全站搜索

AI 模型 2119

• PP-OCRv5_mobile_det↗

  PaddlePaddle

  PP-OCRv5_mobile_det is one of the PP-OCRv5_det series, the latest generation of text detection models developed by the PaddleOCR team. It aims to efficiently and accurately supports the detection of text in diverse scenarios—including handwriting, vertical, rotated, and curved text—across multiple languages such as Simplified Chinese, Traditional Chinese, English, and Japanese. Key features include robust handling of complex layouts, varying text sizes, and challenging backgrounds, making it suitable for practical applications like document analysis, license plate recognition, and scene text detection. The key accuracy metrics are as follow: For details about PaddlePaddle installation, please refer to the PaddlePaddle official website. You can also integrate the model inference of the text detection module into your project. Before running the following code, please download the sample image to your local machine.

• PP-OCRv6_small_det↗

  PaddlePaddle

  PP-OCRv6 is a lightweight OCR system that combines architectural innovation with data-centric optimization. It redesigns the backbone, detection neck, and recognition neck around a unified MetaFormer-style building block with structural reparameterization. Three model tiers (medium, small, tiny) share the same block primitives, covering deployment scenarios from server to edge. 1. **Unified and Scalable Model Family:** A three-tier OCR model family spanning 1.5M to 34.5M parameters. PP-OCRv6_medium achieves 86.2% detection Hmean and 83.2% recognition accuracy, outperforming PP-OCRv5_server by +4.6% and +5.1% respectively. 2. **Lightweight Architectural Innovations:** (i) LCNetV4, a MetaFormer-style lightweight backbone with structural reparameterization; (ii) RepLKFPN, a detection neck with dilated reparameterizable depthwise convolutions; (iii) EncoderWithLightSVTR, a recognition neck with local-global attention and additive skip connections.

• bert-base-japanese-v2↗

  tohoku-nlp

  This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in unidic-lite package), followed by the WordPiece subword tokenization. Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective. The model architecture is the same as the original BERT base model; 12 layers, 768 dimensions of hidden states, and 12 attention heads. The models are trained on the Japanese version of Wikipedia. The training corpus is generated from the Wikipedia Cirrussearch dump file as of August 31, 2020.

• MiniCPM-V-4.6-Thinking-gguf↗

  openbmb

  > **This repository hosts the GGUF (llama.cpp) quantized version of MiniCPM-V 4.6 Thinking.** For the original BF16 weights and the full model card, please refer to openbmb/MiniCPM-V-4.6-Thinking. **MiniCPM-V 4.6 Thinking** is the long chain-of-thought reasoning variant of MiniCPM-V 4.6. It generates an explicit reasoning trace before producing the final answer, substantially boosting performance on complex multimodal reasoning, math, and OCR-heavy tasks, while keeping the same edge-friendly architecture (SigLIP2-400M vision encoder + Qwen3.5-0.8B LLM) and the mixed 4x/16x visual token compression of MiniCPM-V 4.6. MiniCPM-V 4.6 can be deployed across three mainstream end-side platforms — **iOS, Android and HarmonyOS**. The clips below are raw screen recordings on phone devices without edition.

• LTX-Video-gguf↗

  city96

  As this is a quantized model not a finetune, all the same restrictions/original license terms still apply. Place model files in `ComfyUI/models/unet` - see the GitHub readme for further install instructions.

• pythia-70m-deduped↗

  EleutherAI

  The *Pythia Scaling Suite* is a collection of models developed to facilitate interpretability research (see paper). It contains two sets of eight models of sizes 70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two models: one trained on the Pile, and one trained on the Pile after the dataset has been globally deduplicated. All 8 model sizes are trained on the exact same data, in the exact same order. We also provide 154 intermediate checkpoints per model, hosted on Hugging Face as branches. The Pythia model suite was designed to promote scientific research on large language models, especially interpretability research. Despite not centering downstream performance as a design goal, we find the models match or exceed the performance of similar and same-sized models, such as those in the OPT and GPT-Neo suites. Previously, we released an early version of the Pythia suite to the public. However, we decided to retrain the model suite to address a few hyperparameter discrepancies. This model card lists the changes ; see appendix B in the Pythia paper for further discussion. We found no difference in benchmark performance between the two Pythia versions. The old models are still available, but we suggest the retrained suite if you are just starting to use Pythia. **This is the current release.**

• pegasus-large↗

  Google

  We train a pegasus model with sampled gap sentence ratios on both C4 and HugeNews, and stochastically sample important sentences. The updated the results are reported in this table. The "Mixed & Stochastic" model has the following changes: - trained on both C4 and HugeNews (dataset mixture is weighted by their number of examples). - trained for 1.5M instead of 500k (we observe slower convergence on pretraining perplexity). - the model uniformly sample a gap sentence ratio between 15% and 45%. - importance sentences are sampled using a 20% uniform noise to importance scores. - the sentencepiece tokenizer is updated to be able to encode newline character. (*) the numbers of wikihow and big_patent datasets are not comparable because of change in tokenization and data: - wikihow dataset contains newline characters which is useful for paragraph segmentation, the C4 and HugeNews model's sentencepiece tokenizer doesn't encode newline and loose this information. - we update the BigPatent dataset to preserve casing, some format cleanings are also changed, please refer to change in TFDS.

• DeepSeek-V4.1-Flash-1017-ROCMFP-GGUF↗

  Lucebox

  Lucebox builds of DeepSeek's October 17 update of V4.1 Flash (`deepseek-ai/DeepSeek-V4.1-Flash-1017`, revision `1bae0e10`) for the Lucebox DS4.1 engine (AMD Radeon AI PRO R9700 + Ryzen AI Max). Routed experts in the engine's ROCMFP block formats; attention, shared experts and head in Q8_0; both Engram tables embedded, read in place. Same recipes and the same quality harness as our September V4.1 Flash files, re-measured against the 1017 model. For reference, the September files scored KL 0.266 (ROCMFP2S), 0.246 (MIX10) and 0.204 (fp2s with fp3 down everywhere) against the September model (teacher ppl 8.45) on the same tokens. **Which one:** ROCMFP2S is what the 128 GB Lucebox installs (the hottest experts on the R9700, the rest on the Ryzen AI Max and the SSD). MIX10 is the better file where it fits resident (192 GB boards). ROCMFP23S is the best quality of the three, for two-box setups and boards with room for 179 GiB of experts. The `-IM1017` files are the same recipes calibrated on the 1017 weights; prefer them (Lucebox Manage installs `ROCMFP2S-IM1017`).

• Qwen3.8-Flash-Next-MLX-Serve-mixed-4-8bit↗

  ddalcu

  > **Use Qwen3.8-Flash-Next-MLX-Serve-iQ-MLX-4.7bpw instead.** > It is this pack re-quantized with an importance matrix collected on the bf16 > model: same layout, same 75 GB, same bytes read per token, closer to bf16 > (KLD median 0.0052 vs 0.0066, top-1 88.6% vs 87.9% against bf16's own > logits). This pack stays up for existing installs but gets no more updates. > > Switching from this pack? `ngram_table.bin` (32 GB) is identical in both. > Move it into the new pack's folder before downloading and the download > skips it: > > mlx-serve pack of Qwen/Qwen3.8-Flash-Next, the Qwen4 preview architecture (`model_type: qwen4_exp`). Runs on a 128 GB Mac with about 75 GB resident. Includes the MTP head and the vision tower (image and video input). Measured on an M4 Max 128 GB with mlx-serve 26.8.11: ~75 GB resident, decode ~60 tok/s serial and 78 tok/s with MTP (`--mtp`; +41% on code, a few percent slower on prose), prefill ~730 tok/s, a needle at 24.8k tokens recovered with sparse attention engaged. Prefix cache on, images included.

• FLUX-3-Image-Klein-GGUF↗

  AtomicChat

  Black Forest Labs' FLUX 3 Image Klein as GGUF for stable-diffusion.cpp, built from the published BF16 weights with our own importance matrix and measured on one stand against every other Klein GGUF we could find. The renders and measurements are public. Built by the team behind Atomic Chat , the app that runs open-weight models locally and offline. Everything to run Klein in stable-diffusion.cpp is here: the image model, the Qwen3-VL-4B text encoder ( text_encoders/ ) and the autoencoder ( vae/ ). The image model files also load in ComfyUI through ComfyUI-GGUF; see In ComfyUI . The prompt upsampler Klein expects is in FLUX-3-Image-Klein-pup-GGUF . AD-Q5_K stays 14% closer to the BF16 picture than unsloth's UD-Q5_K_XL at a smaller size, and AD-Q4_K 21% closer than their Q4_K_M . Where their file is better, the table below says so. The text encoder moves the picture as much as the image model does. Our AD-Q4_K encoder is 21% closer than the Q4_K_M most people use, at the same size. Looking for uncensored? The open Klein pipeline has no content filter. Our text encoder and prompt upsampler without refusals are in FLUX-3-Image-Klein-TE-Uncensored-GGUF and FLUX-3-Image-Klein-pup-Uncensored-GGUF ; what they change on the pictures is in Content filtering . FLUX 3 Image Klein is Black Forest Labs' 5.6B text-to-image and editing model: one model for generation, single-reference edits and up to ten reference images, distilled to **16 steps at CFG 1**. This repo has the image model as GGUF for stable-diffusion.cpp, with the text encoder and the autoencoder next to it.