Upload v8_stage3_xml

README.md

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+---
+license: apache-2.0
+base_model: kmd2525/v8_stage2_yaml-merged
+tags:
+  - structured-output
+  - xml
+  - sft
+  - sequential-format-learning
+language:
+  - en
+  - ja
+---
+
+# v8_stage3_xml-merged
+
+## Model Description
+
+This model is **Stage 3** of the Sequential Format Learning (v8 strategy) for structured data output.
+
+### Training Strategy
+
+Based on Person U's approach that achieved 0.84 on the leaderboard:
+- Train one format at a time
+- Merge LoRA to base model after each stage
+- Use merged model as the base for the next stage
+
+### Stage 3 Focus: XML
+
+- **Format**: XML (500 samples)
+- **Goal**: 95%+ parse success rate for XML with proper & escaping
+- **Base Model**: `kmd2525/v8_stage2_yaml-merged` (Stage 2 merged model)
+
+### Previous Stages
+
+- Stage 1: JSON/CSV (800 samples) → JSON 100%, CSV 100%
+- Stage 2: YAML (500 samples) → YAML 100%
+
+### Training Parameters
+
+- MAX_SEQ_LEN: 1024
+- EPOCHS: 2
+- Learning Rate: 3e-05
+- LoRA R: 64, Alpha: 128
+
+### Sequential Format Learning Pipeline
+
+```
+Stage 1: JSON/CSV (800) ✅
+    ↓
+Stage 2: YAML (500) ✅
+    ↓
+Stage 3: XML (500) ← This model
+    ↓
+Stage 4: Mixed/TOML (1000)
+    ↓
+Final Model → LB 0.8+
+```
+
+### Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("kmd2525/v8_stage3_xml-merged")
+tokenizer = AutoTokenizer.from_pretrained("kmd2525/v8_stage3_xml-merged")
+```
+
+### Next Stage
+
+Use this model as the base for Stage 4 (Mixed/TOML final tuning):
+```python
+os.environ["SFT_BASE_MODEL"] = "kmd2525/v8_stage3_xml-merged"
+```

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