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AI Models 84999

• bert-base-cased-finetuned-qnli↗

  gchhablani

• DeepSeek-V4-Flash-0731-GGUF↗

  ggml-org

• Huihui-DeepSeek-V4.1-Flash-1017-abliterated-GGUF↗

  huihui-ai

  This time the Maya weights are not provided directly; you need to run the command yourself to apply the patch. For details, Please refer to README-Maya.md in the Maya directory. This time the Strata weights are not provided directly; you need to run the command yourself to apply the patch. For details, Please refer to README-Strata.md in the Strata-deepseek-v4.1-flash-IST-DAS directory. This portion of the weights actually comes from TA-DASLab/DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF. If you want to run it under b11870 or newer, please refer to the llamacpp instructions for the necessary modifications.

• Nyx-RP-9B-Instruct-2608-v1↗

  Indexnusrefather

• InstantID↗

  InstantX

  InstantID is a new state-of-the-art tuning-free method to achieve ID-Preserving generation with only single image, supporting various downstream tasks. You can directly download the model in this repository. You also can download the model in python script: For face encoder, you need to manutally download via this URL to `models/antelopev2`.

• gemma-3-27b-it-GPTQ-4b-128g↗

  ISTA-DASLab

  This model was obtained by quantizing the weights of gemma-3-27b-it to INT4 data type. This optimization reduces the number of bits per parameter from 16 to 4, reducing the disk size and GPU memory requirements by approximately 75%. Only the weights of the linear operators within `language_model` transformers blocks are quantized. Vision model and multimodal projection are kept in original precision. Weights are quantized using a symmetric per-group scheme, with group size 128. The GPTQ algorithm is applied for quantization. Model checkpoint is saved in compressed_tensors format.

• DocLayout-YOLO↗

  juliozhao

• c70-h9↗

  ld9466311

• LFM2.5-1.2B-JP↗

  LiquidAI

• LFM2.5-Encoder-230M↗

  LiquidAI

  **LFM2.5-Encoder** is a family of multilingual bidirectional encoders built on the LFM2 architecture, available in two sizes: Both are masked language models with full bidirectional attention, designed to be fine-tuned into task-specific models (classification, token classification, retrieval, reranking, and semantic similarity) across 15 languages, and to run efficiently on-device. > [!NOTE] > 💻 **Demos**: We built the demos below from fine-tuned LFM2.5-Encoders. Each one runs in a CPU-only Hugging Face space: > - **Zero-shot prompt routing** — define your own routing lanes as free text. The model scores the whole prompt against every lane in one pass. > - **Zero-shot policy linting** — check text against your company's rules, written as free text. It scores every token against every rule in one pass. > - **Spell checking** — correct misspellings token by token. > - **PII detection** — spot and remove 40 kinds of personal information across 16 languages. > - **Masked-diffusion text generation** — bonus: run the encoder as a chatbot that generates text by iteratively unmasking instead of left to right.