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AI 模型 2119

• t5-11b↗

  google-t5

  1. Model Details 2. Uses 3. Bias, Risks, and Limitations 4. Training Details 5. Evaluation 6. Environmental Impact 7. Citation 8. Model Card Authors 9. How To Get Started With the Model > With T5, we propose reframing all NLP tasks into a unified text-to-text-format where the input and output are always text strings, in contrast to BERT-style models that can only output either a class label or a span of the input. Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task. > Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task, including machine translation, document summarization, question answering, and classification tasks (e.g., sentiment analysis). We can even apply T5 to regression tasks by training it to predict the string representation of a number instead of the number itself.

• ZDTaichu5.0-9B-GGUF↗

  ngquocvinh

  ☕ If this GGUF made your day easier, a coffee would make mine. Send a coffee ☕ I build and test these releases myself. Your coffee helps keep me going. Thank you for supporting this work. ZDTaichu5.0-9B is a multimodal foundation model for visual understanding, spatial reasoning, agentic tool use, and embodied-AI workloads. The upstream model combines a Qwen3.5-9B language backbone with the C-RADIOv4-H vision encoder and accepts text, one or more images, and video. The upstream card describes any-resolution visual input and up to 128K tokens of context; the GGUF metadata preserves the converted model context configuration, while practical context length depends on the runtime and available memory. This release is for llama.cpp. The language model is provided as quantized GGUF files and the visual encoder / projector is provided separately as `mmproj-ZDTaichu5.0-9B-BF16.gguf`. Use the same mmproj file with each language-model quant when passing images or video. The official upstream model card and Transformers/vLLM instructions are available here.

• MiMo-V2.5-Pro-GGUF↗

  unsloth

  MiMo-V2.5-Pro is an open-source Mixture-of-Experts (MoE) language model with 1.02T total parameters and 42B active parameters. It utilizes the hybrid attention architecture and 3-layers Multi-Token Prediction (MTP) introduced in MiMo-V2-Flash, with up to 1M tokens context length. MiMo-V2.5-Pro is our most capable model to date, designed for the most demanding agentic, complex software engineering, and long-horizon tasks. It sustains complex trajectories spanning thousands of tool calls with strong instruction following and coherence over a 1M-token context window. Key features include: GraphWalks is a long-context benchmark from OpenAI that fills the prompt with a directed graph of hex-hash nodes and asks the model to run a breadth-first search (nodes exactly at depth *N*) or list a node's parents. We evaluate across the full 32k–1M input-token span and apply the same evaluation fixes described by Anthropic.

• audiogen-medium↗

  facebook

  AudioGen is an autoregressive transformer LM that synthesizes general audio conditioned on text (Text-to-Audio). Internally, AudioGen operates over discrete representations learnt from the raw waveform, using an EnCodec tokenizer. AudioGen was presented at AudioGen: Textually Guided Audio Generation by *Felix Kreuk, Gabriel Synnaeve, Adam Polyak, Uriel Singer, Alexandre Défossez, Jade Copet, Devi Parikh, Yaniv Taigman, Yossi Adi*. AudioGen 1.5B is a variant of the original AudioGen model that follows MusicGen architecture. More specifically, it is trained over a 16kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz with a delay pattern between the codebooks. Having only 50 auto-regressive steps per second of audio, this AudioGen model allows faster generation while reaching similar performances to the original AudioGen model introduced in the paper.

• s2t-small-mustc-en-fr-st↗

  facebook

  `s2t-small-mustc-en-fr-st` is a Speech to Text Transformer (S2T) model trained for end-to-end Speech Translation (ST). The S2T model was proposed in this paper and released in this repository S2T is a transformer-based seq2seq (encoder-decoder) model designed for end-to-end Automatic Speech Recognition (ASR) and Speech Translation (ST). It uses a convolutional downsampler to reduce the length of speech inputs by 3/4th before they are fed into the encoder. The model is trained with standard autoregressive cross-entropy loss and generates the transcripts/translations autoregressively. This model can be used for end-to-end English speech to French text translation. See the model hub to look for other S2T checkpoints.

• Spark-X2.6-4B-abliterated-FIT-GGUF↗

  SC117

  FIT-GGUF v0.5.0 ALL FIVE TIERS GATE-VERIFIED 5 FIT + BF16 + IMATRIX INPUT-GATED ABLITERATION MEASURED KL + SAME-TOP APACHE-2.0 Spark-X2.6-4B-abliterated · FIT-GGUF Five fidelity tiers of a 4B hybrid-attention model — each one the smallest GGUF that provably meets its KL gate , re-measured on its own shipped bytes. 🧭 About FIT-GGUF — the tool behind these files Every FIT tier here was planned, executed and verified by FIT-GGUF , an open-source, deterministic tensor-level planning layer on top of llama.cpp quantization. Standard GGUF quantization asks you to pick one of a handful of presets; FIT-GGUF instead asks what quality do you want , then finds and verifies the smallest GGUF that demonstrably meets it. Traditional GGUF gives you presets. FIT gives you a fidelity contract: macro KL ≤ tier anchor, measured against this model's own BF16 on five fixed domains. Deterministic size prediction & byte-exact delivery ✅ Validated (G2 gate, delta = 0) Universally optimal tensor allocation ⚠️ Not established — FIT claims verified fidelity contracts, not a universal quality optimum Method, pre-registered research record and the fit CLI are all open source: github.com/Scorp1o117/FIT-GGUF ⚠️ Safety notice / 安全提示 The source model is an abliterated, refusal-removed model with no meaningful built-in guardrails, and may comply with harmful, illegal or unsafe requests. Use it only where you can provide appropriate moderation, access control and legal review. Do not deploy it to end users without your own safety layer. 源模型经过拒答方向消融，不具备可靠的内置安全护栏；请仅在合法、受控、具备审核与滥用防护的环境中使用，使用者自行承担部署责任。

• Ingot-VI-27B-Q2↗

  dendriteholdings

  A public mirror of a model that won on Ingot, a Bittensor subnet where miners quantize a fixed teacher model and the validator crowns the copy that stays closest to it. It is Prism ML's Ternary Bonsai 2 27B, refined: the same ternary codes, with the per-block scales and the norm weights re-tuned by the miner to bring it closer to Qwen3.8-27B. The weights are the miner's upload, byte for byte. Credit belongs to the miner, and to Prism ML for Bonsai 2.

• sd-vae-ft-mse↗

  Stability AI

  These weights are intended to be used with the 🧨 diffusers library. If you are looking for the model to use with the original CompVis Stable Diffusion codebase, come here. You can integrate this fine-tuned VAE decoder to your existing `diffusers` workflows, by including a `vae` argument to the `StableDiffusionPipeline` We publish two kl-f8 autoencoder versions, finetuned from the original kl-f8 autoencoder on a 1:1 ratio of LAION-Aesthetics and LAION-Humans, an unreleased subset containing only SFW images of humans. The intent was to fine-tune on the Stable Diffusion training set (the autoencoder was originally trained on OpenImages) but also enrich the dataset with images of humans to improve the reconstruction of faces. The first, _ft-EMA_, was resumed from the original checkpoint, trained for 313198 steps and uses EMA weights. It uses the same loss configuration as the original checkpoint (L1 + LPIPS). The second, _ft-MSE_, was resumed from _ft-EMA_ and uses EMA weights and was trained for another 280k steps using a different loss, with more emphasis on MSE reconstruction (MSE + 0.1 * LPIPS). It produces somewhat ``smoother'' outputs. The batch size for both versions was 192 (16 A100s, batch size 12 per GPU). To keep compatibility with existing models, only the decoder part was finetuned; the checkpoints can be used as a drop-in replacement for the existing autoencoder.

• coco_instance_eomt_large_1280↗

  tue-mps

  **EoMT (Encoder-only Mask Transformer)** is a Vision Transformer (ViT) architecture designed for high-quality and efficient image segmentation. It was introduced in the CVPR 2025 highlight paper: **Your ViT is Secretly an Image Segmentation Model** by Tommie Kerssies, Niccolò Cavagnero, Alexander Hermans, Narges Norouzi, Giuseppe Averta, Bastian Leibe, Gijs Dubbelman, and Daan de Geus. > **Key Insight**: Given sufficient scale and pretraining, a plain ViT along with additional few params can perform segmentation without the need for task-specific decoders or pixel fusion modules. The same model backbone supports semantic, instance, and panoptic segmentation with different post-processing 🤗

• wmt22-comet-da↗

  Unbabel

  This is a COMET evaluation model: It receives a triplet with (source sentence, translation, reference translation) and returns a score that reflects the quality of the translation compared to both source and reference. Given a a triplet with (source sentence, translation, reference translation) outputs a single score between 0 and 1 where 1 represents a perfect translation. Afrikaans, Albanian, Amharic, Arabic, Armenian, Assamese, Azerbaijani, Basque, Belarusian, Bengali, Bengali Romanized, Bosnian, Breton, Bulgarian, Burmese, Burmese, Catalan, Chinese (Simplified), Chinese (Traditional), Croatian, Czech, Danish, Dutch, English, Esperanto, Estonian, Filipino, Finnish, French, Galician, Georgian, German, Greek, Gujarati, Hausa, Hebrew, Hindi, Hindi Romanized, Hungarian, Icelandic, Indonesian, Irish, Italian, Japanese, Javanese, Kannada, Kazakh, Khmer, Korean, Kurdish (Kurmanji), Kyrgyz, Lao, Latin, Latvian, Lithuanian, Macedonian, Malagasy, Malay, Malayalam, Marathi, Mongolian, Nepali, Norwegian, Oriya, Oromo, Pashto, Persian, Polish, Portuguese, Punjabi, Romanian, Russian, Sanskri, Scottish, Gaelic, Serbian, Sindhi, Sinhala, Slovak, Slovenian, Somali, Spanish, Sundanese, Swahili, Swedish, Tamil, Tamil Romanized, Telugu, Telugu Romanized, Thai, Turkish, Ukrainian, Urdu, Urdu Romanized, Uyghur, Uzbek, Vietnamese, Welsh, Western, Frisian, Xhosa, Yiddish.