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XingChen-AGI/Xing4.0-29B-Intent
Text Generation β’ 31B β’ Updated β’ 5.41k β’ 326 -
XingChen-AGI/Xing4.0-29B-Intent-FP8
Text Generation β’ 31B β’ Updated β’ 870 β’ 7 -
XingChen-AGI/Xing4.0-29B-Intent-GGUF
Text Generation β’ 31B β’ Updated β’ 1.17k β’ 7 -
XingChen-AGI/Xing4.0-29B-Intent-oss
Preview β’ Updated β’ 797 β’ 13
AI & ML interests
XingChen-AGI is the open-source organization, dedicated to building agent-era large language models. Our flagship Xing4.0-29B-A4B is a 29B MoE model with only 4B activated parameters, natively supporting 256K context (extensible to 512K). With 4-bit quantization, it runs on a single consumer GPU (~15GB VRAM) and scores 93.52 on SuperCLUE Agent benchmark.
Recent Activity
XingChen-AGI
Open-source large models from China Telecom Artificial Intelligence Technology (Beijing) Co., Ltd.
Building agent-era LLMs β from flagship benchmarks to a single consumer GPU.
π Our Models
Xing4.0-29B-A4B
Our flagship 29B MoE language model with only 4B activated parameters:
- π§ 256K context natively, extensible to 512K
- π 75.0 on SWE-bench Verified Β· 93.52 on SuperCLUE-Agent
- π» 4-bit quantization runs locally on a single GPU with ~15GB VRAM (RTX 3090/4090)
- βοΈ Official runtimes: vLLM, SGLang, TensorRT-LLM, llama.cpp, KTransformers
- π¦ Precision builds: FP8 Β· GGUF Β· W8A8
Xing4.0-29B-Intent
A task-specific model built on Xing4.0-29B-A4B for schema-guided intent recognition and structured information extraction β turns natural language into structured JSON for business systems: intelligent assistants, request routing and document extraction.
- π― Four tasks in one model: intent recognition & slot filling Β· named entity recognition Β· relation extraction Β· event extraction β unified
category + argsoutput, configured by schema - π Top overall score (58) ahead of Gemini-3.6-Flash (49), Qwen3.8-Max (46) and GPT-5.6-Sol (45) on a 6,000-sample benchmark drawn from 48 public datasets β best on 5 of 6 metrics
- π Chinese & English
- π¦ Precision build: FP8
- π Companion dataset β 2.75M samples unifying 61 public datasets in one schema-guided format across 4 task types, 19 domains and 52 languages, plus China Telecom's in-house smart-device assistant annotations (Apache-2.0, upstream licenses preserved)
- π§° XingIntent SDK β open-source tooling for schema-driven training and structured inference
TeleOCR
A compact multimodal model that turns scans, PDFs and photos into structured text β complex layouts, tables, formulas and charts included.
- π 96.87 on OmniDocBench V1.6 Β· 88.53 on Wild-OmniDocBench
- π Bilingual (Chinese & English)
π Trained in-house, runs everywhere
All models are trained in-house on 1.5T tokens of curated bilingual data, and run across a wide range of hardware β from a single consumer GPU to production clusters.
π€ Community
- 30+ community derivatives β GGUF/MLX/ROCm quantizations and multi-chip adaptations (incl. BAAI FlagOS)
- Questions, deployment help or derivative showcases β open a Discussion on any repo, we respond.
- Code & mirrors: GitHub Β· ModelScope Β· Gitee
Named to Hugging Face's trending board top 4 (Sep 2026).
Maintained by the Xingchen General Artificial Intelligence Laboratory, China Telecom Artificial Intelligence Technology Co., Ltd.
Benchmarks are internal test results and do not represent final product quality.
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XingChen-AGI/Xing4.0-29B-Intent
Text Generation β’ 31B β’ Updated β’ 5.41k β’ 326 -
XingChen-AGI/Xing4.0-29B-Intent-FP8
Text Generation β’ 31B β’ Updated β’ 870 β’ 7 -
XingChen-AGI/Xing4.0-29B-Intent-GGUF
Text Generation β’ 31B β’ Updated β’ 1.17k β’ 7 -
XingChen-AGI/Xing4.0-29B-Intent-oss
Preview β’ Updated β’ 797 β’ 13