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AI Models 2119

• bert-large-cased-whole-word-masking-finetuned-squad↗

  google-bert

  Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in this paper and first released in this repository. This model is cased: it makes a difference between english and English. Differently to other BERT models, this model was trained with a new technique: Whole Word Masking. In this case, all of the tokens corresponding to a word are masked at once. The overall masking rate remains the same. The training is identical -- each masked WordPiece token is predicted independently.

• Qwen-Image-2.1-Turbo-GGUF↗

  unsloth

  This is a GGUF quantized version of Qwen-Image-2.1-Turbo, the 8-step distilled checkpoint of Qwen-Image-2.1. unsloth/Qwen-Image-2.1-Turbo-GGUF uses Unsloth Dynamic 2.0 methodology for SOTA performance. - Important layers are upcasted to higher precision, per tensor, from a measured sensitivity scan. Every file uses the same per-tensor layout as the matching file in unsloth/Qwen-Image-2.1-GGUF, with an importance matrix collected on Turbo itself at its own 8-step schedule. - Run these with Unsloth Desktop, ComfyUI (ComfyUI-GGUF), stable-diffusion.cpp and more. A GGUF is the denoiser only, so it needs the VAE and the Qwen3-VL text encoder alongside it. Turbo uses the same text encoder and VAE as Qwen-Image-2.1. - VAE: unsloth/Qwen-Image-2.1-FP8 `vae/qwen_image_2.1_vae_bf16.safetensors`. Text encoder: unsloth/Qwen3-VL-8B-Instruct-GGUF `Qwen3-VL-8B-Instruct-UD-Q4_K_XL.gguf`, the Dynamic 2.0 4-bit rung rather than the uniform `Q4_K_M`. - Turbo is meant for **8 steps at CFG 1** with the sampling schedule it ships (`sample_sigmas` in model_index.json). Unsloth Desktop applies it automatically. LPIPS (AlexNet, lower is better) against the bf16 Turbo transformer, same text encoder, VAE, prompts and seeds, 1024x1024, 8 steps, CFG 1, rendered through Unsloth Desktop's diffusers route. Mean with 95% bootstrap CI over 8 prompts x 2 seeds; the held-out column uses 8 further prompts x 2 seeds that were not part of the importance-matrix calibration. Two bf16 runs of the same seeds give 0.000 (run-to-run floor).

• d3-mini↗

  vllm-sr

  **d3-mini** is the 4B multimodal foundation decision model of Decision 3.0, the decision models of vLLM Semantic Router. Give it an input (text or JSON, optionally with images and videos) and the questions you need answered: pick one of several options, say yes or no, or rate on a scale. It answers them all in one call and returns a probability for every answer, without generating text. **Try it free** at decision.vllm-sr.ai · Official Space: vllm-sr/Decision-3.0 - **Jev Decision Index 0.3.2: 52.6 on the official text board**, first among the entries of similar size and on the board's Pareto frontier; public suite 54.90, measured with the official 0.3 kit on the released weights: all 140,178 public requests answered, none unsupported. - **+7.7 over Decision 2.0** (its 4B model: 45.0 on the official board): +10.7 on the public suite, ahead in all five of its areas, and +8.2 and +3.4 on the private same-skill tests and new-domain tasks. - **Reads images:** multiple images per request (PNG, JPEG or WebP), given as paths, URLs, PIL images or base64 data URLs; every question of the request sees all of them. - **Reads videos:** multiple videos per request (MP4, WebM, MOV or MKV), given as paths, URLs, base64 data URLs or frame arrays, read at 2 frames per second; **Perception Test (multiple-choice video QA): 70.3** (internal evaluation). - **Speed:** a median of 18.1 ms for a text request, 112 ms for a request with an image and 336 ms for a request with a 10-second video, on one AMD Instinct MI325X GPU, one request at a time with AutoModel. - **Serving:** 151 requests/s with p99 under 1 s on one AMD Instinct MI325X with vLLM Semantic Router, 2.7× AutoModel (one-question text, with batching, now the default); with one request per forward, the former default, it gave answers identical to AutoModel's (Serving performance). - **Many questions, one call:** Choice, Yes / No and Score questions about the same input are answered together, each from its own forward pass over the input, with a probability for every option.

• whisper-small.en-gguf↗

  handy-computer

  Ported from upstream commit e872752, pinned 2026-04-25. Validated against the transformers reference at transcribe.cpp commit 0a26478 on 2026-09-13. OpenAI Whisper small.en — converted to GGUF for transcribe.cpp. English-only; faster than the multilingual model at the same size. Encoder-decoder transformer; 30-second windows with chunked long-form decoding. WER on the full LibriSpeech test-clean split (2,620 utterances), batch size 1, timestamps none. Figures without a commit were published before provenance was recorded.

• SAM2.1-Hiera-Tiny-Mask-Decoder↗

  litert-community

• Carnice-Qwen3.6-MoE-35B-A3B-APEX-MTP-GGUF↗

  mudler

  ⚡ Each donation = another big MoE quantized I host 30+ free APEX MoE quantizations as independent research. My only local hardware is an NVIDIA DGX Spark (122 GB unified memory), enough for ~30-50B-class MoEs, but bigger ones (200B+) require rented compute on H100/H200/Blackwell, typically $20-100 per quant. If APEX quants are useful to you, your support directly funds those bigger runs. **APEX (Adaptive Precision for EXpert Models)** quantizations of samuelcardillo/Carnice-Qwen3.6-MoE-35B-A3B, with the **MTP (multi-token prediction) head bundled** for in-the-box self-speculative decoding. These GGUFs bundle the model's **MTP (multi-token prediction) head** alongside the trunk in a single file, courtesy of llama.cpp PR #22673. With a recent llama.cpp (>= commit 255582687) you can enable self-speculative decoding using just this one file — no separate draft model needed:

• UVDoc↗

  PaddlePaddle

  The main purpose of text image correction is to carry out geometric transformation on the image to correct the document distortion, inclination, perspective deformation and other problems in the image, so that the subsequent text recognition can be more accurate. For details about PaddlePaddle installation, please refer to the PaddlePaddle official website. You can also integrate the model inference of the TextImageUnwarping module into your project. Before running the following code, please download the sample image to your local machine.

• wav2vec2-large-xlsr-marathi↗

  sumedh

  Fine-tuned facebook/wav2vec2-large-xlsr-53 on Marathi using the Open SLR64 dataset. When using this model, make sure that your speech input is sampled at 16kHz. This data contains only female voices but the model works well for male voices too. Trained on Google Colab Pro on Tesla P100 16GB GPU. **WER (Word Error Rate) on the Test Set**: 12.70 % The model can be used directly without a language model as follows, given that your dataset has Marathi `actual_text` and `path_in_folder` columns: Train-Test ratio was 90:10. The training notebook Colab link here. weights-and-biases run summary here

• DeepSeek-V4-Flash-DSpark-draft-GGUF↗

  YanissAmz

• MiniMax-H3-GGUF↗

  Abiray

  This repository (`Abiray/MiniMax-H3-GGUF`) provides GGUF quantized versions and necessary component files for the MiniMax H3 model. MiniMax H3 is a general-purpose, omni-modal generative system that supports unified understanding of multimodal contexts composed of text, images, video, and audio. It can generate video with native stereo audio at resolutions up to 2K and durations of up to 15 seconds. --- If you are looking for a smaller model with the same great quality that fits better on consumer-tier GPUs, please check out the **MiniMax-H3-Pruned-GGUF** repository. The pruned architecture is compressed down to 8.9 GB – 21.6 GB, bringing MiniMax H3 execution directly to consumer hardware. **Available Pruned Quants:** | Bit Size | Quantization Type | File Size | | :--- | :--- | :--- | | **3-bit** | `Q3_K_M` | 8.9 GB | | **4-bit** | `Q4_K_S`, `Q4_K_M` | 11.6 GB | | **5-bit** | `Q5_K_S`, `Q5_K_M` | 14.1 GB | | **6-bit** | `Q6_K` | 16.7 GB | | **8-bit** | `Q8_0` | 21.6 GB | --- This repository includes quantized versions of both the `FL2VA` (First-and-last-frame mode) and `Ref2VA` (Omni-reference mode) base models. * **FL2VA Models:** * `MiniMax-H3-FL2VA-Q3_K_M.gguf` (15.6 GB) * `MiniMax-H3-FL2VA-Q3_K_S.gguf` (15.6 GB) * `MiniMax-H3-FL2VA-Q4_0.gguf` (18.6 GB) * `MiniMax-H3-FL2VA-Q4_K_M.gguf` (19.9 GB) * `MiniMax-H3-FL2VA-Q4_K_S.gguf` (19.9 GB) * `MiniMax-H3-FL2VA-Q5_0.gguf` (22.8 GB) * `MiniMax-H3-FL2VA-Q5_K_M.gguf` (23.9 GB) * `MiniMax-H3-FL2VA-Q5_K_S.gguf` (23.9 GB) * `MiniMax-H3-FL2VA-Q6_K.gguf` (28.2 GB) * `MiniMax-H3-FL2VA-Q8_0.gguf` (36 GB) * **Ref2VA Models:** * `MiniMax-H3-Ref2VA-Q3_K_M.gguf` (15.6 GB) * `MiniMax-H3-Ref2VA-Q3_K_S.gguf` (15.6 GB) * `MiniMax-H3-Ref2VA-Q4_0.gguf` (18.6 GB) * `MiniMax-H3-Ref2VA-Q4_K_M.gguf` (19.9 GB) * `MiniMax-H3-Ref2VA-Q4_K_S.gguf` (19.9 GB) * `MiniMax-H3-Ref2VA-Q5_0.gguf` (22.8 GB) * `MiniMax-H3-Ref2VA-Q5_K_M.gguf` (23.9 GB) * `MiniMax-H3-Ref2VA-Q5_K_S.gguf` (23.9 GB) * `MiniMax-H3-Ref2VA-Q6_K.gguf` (28.2 GB) * `MiniMax-H3-Ref2VA-Q8_0.gguf` (36 GB) * `qwen3vl_32b_minimax_h3_int4_convrot.safetensors` (15 GB) * `qwen3vl_32b_minimax_h3_nvfp4_awq.safetensors` (27.1 GB) * `qwen3vl_32b_minimax_h3-Q4_K_M.gguf` (14.6 GB) * `minimax_h3_audio_vae_fp32.safetensors` (605 MB) * `minimax_h3_video_vae_fp16.safetensors` (5.21 GB) * **H3-Base-FL2VA:** First-and-last-frame mode. Supports zero, one, or two input images. * No image input: T