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.

[!IMPORTANT] 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.

Training Details

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}
}