Chirp Chirp! 🐦 We are introducing Ornith-1.5, a major step toward building foundation models through end-to-end self-improvement. Ornith-1.5 extends Ornith-1.0 (which was developed on top of Qwen3.5 and Gemma4 with additional continued pretraining, mid-training, and post-training) by expanding the self-improvement loop from scaffold and rollout optimization to jointly optimizing task generation, scaffold construction, and solution rollouts. Rather than relying on a fixed set of human-curated tasks and manually designed harnesses, Ornith-1.5 continuously generates new training tasks, discovers effective strategies for solving them, and improves the policy through reinforcement learning. For more details on the task, harness, and rollout reward design, please refer to our blog. This model card documents **Ornith-1.5-9B**, the most lightweight member of the Ornith-1.5 family — a 9B dense model designed for efficient single-GPU deployment, and edge-deployable on mobile devices via its quantized Ornith-1.5-9B-Mobile variant.

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orpheus-3b-0.1-ft-GGUF

Mungert

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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orpheus-finetuned-3b-GGUF

Mungert

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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PaddleOCR-VL-1.6-GGUF

Mungert

PaddleOCR-VL-1.6: Expanding the Frontier of Document Parsing with Under-Optimized Region Refinement and Progressive Post-Training We introduce PaddleOCR-VL-1.6, an upgraded compact document parsing model built upon PaddleOCR-VL-1.5. PaddleOCR-VL-1.6 introduces a region-aware data optimization framework that identifies weak regions from the previous model, applies targeted enhancement to those regions, and improves the reliability of supervision signals. It further adopts a progressive post-training recipe based on curated data selection and reinforcement learning, pushing model performance to a higher level through staged optimization. **PaddleOCR-VL-1.6 achieves a new state-of-the-art score of 96.33% on OmniDocBench v1.6, sets new records on OmniDocBench v1.5 and Real5-OmniDocBench as well**, and demonstrates strong competitiveness against top-tier VLMs. The model architecture is fully compatible with PaddleOCR-VL-1.5, enabling zero-cost plug-and-play migration. **🚀 New SOTA Accuracy**: OmniDocBench v1.6 achieves **96.33%**, setting new state-of-the-art records on OmniDocBench v1.5 and Real5-OmniDocBench as well. It delivers comprehensive leading performance across text, formula, and table recognition, surpassing both open-source and closed-source solutions.

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Phi-4-mini-instruct.gguf

Mungert

Phi-4-mini-instruct is a lightweight open model built upon synthetic data and filtered publicly available websites - with a focus on high-quality, reasoning dense data. The model belongs to the Phi-4 model family and supports 128K token context length. The model underwent an enhancement process, incorporating both supervised fine-tuning and direct preference optimization to support precise instruction adherence and robust safety measures. 📰 Phi-4-mini Microsoft Blog 📖 Phi-4-mini Technical Report 👩‍🍳 Phi Cookbook 🏡 Phi Portal 🖥️ Try It Azure, Huggingface This format is used when the user wants the model to provide function calls based on the given tools. The user should define the available tools in the system prompt, wrapped by ` ` and ` ` tokens. The tools must be specified in JSON format using a structured JSON dump.

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