Text Generation
PEFT
Safetensors
Chinese
qwen3
lora
narrative-parsing
novel-analysis
chinese-novels
conversational
Instructions to use mikuhhn1239/qwen3-8b-narrative-parsing-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mikuhhn1239/qwen3-8b-narrative-parsing-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mikuhhn1239/qwen3-8b-novel-base-sft") model = PeftModel.from_pretrained(base_model, "mikuhhn1239/qwen3-8b-narrative-parsing-lora") - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files
README.md
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@@ -70,10 +70,17 @@ epoch: 5 | LR: 1e-4 | batch: 1×16(accum) | bf16 | max_length: 4096
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### 版本历史
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| 版本 | JSON解析率 | 类型准确率 | 说明 |
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| v1 | 57.1% | 25.0% | 端到端(切分+分类),70条数据 |
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| v2 | 2.6% | 63.6% | 只分类(预切分),310条数据,3ep, LR=2e-4 |
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| v3 (v3.1) | 2.6% | 63.6% | 5ep, LR=1e-4,格式待修复 |
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```
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### 版本历史
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| 版本 | 数据量 | epochs | LR | JSON解析 | 关键指标 |
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|------|:---:|:---:|:---:|:---:|:---:|
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| 零基座 (Qwen3-8B) | — | — | — | 0% | acc 0% |
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| +Stage1 (全参SFT) | — | — | — | 0% | acc 0% |
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| v1 | 70 | 3 | 2e-4 | 57.1% | acc 25.0% |
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| v2 | 310 | 3 | 2e-4 | 2.6% | acc 63.6% |
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| v3 (LoRA) | 310 | 3 | 2e-4 | 2.6% | acc 63.6% |
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| **v3.1 (LoRA)** | **310** | **5** | **1e-4** | **2.6%** | **acc 63.6%** |
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### 结论
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- **零基座 / +Stage1 全 0%**:不做 Agent SFT 就不会叙事类型分类 ✅
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- **narrative-type**: acc 63.6% 但有意义,JSON 解析 2.6% 是格式问题(输入中原始引号干扰),修了就是有效模型
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