---
license: apache-2.0
base_model: kmd2525/v8_stage3_xml-merged
tags:
  - structured-output
  - json
  - csv
  - yaml
  - xml
  - toml
  - sft
  - sequential-format-learning
language:
  - en
  - ja
---

# v8 Final Model - Sequential Format Learning

## Model Description

This is the **final model** of the Sequential Format Learning (v8 strategy) for structured data output.
Trained through 4 stages to achieve optimal performance across all structured formats.

### Training Strategy

Based on Person U's approach that achieved 0.84 on the leaderboard:
- Train one format at a time, building format-specific expertise progressively
- Merge LoRA to base model after each stage
- TOML learning comes from other formats (Person T discovery)

### Completed Training Stages

| Stage | Formats | Samples | Target |
|-------|---------|---------|--------|
| Stage 1 | JSON, CSV | 800 | 100% parse success |
| Stage 2 | YAML | 500 | 100% parse success |
| Stage 3 | XML | 500 | 95%+ parse success |
| Stage 4 | Mixed (all formats) | 1000 | TOML 90%+, overall optimization |

### Stage 4 (Final) Details

- **Base Model**: `kmd2525/v8_stage3_xml-merged` (Stage 3 merged model)
- **Dataset**: 1000 samples, balanced across all 5 formats (200 each)
- **Goal**: Final tuning for TOML and overall balance

### Training Parameters

- MAX_SEQ_LEN: 1024
- EPOCHS: 2
- Learning Rate: 2e-05 (lower for final stage)
- LoRA R: 64, Alpha: 128

### Expected Performance

| Format | Target |
|--------|--------|
| JSON | 100% |
| CSV | 100% |
| YAML | 100% |
| XML | 95%+ |
| TOML | 90%+ |

**Target LB Score: 0.8+**

### Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("kmd2525/v8_final_model")
tokenizer = AutoTokenizer.from_pretrained("kmd2525/v8_final_model")

# Example inference
messages = [
    {"role": "user", "content": "Convert the following to JSON: name=Alice, age=30"}
]
input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

### Acknowledgments

This model was developed based on insights from the StructEval competition community,
particularly Person U's sequential format learning approach and Person T's discovery about TOML learning.