Xing4.0-29B-A4B

Xing4.0-29B-A4B

Generates text and works through multi-step jobs such as planning and tool calls, in English and Chinese, with a 256K context window. A mixture-of-experts model for developers who run it themselves.

Made by XingChen-AGI on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face.

About

What it is: Xing4.0-29B-A4B is a text generation model from XingChen-AGI for the transformers library. It aims at engineering jobs that need planning over long inputs, step-by-step reasoning, tool calls and structured replies. It is a mixture-of-experts model with 29B parameters in total and about 4B active per token, and it reads English and Chinese.

Who it suits: It suits developers and researchers who want an open-weight model they can run and adapt themselves, for agent work or for English and Chinese text.

Industry | Who uses it | How they use it | Example
Software | Developers building coding agents | Run the model as the engine of an agent that plans and calls tools | A script that works through a bug report in several steps
AI research | Teams that fine-tune open models | Start from the BF16 weights and train on their own data | A Chinese and English assistant tuned for one subject
Data engineering | Engineers who pass model output to other programs | Ask for replies in a fixed structure | A job that returns fields another program can read
Document analysis | Analysts who read long files | Paste a long document and ask questions about it | A question about a report that fits the 256K context

You can skip it if you only want a hosted chat site, or if your machine cannot hold a model of this size.

Where to use it:

• Python with the transformers library, on Windows, macOS or Linux
• vLLM and SGLang servers, which the card lists for serving it
• llama.cpp, through the separate GGUF build
• ms-swift and LLaMA-Factory, for fine-tuning
• Agent tools such as OpenCode and Claude Code, whose formats the card says it matches

Use case | Who | What they get back
Multi-step agent tasks | Developers | A plan and requests to call tools
Long-input questions | Analysts | Answers based on inputs of up to 256K tokens
Structured output | Engineers | Replies in a fixed layout that other programs can read
English and Chinese chat | Anyone | Plain text replies in either language

How to set it up: You need these first:

• Python on Windows, macOS or Linux
• Disk and memory for about 62.4 GB of BF16 weights, or less for the smaller builds

1. Install or update the Hugging Face download client.
2. Download this repository's files to your computer.
3. Open the quickstart in the model card and follow its steps to load the model in transformers.

pip install -U huggingface_hub
hf download XingChen-AGI/Xing4.0-29B-A4B

FP8, GGUF and W8A8 builds sit in their own repositories. The GGUF one holds 2 to 8-bit files of 11 to 34 GB for llama.cpp.

How to use it: After the download, you load the model in transformers and send it chat messages, as the card's inference section shows. The card also has a section of recommended parameters for generation. The only commands the source gives fetch the files.

hf download XingChen-AGI/Xing4.0-29B-A4B

Tool | What it does
pip install -U huggingface_hub | Installs or updates Hugging Face's download client
hf download | Fetches a model repository's files to your computer

Example: You paste a failing test and its log, then ask for a fix plan in steps and the first tool to call. The model answers with a step-by-step plan. When your agent framework gives it tools, it can request one by name.

For newcomers and experts: This is a language model you download and run yourself, rather than a website you chat on. A first small step is to run the two download commands above, then read the quickstart in the model card before you load anything, since the files are large.

Underneath, it is a mixture-of-experts transformer built on the mHC, MLA and MTP design, so about 13.8 percent of the parameters are active for each token. That lowers compute per token, though every weight still has to sit in memory. Context is 256K tokens natively, and the card says it extends to 512K. The training corpus was 1.5 trillion Chinese and English tokens.

Download the files with hf download
Load the model in transformers
Send a prompt of up to 256K tokens
Experts activate, about 4B per token
Get text or a tool request back

Good to know:

• Size: the BF16 repository is about 62.4 GB, and a 4-bit copy is estimated near 15.6 GB.
• Counting: Hugging Face lists 31.2B parameters, while the card's headline says 29B.
• Benchmarks: the card's badge shows 75.0 on SWE-bench Verified.
• Other versions: people have published 18 quantized and 11 fine-tuned versions of it.

Title: Weight size by precision
BF16: 62.4 GB
4-bit estimate: 15.6 GB

• License: Apache 2.0.
• Cost to run: none named. You supply the hardware, and the files come from Hugging Face.
• Data it reaches: only the Hugging Face download of the model files, in what the source shows.
• Not for: images or audio, since Hugging Face lists it only as a text generation model.

On Hugging Face, 13 November 2026

• Task: text generation
• Library: transformers
• Parameters: 31.2B
• Weights: about 62.4 GB in BF16; about 15.6 GB at 4-bit (estimate)
• Formats: Safetensors
• Downloads in the last 30 days: 5,406
• Likes: 1,867
• License: apache-2.0
• Languages: en, zh
• Quantized versions: 18
• Fine-tunes: 11
• Tags: transformers, safetensors, xing4_0, text-generation, conversational

Links

• Model: https://huggingface.co/XingChen-AGI/Xing4.0-29B-A4B
• Files: https://huggingface.co/XingChen-AGI/Xing4.0-29B-A4B/tree/main

Get the model

Download its files with Hugging Face's command line:

pip install -U huggingface_hub
hf download XingChen-AGI/Xing4.0-29B-A4B

Then follow the usage in its model card. Nothing in this kit runs by itself.