Fine‑tuning a 7B model on 549 Chinese A‑share signal samples

说明与用途

一个包含549个样本的中文A股信号数据集过小且类别不平衡，导致使用LoRA微调的7B模型无法有效学习。增加模型容量或训练轮次并未提升准确率，主要瓶颈在于数据覆盖不足和缺乏上下文特征。

问题

A small, imbalanced dataset of 549 samples across 12 stocks and 5 classes limited the effectiveness of a 7B model fine‑tuned with LoRA, causing the model to misclassify rare but critical signals.

结果

The experiment showed that adding model capacity or training epochs did not improve overall accuracy; the primary limitation was dataset imbalance and missing contextual features. The author released a free, key‑less data pipeline, regime‑split backtest, and detailed failure log to aid reproducibility.

工作流

• Collect daily snapshots with a cron job using Tencent’s free quote endpoint
• Parse GBK‑encoded data into price, volume, etc.
• Create chat‑style training rows (instruction/input/output) with structured verdicts
• Fine‑tune Qwen2.5‑7B‑Instruct with QLoRA (rank 16, 3 epochs) on an RTX 3080
• Run evaluation with fixed test set, adjust rank and epochs, observe no score improvement
• Analyze errors: rare class collapses into common class due to 5.8:1 prior
• Identify infrastructure bugs (trl version, custom tokens, environment isolation)
• Plan improvements: balance classes, add regime context, time‑based split, freeze eval set, route low‑confidence outputs for re‑check
• Publish data pipeline, backtest, and failure log on GitHub
• Offer packaged skill for agents

证据

The gap between the most and least frequent class is 5.8:1. The rarest class — the one that tells you to get out — is also the one that matters most.

来源

First run (rank 16, 3 epochs): the model produced fluent Chinese analysis but didn't reliably follow the output format of the training rows.

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When your dataset is coverage‑bound, adding capacity only reorders your errors. It doesn't create knowledge that isn't in the examples.

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