Instructions to use Kuromi22/Xing4.0-29B-A4B-Official_Document_Writing_8k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kuromi22/Xing4.0-29B-A4B-Official_Document_Writing_8k with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Kuromi22/Xing4.0-29B-A4B-Official_Document_Writing_8k", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Kuromi22/Xing4.0-29B-A4B-Official_Document_Writing_8k", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Kuromi22/Xing4.0-29B-A4B-Official_Document_Writing_8k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kuromi22/Xing4.0-29B-A4B-Official_Document_Writing_8k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kuromi22/Xing4.0-29B-A4B-Official_Document_Writing_8k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Kuromi22/Xing4.0-29B-A4B-Official_Document_Writing_8k

SGLang

How to use Kuromi22/Xing4.0-29B-A4B-Official_Document_Writing_8k with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Kuromi22/Xing4.0-29B-A4B-Official_Document_Writing_8k" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kuromi22/Xing4.0-29B-A4B-Official_Document_Writing_8k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Kuromi22/Xing4.0-29B-A4B-Official_Document_Writing_8k" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kuromi22/Xing4.0-29B-A4B-Official_Document_Writing_8k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Kuromi22/Xing4.0-29B-A4B-Official_Document_Writing_8k with Docker Model Runner:
```
docker model run hf.co/Kuromi22/Xing4.0-29B-A4B-Official_Document_Writing_8k
```

Xing4.0-29B-A4B-Official_Document_Writing_8k

This model is a fine-tuned version of Xing4.0-29B-A4B, specifically optimized for official document writing in Chinese. It supports outline generation from titles, full-text generation from titles, and paragraph-level reference writing based on outlines, reference materials, and preceding context. It is designed for generating formal documents such as government reports, Party-building materials, and speeches.

Base Model

The base model is Xing4.0-29B-A4B (formerly the TeleChat series), developed by China Telecom AI Technology Co., Ltd. It uses a Mixture-of-Experts (MoE) architecture with 29B total parameters, activating only 4B per inference step, balancing generation quality and deployment efficiency. It natively supports a 256K context window, providing a solid foundation for coherent long-form document generation. The model was trained on Ascend NPUs with the MindSpore framework.

Training Details

Fine-tuning Task: Official document writing (title-to-outline / title-to-full-text / paragraph-level reference writing)
Training Data: The fine-tuning dataset contains approximately 56,000 high-quality samples, covering government reports, Party-building materials, speeches, and other formal document types, spanning outline generation, full-text generation, and paragraph continuation tasks.
Training Length: 8K
Training Method: Full-parameter fine-tuning
Training Hardware: Ascend NPU cluster
Training Framework: MindSpore

Quickstart

Inference

Xing4.0-29B-A4B-Official_Document_Writing_8k can be accessed via an OpenAI-compatible API:

from openai import OpenAI

client = OpenAI(
    base_url="your-base-url",
    api_key="your-api-key",
)

completion = client.chat.completions.create(
    model="Xing4.0-29B-A4B-Official_Document_Writing_8k",
    messages=[{"role": "user", "content": "基于以下信息生成文章大纲，生成大纲层级：二级，要求：大纲格式规范，涵盖知识完整\n\n\n标题：在高质量发展中促进共同富裕\n关键词：共同富裕、高质量发展、初次分配、再分配、社会公益\n\n\n开始生成大纲:"}],
    temperature=1.0,
    top_p=0.95,
    extra_body={
        "repetition_penalty": 1.05,
        "skip_special_tokens": False,
        "spaces_between_special_tokens": False,
        "chat_template_kwargs": {
            "enable_thinking": True,  # Set to False to disable thinking
        },
    },
)

print(completion.choices[0].message.content)

Recommended Parameters

temperature	top_p	repetition_penalty
1.1	0.95	1.02

Key Capabilities

Capability	Description
Title-to-Outline	Generate a structured outline from a given document title
Title-to-Full-Text	Generate a complete document directly from a given title
Paragraph-level Reference Writing	Generate high-quality content paragraph by paragraph, leveraging outline structure, reference materials, and preceding context

Downloads last month: 347

Safetensors

Model size

31B params

Tensor type

F32

BF16

Model tree for Kuromi22/Xing4.0-29B-A4B-Official_Document_Writing_8k

Base model

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

Finetuned

(10)

this model