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AI 模型 2119

  • bert-base-spanish-wwm-uncased↗

    dccuchile

    BETO is a BERT model trained on a big Spanish corpus. BETO is of size similar to a BERT-Base and was trained with the Whole Word Masking technique. Below you find Tensorflow and Pytorch checkpoints for the uncased and cased versions, as well as some results for Spanish benchmarks comparing BETO with Multilingual BERT as well as other (not BERT-based) models. All models use a vocabulary of about 31k BPE subwords constructed using SentencePiece and were trained for 2M steps. The following table shows some BETO results in the Spanish version of every task. We compare BETO (cased and uncased) with the Best Multilingual BERT results that we found in the literature (as of October 2019). The table also shows some alternative methods for the same tasks (not necessarily BERT-based methods). References for all methods can be found here.

  • sam2-hiera-small↗

    facebook

    Repository for SAM 2: Segment Anything in Images and Videos, a foundation model towards solving promptable visual segmentation in images and videos from FAIR. See the SAM 2 paper for more information. SAM2 can be used for automatic mask generation to segment all objects in an image using the `mask-generation` pipeline: SAM2's key strength is its ability to track objects across video frames. Here's how to use it for video segmentation:

  • sfm_unfiltered_e2e_misalignment_upsampled_base↗

    geodesic-research

  • ThinkingCap-Qwen3.8-27B-abliterated-NVFP4A16↗

    IstroSec

    A 4-bit weight-only quantization (NVFP4 weights, bf16 activations) of ThinkingCap-Qwen3.8-27B-abliterated, the uncensored variant of bottlecapai/ThinkingCap-Qwen3.8-27B. 20.6 GB, down from 55.6 GB in bf16, at KL 0.0155 against the bf16 model. The weights fit a 24 GB GPU with a little room for KV cache; 32 GB and up is comfortable. vLLM serves it on Ampere and newer (sm80+) with a weight-only kernel: Marlin FP4 on most GPUs (Ampere, Ada, Hopper, RTX 50, DGX Spark) and a dedicated BF16×FP4 kernel on B200/B300. Activations stay bf16, so no GPU computes in FP4 here. **Which build?** The W4A16 build (19.5 GB, KL 0.0260) runs on the same GPUs (vLLM: Marlin, Machete on Hopper) and is 1.1 GB smaller; this build has the lower KL. Speed was not measured for either. With memory to spare, the FP8-BLOCK build (31.3 GB, KL 0.0046) has the lowest KL of the published builds; on Ada GPUs use FP8-DYNAMIC (31.3 GB, KL 0.0068) instead.

  • Qwen-Image-Edit-GGUF↗

    QuantStack

    This GGUF file is a direct conversion of Qwen/Qwen-Image-Edit Type | Name | Location | Download | ------------ | -------------------------------------------------- | --------------------------------- | ------------------------- | Main Model | Qwen-Image | `ComfyUI/models/unet` | GGUF (this repo) | Main Text Encoder | Qwen2.5-VL-7B | `ComfyUI/models/text_encoders` | Safetensors / GGUF | | Text_Encoder (mmproj) | Qwen2.5-VL-7B-Instruct-mmproj-BF16 | `ComfyUI/models/text_encoders` (same folder as your main text encoder) | GGUF (this repo) | VAE | Qwen-Image VAE | `ComfyUI/models/vae` | Safetensors (this repo) | Since this is a quantized model, all original licensing terms and usage restrictions remain in effect.

  • IndicBERTv2-MLM-only↗

    ai4bharat

    A multilingual language model trained on IndicCorp v2 and evaluated on IndicXTREME benchmark. The model has 278M parameters and is available in 23 Indic languages and English. The models are trained with various objectives and datasets. The list of models are as follows: - IndicBERT-MLM [Model] - A vanilla BERT style model trained on IndicCorp v2 with the MLM objective - +Samanantar [Model] - TLM as an additional objective with Samanantar Parallel Corpus [Paper] | [Dataset] - +Back-Translation [Model] - TLM as an additional objective by translating the Indic parts of IndicCorp v2 dataset into English w/ IndicTrans model [Model] - IndicBERT-SS [Model] - To encourage better lexical sharing among languages we convert the scripts from Indic languages to Devanagari and train a BERT style model with the MLM objective Fine-tuning scripts are based on transformers library. Create a new conda environment and set it up as follows:

  • DeepSeek-V4-Pro-Qwen3.5-4B-MTP-GGUF↗

    Jackrong

    🔬 DeepSeek-V4-Pro Distillation 🧠 4B Parameters 📐 ~250K Math & STEM Samples ⚡ MTP-Q8 Evaluated 💡 What is DeepSeek-V4-Pro-Qwen3.5-4B? DeepSeek-V4-Pro-Qwen3.5-4B is a reasoning-focused fine-tune of Qwen3.5-4B , distilled from responses generated by DeepSeek-V4-Pro in Max Effect mode . It follows the same training pipeline and approximately 250,000-sample mathematics and STEM mixture used for the 9B release, while targeting a substantially smaller and more accessible deployment class. 🧩 Structured Reasoning Learns multi-step solution patterns and explicit problem decomposition from a strong teacher.

  • gemma-4-31B-it-qat-AWQ-INT4↗

    cyankiwi

    > [!Note] > This model card is for the new versions of the Gemma 4 family optimized with Quantization-Aware Training (QAT), which allows preserving similar quality to bfloat16 while dramatically reducing the memory requirements to load the model. > Four versions of the QAT checkpoints are available: > * **Unquantized QAT checkpoints** (Q4_0): Half-precision weights extracted from the QAT pipeline, ideal for custom downstream compilation and research. Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B, and their drafter models. > * **GGUF** (Q4_0): Ready-to-deploy formats for broad ecosystem compatibility. Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B. > * **Mobile-optimized** (wNa8o8): A custom schema engineered explicitly for mobile hardware efficiency. It features targeted 2-bit decoding layers, optimized KV caches, and static activations to maximize VRAM savings. Available for Gemma 4 E2B and E4B. > * **Compressed Tensors** (w4a16): QAT checkpoints serialized in the compressed-tensors format for native, optimized inference with vLLM. Available for Gemma 4 E2B, E4B, 12B, and 31B. > * **Assistant Compatibility**: When using multi-token prediction (speculative decoding) with an assistant model alongside a QAT target model, the assistant model must also be a QAT checkpoint with the same precision to ensure compatibility. Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on E2B, E4B, and 12B) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages. Featuring both Dense and Mixture-of-Experts (MoE) architectures, Gemma 4 is well-suited for tasks like text generation, coding, and reasoning. The models are available in five distinct sizes: **E2B**, **E4B**, **12B**, **26B A4B**, and **31B**. Their diverse sizes make them deployable in environments ranging from high-end phones to laptops and servers, democratizing access to state-of-the-art AI.

  • Florence-2-large-ft↗

    florence-community

    > [!NOTE] > This is the repository for official transformers converted checkpoint of Microsoft's Florence model. **This is a continued pretrained version of Florence-2-large model with 4k context length, only 0.1B samples are used for continue pretraining, thus it might not be trained well. In addition, OCR task has been updated with line separator ('\n'). COCO OD AP 39.8** Florence-2 is an advanced vision foundation model that uses a prompt-based approach to handle a wide range of vision and vision-language tasks. Florence-2 can interpret simple text prompts to perform tasks like captioning, object detection, and segmentation. It leverages our FLD-5B dataset, containing 5.4 billion annotations across 126 million images, to master multi-task learning. The model's sequence-to-sequence architecture enables it to excel in both zero-shot and fine-tuned settings, proving to be a competitive vision foundation model.

  • tcd-segformer-mit-b0↗

    restor

    This is a semantic segmentation model that can delineate tree cover in high resolution (10 cm/px) aerial images. This model card is mostly the same for all similar models uploaded to Hugging Face. The model name refers to the specific architecture variant (e.g. nvidia-mit-b0 to nvidia-mit-b5) but the broad details for training and evaluation are identical. This semantic segmentation model was trained on global aerial imagery and is able to accurately delineate tree cover in similar images. The model does not detect individual trees, but provides a per-pixel classification of tree/no-tree.