Xing4.0-29B-A4B (bf16)

Full-precision (bf16) PyTorch port of Venastine-Research/Xing4.0-29B-A4B, for vLLM / TGI / Xinference / transformers users who don't want the GGUF quant.

• Format: pytorch_model.bin (bf16). safetensors conversion in progress.
• Architecture: identical to the original — drop-in for AutoModelForCausalLM.from_pretrained("rty813/Xing4.0-29B-A4B").
• Also loads in vLLM (vllm serve rty813/Xing4.0-29B-A4B) and Xinference (search "Xing4.0-29B-A4B").

Original model card:

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library_name: transformers license: apache-2.0 pipeline_tag: text-generation base_model: - XingChen-AGI/Xing4.0-29B-A4B

Xing4.0-29B-A4B

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.

For more information, please refer to our GitHub repository.

Highlights

• Agent-Oriented Architecture: Built on the mHC + MLA + MTP architecture, supporting multi-step planning, tool calling, and complex reasoning chain execution, ensuring task coherence and execution stability under long contexts.
• Deep Co-optimization with Ascend NPU: Adapted for Ascend 910C clusters using MindSpore/MindFormers, including feature adaptation for mHC and fused operator development, enabling stable and efficient training on the Ascend platform.
• **Significan