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AI Models 84759

• PP-DocLayout_plus-L↗

  PaddlePaddle

  A higher-precision layout area localization model trained on a self-built dataset containing Chinese and English papers, PPT, multi-layout magazines, contracts, books, exams, ancient books and research reports using RT-DETR-L. The layout detection model includes 20 common categories: document title, paragraph title, text, page number, abstract, table, references, footnotes, header, footer, algorithm, formula, formula number, image, table, seal, figure_table title, chart, and sidebar text and lists of references. The key metrics are as follow: **Note**: the evaluation set of the above precision indicators is the self built version sub area detection data set, including Chinese and English papers, magazines, newspapers, research reports PPT、 1000 document type pictures such as test papers and textbooks. You can also integrate the model inference of the layout detection module into your project. Before running the following code, please download the sample image to your local machine.

• termuxgpt↗

  parixit679

• econ-ai-detector-distilroberta-v3↗

  paulgp85

• Llama-3-8B-ProLong-512k-Instruct↗

  princeton-nlp

  **ProLong** ( Pr incet o n long -context language models) is a family of long-context models that are continued trained and supervised fine-tuned from Llama-3-8B, with a maximum context window of 512K tokens. Our main ProLong model is one of the best-performing long-context models at the 10B scale (evaluated by HELMET). To train this strong long-context model, we conduct thorough ablations on the long-context pre-training data, SFT data, and numerous other design choices. We demonstrate our findings in our paper, How to Train Long-Context Language Models (Effectively). Here are some quick facts about our main ProLong model: princeton-nlp/Llama-3-8B-ProLong-512k-Instruct. * Base model: meta-llama/Meta-Llama-3-8B-Instruct * Long-context continued training: 20B tokens on 64K training data (princeton-nlp/prolong-data-64K), and 20B tokens on 512K training data (princeton-nlp/prolong-data-512K) * Supervised fine-tuning (SFT): UltraChat * Maximum context window: 512K tokens

• Gliese-4B-OSS-0410-GGUF↗

  prithivMLmods

• Qwen3-0.6B-GGUF↗

  prithivMLmods

• Regulus-Qwen3-R1-Llama-Distill-GGUF↗

  prithivMLmods

• q-office-suite-runtime↗

  Qovaryx

• dolphin-2.9-llama3-8b-GGUF↗

  QuantFactory

  My appreciation for the sponsors of Dolphin 2.9: - Crusoe Cloud - provided excellent on-demand 10xL40S node Dolphin-2.9 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling. Dolphin is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly.

• Josiefied-Qwen2.5-7B-Instruct-abliterated-GGUF↗

  QuantFactory