THE AI MODEL DIRECTORY 모델 탐색을 시작하세요

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언어부터 이미지, 오픈 웨이트부터 API까지. 기능과 실제 비용을 비교하세요.
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AI 모델 탐색 6

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TeleOCR

XingChen-AGI

TeleOCR: Navigating Document Parsing Across Digital and Camera-Captured Documents - **2026/09/10** - We have renamed NaviDC-OCR to TeleOCR, and all subsequent model iterations will be developed and released under the TeleOCR version. - **2026/09/01** - We noticed that EMNLP 2026 is hosting the Dr.DocBench Challenge, a document parsing competition. We evaluated NaviDC-OCR with its native weights, achieving better results than MinerU 2.5 Pro and PaddleOCR-VL 1.6. Detailed results are shown below **dr.docbench-challenge**. We welcome the use of NaviDC‑OCR for competitions. Going forward, we will continue to deliver competitive parsing models for the community. - **2026/08/29** — Thanks to Nandraj for the GGUF conversion and llama.cpp support 🔗 NaviDC-OCR-GGUF, and to the community for sharing their experience deploying NaviDC-OCR on Ascend 910B! - **2026/08/17** — NaviDC-OCR model weights and technical report have been released. **TeleOCR** is a lightweight (~1.2B parameters), open-source Vision-Language Model designed specifically for document parsing.

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시각 질의응답

입력 텍스트 · 이미지→ 출력 텍스트

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Xing4.0-29B-A4B-FP8

XingChen-AGI

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。

기본 정보 등록 · 상세 정보 보완 예정

텍스트 생성

입력 텍스트→ 출력 텍스트

요금 미확인멀티모달 및 채널 요금 별도 확인

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비교 추가출처 / 문서 ↗

Xing4.0-29B-A4B-GGUF

XingChen-AGI

This repository provides low-bit quantized weights and a quantization imatrix for Xing4.0-29B-A4B, covering a 2–8-bit quantization range and achieving better accuracy. The quantized GGUF files range from 11–34 GB, allowing you to choose appropriate weights to run on a single consumer-grade GPU. > [!Note] > This repository provides the model weights and configuration files for Xing4.0-29B-A4B in Hugging Face Transformers format, compatible with Transformers, vLLM, SGLang, KTransformers, and other mainstream inference frameworks. **Xing4.0-29B-A4B** is a next-generation large language model in the Xing series (formerly TeleChat), developed by China Telecom Artificial Intelligence Technology Co., Ltd. With 29B total parameters and only 4B activated per token, it natively supports a 256K context length, extensible to 512K. It is the first model of this scale trained entirely on the Ascend NPU platform with the MindSpore framework, and deeply optimized for complex engineering tasks.

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텍스트 생성

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비교 추가출처 / 문서 ↗

Xing4.0-29B-Intent

XingChen-AGI

For full project documentation, deployment instructions, SDK usage, and data details, visit the GitHub repository. **Xing4.0-29B-Intent** is a **schema-guided model for intent recognition and structured information extraction**, developed from Xing4.0-29B-A4B, the Xing semantic large language model from China Telecom Artificial Intelligence Technology Co., Ltd. It converts natural language into structured results for business systems, supporting intelligent assistants, request routing, and document information extraction. For example, a request such as “Find flights from Beijing to Shanghai tomorrow” needs to be parsed into a flight-search intent and parameters such as origin, destination, and date. Xing4.0-29B-Intent performs recognition and extraction according to the supplied category and field definitions, returning results in a unified JSON format.

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텍스트 생성

입력 텍스트→ 출력 텍스트

요금 미확인멀티모달 및 채널 요금 별도 확인

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비교 추가출처 / 문서 ↗