qwen3-4b-structeval-strategy7-baseline-yamlxml-lr6e-6

This repository provides a LoRA adapter fine-tuned from Qwen/Qwen3-4B-Instruct-2507 using QLoRA (4-bit, Unsloth).

Training Objective

This adapter is trained to improve structured output accuracy (JSON / YAML / XML / TOML / CSV).

Strategy 7: Baseline Return + YAML/XML Pure Increase 🔥

Training Configuration

• Base model: Qwen/Qwen3-4B-Instruct-2507
• Method: QLoRA (4-bit)
• Max sequence length: 1024
• Epochs: 1
• Learning rate: 6e-06
• LoRA: r=16, alpha=32

Dataset: Baseline Return + YAML/XML Boost

Strategy 7: Reverting to Baseline's TOML 100% Success

Baseline Success (0.80195):

• TOML: 100% ✅
• YAML: 91.4%
• XML: 78.0%
• TOML ratio: 14%

Problem with Strategy 2 Revised (0.82286):

• TOML: 76.0% ❌ (down from 100%)
• YAML: 97.1% ✅
• XML: 90.0% ✅
• TOML ratio: 10%

Strategy 3 & 4 Failure:

• TOML ratio 14% did NOT reproduce Baseline's TOML 100%
• Something else was missing

Strategy 7 Solution:

1. Revert to Baseline's daichira level

• daichira系: 1.0x (Baseline level, no boost)
• Avoid daichira reduction that harmed TOML

2. Pure YAML/XML increase

• u-10bei v2/v4/v5: 2.0x boost (YAML-rich)
• u-10bei base512/base: 1.3x boost (TOML-rich)
• u-10bei v2_short: 1.3x boost (TOML-rich)

3. Maintain TOML 14% ratio

• TOML absolute quantity: +30% (2,800 → 3,640)
• TOML ratio: 14% (same as Baseline)
• Expected TOML recovery: 95-98%

Data Cleaning Pipeline:

1. CoT tags removal: <thinking>...</thinking> completely removed
2. Code fence removal: yaml, json, xml, toml, ````csv removed
3. Leading phrase removal: "Here's the output:", "Sure!" etc. removed
4. 🔥 Output extraction: For u-10bei datasets, extract only content after "Output:" marker
5. Format validation: JSON/YAML/XML/TOML/CSV parsing validation
6. Deduplication: Exact duplicates removed

Format Distribution (Expected):

• YAML: ~13,000 (50%) 🔥 (up from 35% in Baseline)
• XML: ~5,200 (20%) 🔥 (up from 18% in Baseline)
• TOML: ~3,640 (14%) 🔥 (same ratio as Baseline, +30% absolute)
• JSON: ~1,560 (6%)
• CSV: ~2,600 (10%)

Total: ~26,000 samples

Source Datasets with Boost Factors:

daichira系（1.0x - Baseline level）:

• daichira/structured-3k-mix-sft 🔥 1.0x (TOML 0%)
• daichira/structured-5k-mix-sft 🔥 1.0x (TOML 0%)
• daichira/structured-hard-sft-4k 🔥 1.0x (TOML 0%)

u-10bei系（YAML/XML強化）:

• u-10bei/structured_data_with_cot_dataset_512_v2 🔥 2.0x (YAML-rich)
• u-10bei/structured_data_with_cot_dataset_512_v4 🔥 2.0x (YAML-rich)
• u-10bei/structured_data_with_cot_dataset_512_v5 🔥 2.0x (YAML-rich)
• u-10bei/structured_data_with_cot_dataset_512 🔥 1.3x (TOML 20%)
• u-10bei/structured_data_with_cot_dataset 🔥 1.3x (TOML 17.6%)
• u-10bei/structured_data_with_cot_dataset_v2 🔥 1.3x (TOML 15.9%)

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

base = "Qwen/Qwen3-4B-Instruct-2507"
adapter = "yuk1chan/qwen3-4b-structeval-strategy7-baseline-yamlxml-lr6e-6"

tokenizer = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(
    base,
    torch_dtype=torch.float16,
    device_map="auto",
)
model = PeftModel.from_pretrained(model, adapter)

# Inference
prompt = "Generate YAML code for..."
# ... your inference code

Training Results

• Training Loss: ~1.25-1.30
• Validation Loss: ~1.50-1.55
• Training Time: ~9-10 hours (T4 GPU)
• Expected Score: 0.840-0.860

Strategy: Baseline Return + YAML/XML Pure Increase

Key Insights:

Baseline's TOML 100% Success:

• TOML ratio: 14%
• TOML score: 100%
• This is the only time TOML achieved 100%

Why Strategy 2/3/4 Failed:

• Strategy 2: TOML ratio 10% → TOML 76% (ratio too low)
• Strategy 3: TOML ratio 14-15% → TOML ~70% (something else was wrong)
• Strategy 4: TOML ratio 11-12% → TOML 0 samples (daichira reduction failed)

Strategy 7 Design:

Aspect	Baseline	Strategy 2	Strategy 7
daichira boost	1.0x	2.0x	1.0x
TOML ratio	14%	10%	14%
TOML absolute	2,800	2,600	3,640
TOML score	100%	76%	95-98%
YAML ratio	35%	45%	50%
YAML score	91.4%	97.1%	95-97%
Overall	0.80195	0.82286	0.840-0.860

Expected Improvements:

• TOML recovery: 76% → 95-98% (+19-22%)
• YAML maintained: 97.1% → 95-97%
• XML: 90.0% → 88-90%
• Overall: 0.82286 → 0.840-0.860 (+1.7-3.7%)

Risk Analysis:

• Risk: Baseline's TOML 100% may not be reproducible
• Mitigation: Maintain TOML 14% ratio, daichira 1.0x
• Expected: High chance of TOML recovery

License

Apache 2.0

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Trained on Baseline Return + YAML/XML Boost StructEval dataset Learning Rate: 6e-6 (proven setting from 0.82286) Strategy: daichira 1.0x (Baseline) + u-10bei 1.3-2.0x (YAML/XML boost) TOML ratio: 14% (same as Baseline)