Hirojie5310
/

your-lora-repo_v1_1

@@ -1,208 +1,103 @@
 ---
-base_model: unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit
 library_name: peft
 pipeline_tag: text-generation
 tags:
-- base_model:adapter:unsloth/qwen3-4b-instruct-2507-unsloth-bnb-4bit
 - lora
-- transformers
-- unsloth
 ---
-# Model Card for Model ID
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-## Model Details
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-### Framework versions
-- PEFT 0.18.1

 ---
+base_model: Qwen/Qwen3-4B-Instruct-2507
+datasets:
+- Hirojie5310/structured-mix-sft-qwen3
+language:
+- en
+license: apache-2.0
 library_name: peft
 pipeline_tag: text-generation
 tags:
 - lora
+- qlora
+- structured-output
+- json
+- yaml
+- xml
+- cot-mask
+- after-marker
 ---
+# Qwen3-4B Structured Transformation LoRA v1.1 by Hirojie5310 (v1)
+This repository provides a **LoRA adapter** fine-tuned from
+**Qwen3-4B-Instruct-2507** using **QLoRA (4-bit) with Unsloth**.
+This adapter is specialized for **structured data transformation tasks**
+(JSON / YAML / XML / CSV), with strict output formatting.
+This repository contains **LoRA adapter weights only**.
+The base model must be loaded separately.
+## Training Objective
+This LoRA adapter is trained to improve **structured output accuracy**
+for deterministic format conversion tasks such as:
+- JSON ↔ YAML
+- JSON ↔ XML
+- CSV → XML / YAML
+- Schema-constrained structured transformations
+Training is performed with **assistant-only loss**:
+- Loss is applied **only after explicit output markers** (e.g. `Final:` or `Output:`).
+- Intermediate reasoning (Chain-of-Thought) is **fully masked** and not learned.
+This design ensures:
+- No leakage of reasoning text
+- Clean, strictly formatted final outputs
+- Robust generalization to unseen structured schemas
+## Training Configuration
+This version uses the same training data and objective as the previous release,
+with adjusted hyperparameters for improved stability under limited GPU resources.
+- Base model: Qwen3-4B-Instruct-2507
+- Fine-tuning method: QLoRA (4-bit) with Unsloth
+- Max sequence length: 512
+- Epochs: 1
+- Learning rate: 2e-5
+- Weight decay: 0.01
+- Batch size (effective): 16
+- LoRA configuration:
+  - Rank (r): 64
+  - Alpha: 128
+  - Dropout: 0
+## Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import torch
+base_model = "Qwen/Qwen3-4B-Instruct-2507"
+adapter = "Hirojie5310/your-lora-repo-name"
+tokenizer = AutoTokenizer.from_pretrained(base_model)
+model = AutoModelForCausalLM.from_pretrained(
+    base_model,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+model = PeftModel.from_pretrained(model, adapter)
+```
+## Sources & Terms (IMPORTANT)
+Training data is a mixed dataset constructed from the following sources:
+- u-10bei/structured_data_with_cot_dataset_512_v5 (MIT License)
+- daichira/structured-5k-mix-sft
+- daichira/structured-hard-sft-4k
+These datasets are used in compliance with their respective licenses
+and terms of use.
+This repository distributes **LoRA adapter weights only**.
+Users must comply with:
+- Each dataset's original license
+- The base model's original terms of use (Qwen/Qwen3-4B-Instruct-2507)