. It is trained on several million anime images and about 800k non-anime artistic images. No synthetic data was used for training. The knowledge cut-off date for the anime training data is September 2025. This preview version is an intermediate model checkpoint. The model is still training and the final version will improve, especially for fine details and overall aesthetics.

  • Huihui-Laguna-S-2.1-abliterated-GGUF↗

    huihui-ai

    This is an uncensored version of poolside/Laguna-S-2.1 created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. **Note** This is an English language model, and support for other languages is not very good. - **Risk of Sensitive or Controversial Outputs**: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs.

  • gpt-neo-125m↗

    EleutherAI

    GPT-Neo 125M is a transformer model designed using EleutherAI's replication of the GPT-3 architecture. GPT-Neo refers to the class of models, while 125M represents the number of parameters of this particular pre-trained model. GPT-Neo 125M was trained on the Pile, a large scale curated dataset created by EleutherAI for the purpose of training this model. This model was trained on the Pile for 300 billion tokens over 572,300 steps. It was trained as a masked autoregressive language model, using cross-entropy loss.

  • wav2vec2-deepfake-voice-detector↗

    garystafford

    Fine-tuned Wav2Vec2 model for detecting AI-generated speech. Determines if audio was spoken by a human or created by AI text-to-speech/voice cloning software. Fine-tuned Wav2Vec2 transformer for binary audio classification (real vs AI-generated speech). Trained to distinguish authentic human speech from synthetic audio generated by AI text-to-speech and voice cloning services including: **Note:** This model uses transfer learning from a base model already trained for deepfake detection. Fast convergence is expected due to task similarity and TTS engine overlap with the base model's training data.

  • Smooth_Booster_v3↗

    LyliaEngine

    Skin Details: created and merged Loras that add a little more details to skin. Should be more noticeable on realistic Checkpoints. Armor and Weapons Details:: created and merged Loras that adds detailed engraving and patterns. Better material quality. A detail tweaker/booster inspired by the work of many awesome creators like Shed_The_Skin's Detail Slider LoRA, w4r10ck's Detail Tweaker XL, and many, MANY, others.

  • Psyonic-Cetacean-Ultra-Quality-20b-GGUF-imat-plus2↗

    DavidAU

    This is a Floating Point 32 upscale, where all components and merges were remastered to floating point 32. This includes all the merges (recreated with master files), and where possible subbing full FP32 models. This repo contains the new Imatrix Plus 2 quants using a new inhouse dataset merged with a master dataset to push performance of the Ultra Quality remaster even higher. At Q2K an impressive drop of 533 points in perplexity. (lower is better) (VS: Q2K original base model: PPL = 9.8077 +/- 0.06821 )

  • MegaBeam-Mistral-7B-300k↗

    aws-prototyping

    MegaBeam-Mistral-7B-300k is a fine-tuned Mistral-7B-Instruct-v0.2 language model that supports input contexts up to 320k tokens. MegaBeam-Mistral-7B-300k can be deployed on a single AWS `g5.48xlarge` instance using serving frameworks such as vLLM, Sagemaker DJL endpoint, and others. Similarities and differences beween MegaBeam-Mistral-7B-300k and Mistral-7B-Instruct-v0.2 are summarized below: _InfiniteBench is a cutting-edge benchmark tailored for evaluating the capabilities of language models to process, understand, and reason over super long contexts (100k+ tokens)_. We therefore evaluated MegaBeam-Mistral-7B-300k, Mistral-7B-Instruct-v0.2, Llama-3-8B-Instruct-262k, and Llama3-70B-1M on InfiniteBench. The InfiniteBench authors also evaluated SOTA proprietary and open-source LLMs on InfiniteBench. We thus combined both results in the table below. The 12 evaluation tasks are summarized below (as per InfiniteBench)) | Task Name | Context | # Examples | Avg Input Tokens | Avg Output Tokens | Description | | -------------------- | ------------- | ---------- | ---------------- | ----------------- | ------------------------------------------------------------------------------------------- | | En.Sum | Fake Book | 103 | 171.5k | 1.1k | Summarization of a fake book created with core entity substitution. | | En.QA | Fake Book | 351 | 192.6k | 4.8 | Free-form question answering based on the fake book. | | En.MC | Fake Book | 229 | 184.4k | 5.3 | Multiple choice questions derived from the fake book. | | En.Dia | Script | 200 | 103.6k | 3.4 | Identification of talkers in partially anonymized scripts. | | Zh.QA | New Book | 175 | 2068.6k | 6.3 | Question answering on a set of newly collected books. | | Code.Debug | Code Document | 394 | 114.7k | 4.8 | Finding which function in a code repo contains an crashing error (in multiple choice form). | | Code.Run | Synthetic | 400 | 75.2k | 1.3 | Simulating execution of multiple simple, synthetic functions. | | Math.Calc | Synthetic | 50 | 43.9k | 43.9k | Calculations involving super-long arithmetic equations. | | Math.Find | Synthetic | 350 | 87.9k | 1.3 | Finding special integers in a lengthy list. | | Retrieve.PassKey | Syntheti

  • Huihui-Qwen3.8-27B-abliterated-GGUF↗

    LxBarn

    This is an uncensored version of Qwen/Qwen3.8-27B created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. The newly added **Huihui-Qwen3.8-27B-abliterated-Underdog-Saluki** series come from ConwayResearch/Underdog-Saluki-27B-1.0 and ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF. Only layers **22 to 52 (0-based indexing)** have been ablated, while the other layers remain unablated. It may come with a small disclaimer warning. Due to ConwayResearch/Underdog-Saluki-27B-1.0 not providing an imatrix file, we will attempt to perform ablation by mixing the GGUF weights of Swift-1.5 and ConwayResearch/Underdog-Saluki-27B-1.0.

  • Nano Banana 2 Lite↗

    Google

    Nano Banana 2 Lite API（APIMart）：Gemini 3.1 Flash Lite Image，低延迟 1K 生图与编辑，最多 14 张参考图。开通会员，API 调用立省20%起。

  • gpt2-medium↗

    openai-community

    **Model Description:** GPT-2 Medium is the **355M parameter** version of GPT-2, a transformer-based language model created and released by OpenAI. The model is a pretrained model on English language using a causal language modeling (CLM) objective. - **Developed by:** OpenAI, see associated research paper and GitHub repo for model developers. - **Model Type:** Transformer-based language model - **Language(s):** English - **License:** Modified MIT License - **Related Models:** GPT2, GPT2-Large and GPT2-XL - **Resources for more information:** - Research Paper - OpenAI Blog Post - GitHub Repo - OpenAI Model Card for GPT-2 - Test the full generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large Use the code below to get started with the model. You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we set a seed for reproducibility: