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

Libraries

How to use HITSZ-TMG/Xing4.0-29B-A4B-Finance-SFT with Transformers:

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

pipe = pipeline("text-generation", model="HITSZ-TMG/Xing4.0-29B-A4B-Finance-SFT", 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("HITSZ-TMG/Xing4.0-29B-A4B-Finance-SFT", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use HITSZ-TMG/Xing4.0-29B-A4B-Finance-SFT with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/HITSZ-TMG/Xing4.0-29B-A4B-Finance-SFT

SGLang

How to use HITSZ-TMG/Xing4.0-29B-A4B-Finance-SFT 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 "HITSZ-TMG/Xing4.0-29B-A4B-Finance-SFT" \
    --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": "HITSZ-TMG/Xing4.0-29B-A4B-Finance-SFT",
		"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 "HITSZ-TMG/Xing4.0-29B-A4B-Finance-SFT" \
        --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": "HITSZ-TMG/Xing4.0-29B-A4B-Finance-SFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use HITSZ-TMG/Xing4.0-29B-A4B-Finance-SFT with Docker Model Runner:
```
docker model run hf.co/HITSZ-TMG/Xing4.0-29B-A4B-Finance-SFT
```

Xing4.0-29B-A4B-Finance-SFT

This model is a full-parameter supervised fine-tune (SFT) of XingChen-AGI/Xing4.0-29B-A4B, specialized for Chinese and English financial-domain tasks.

This repository requires trust_remote_code=True. It ships custom modeling code (modeling_xing4_0.py, configuration_xing4_0.py, tokenization_xing4_0.py) and declares an auto_map in config.json. Do not use trust_remote_code=False.

Base Model

Xing4.0-29B-A4B is developed by China Telecom AI Technology Co., Ltd. (中电信人工智能科技有限公司). It is a Mixture-of-Experts model in the Xing series (formerly the TeleChat series) with 29B total parameters, of which only ~4B are activated per token. It uses the mHC + MLA + MTP architecture, natively supports a 256K context window (extensible to 512K), and was trained on Ascend NPU clusters with the MindSpore framework.

	Xing4.0-29B-A4B
Total / active parameters	29B / 4B
Layers	40
Hidden size	3584
Attention	MLA
Routed experts	64 (4 active per token) + 1 shared
Context length	256K

Training Details

Item	Value
Fine-tuning type	Full-parameter SFT
Training sequence length	2048
Learning rate	7e-6 (AdamW, betas 0.9/0.95, weight decay 0.1)
LR schedule	Warmup + cosine decay, 30 warmup steps, cos_min_ratio 0.1
Total steps	578 (≈2 epochs)
Per-device batch size	4
Gradient accumulation	4
GPUs	8
Effective batch size	128
Precision	bfloat16
Distributed strategy	DeepSpeed ZeRO-3 (optimizer CPU offload, overlap_comm)
Gradient clipping	1.0

Training Data

Approximately 368K financial-domain multi-turn chat samples (fin_sft_v1_368k_messages.jsonl, 368,415 records), aggregated from publicly available financial NLP datasets and corpora, covering:

Financial examinations / certification QA — accounting, securities, and finance exam questions with explanations (largest component).
Financial sentiment classification — classifying financial news and statements as positive / negative / neutral.
Financial question answering — QA over financial filings and reports.
Financial evaluation sets — a small held-out-style evaluation portion.

Samples are formatted in the base model's native chat template (<_system> / <_user> / <_bot> / <_end>), with <think>...</think> reasoning blocks where present.

Quickstart

Because this is a custom_code model, set trust_remote_code=True:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "HITSZ-TMG/Xing4.0-29B-A4B-Finance-SFT"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    trust_remote_code=True,
    device_map="auto",
    dtype="bfloat16",
)

Chat

messages = [
    {"role": "system", "content": "你是一名专业的金融领域助手。"},
    {"role": "user", "content": "解释一下什么是久期，以及它对债券价格的影响。"},
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=1024, temperature=1.0, top_p=0.95)
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Thinking mode is controlled by the enable_thinking template flag (apply_chat_template(..., enable_thinking=True/False)).

Recommended generation parameters: temperature=1.0, top_p=0.95, repetition_penalty=1.05.

Intended Use and Limitations

Intended for: financial-domain Chinese/English assistants, financial QA, financial sentiment analysis, and as a starting point for further domain adaptation in banking, securities, and accounting scenarios.
Not intended for: use as a substitute for professional financial, legal, or investment advice; autonomous trading; or any high-stakes financial decision-making without human review.
The model inherits the limitations and biases of its base model and of the underlying public financial datasets.
Outputs may be factually incorrect ("hallucinated"), especially on numeric and regulatory questions. Always verify financial figures against primary sources.
This model has not been safety-aligned or red-teamed beyond the base model's own training; apply your own guardrails in production.

License

This model is released under the Apache-2.0 license, inherited from XingChen-AGI/Xing4.0-29B-A4B. Please also comply with any terms attached to the upstream financial datasets used during fine-tuning.

Citation

If you use this model, please cite the base model:

@misc{xing4_0_29b_a4b,
  title  = {Xing4.0-29B-A4B},
  author = {China Telecom Artificial Intelligence Technology Co., Ltd.},
  year   = {2026},
  url    = {https://huggingface.co/XingChen-AGI/Xing4.0-29B-A4B}
}

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F32

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