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README.md

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+---
+license: apache-2.0
+task_categories:
+- image-to-text
+- visual-question-answering
+tags:
+- vlm
+- unsloth
+- qwen-vl
+- document-ai
+- trade-finance
+- bangladesh-bank
+size_categories:
+- n<1K
+---
+
+# 🏦 Bangladesh Bank Trade Finance IDP — Multi-User Collaborative Fine-Tuning Dataset
+
+This dataset contains human-reviewed, verified, and corrected document extractions for the **8 official Bangladesh Bank regulatory trade-finance document types**.
+
+It is **completely self-contained** and structured for immediate Vision-Language Model (VLM) fine-tuning anytime from any environment (Colab, Kaggle, GPU cluster, or local), with built-in multi-annotator merge support and incremental delta uploads.
+
+---
+
+## 📊 Dataset Structure & Splits
+
+| Split / Subset | Description | Examples |
+|---|---|---|
+| **`train`** | Ready-to-train multi-task VLM conversations (`messages` format) | 264 |
+| **`validation`** | Stratified held-out evaluation conversations for monitoring eval loss | 56 |
+| **`documents`** | Full document-level feedback records with model outputs, human corrections, confidence scores & DPO pairs (subset: `documents`) | 160 |
+
+### 📑 Document Types Represented
+| Document Type | Document Count |
+|---|---|
+| `air_waybill` | 18 |
+| `bill_of_entry` | 22 |
+| `commercial_lca` | 20 |
+| `exp_form` | 19 |
+| `final_invoice` | 23 |
+| `imp_form` | 19 |
+| `industrial_lca` | 20 |
+| `ocean_bl` | 19 |
+
+**Total DPO Preference Pairs Available:** 9 (for Direct Preference Optimization)
+
+---
+
+## 🚀 Quickstart: Train in 10 Lines with Unsloth / TRL
+
+You can fine-tune Qwen3-VL / Qwen2.5-VL directly on this dataset without ANY manual data wrangling:
+
+```python
+from datasets import load_dataset
+from unsloth import FastVisionModel
+from unsloth.trainer import UnslothVisionDataCollator
+from trl import SFTTrainer, SFTConfig
+
+# 1. Load the ready-to-train dataset directly from Hugging Face
+ds = load_dataset("jihadv4/bb-trade-idp-feedback")
+train_data = ds["train"]
+eval_data = ds.get("validation")
+
+# 2. Load model & attach vision LoRA adapters
+model, tokenizer = FastVisionModel.from_pretrained(
+    "unsloth/Qwen3-VL-Max-Instruct",
+    load_in_4bit=True,
+    max_seq_length=2048,
+)
+model = FastVisionModel.get_peft_model(
+    model,
+    finetune_vision_layers=True,
+    finetune_language_layers=True,
+    finetune_attention_modules=True,
+    finetune_mlp_modules=True,
+    r=16,
+    lora_alpha=16,
+    lora_dropout=0.05,
+)
+FastVisionModel.for_training(model)
+
+# 3. Train with SFTTrainer
+trainer = SFTTrainer(
+    model=model,
+    tokenizer=tokenizer,
+    data_collator=UnslothVisionDataCollator(model, tokenizer),
+    train_dataset=train_data,
+    eval_dataset=eval_data,
+    args=SFTConfig(
+        per_device_train_batch_size=2,
+        gradient_accumulation_steps=4,
+        learning_rate=1e-4,
+        num_train_epochs=3,
+        optim="adamw_8bit",
+        output_dir="./lora_checkpoints",
+    ),
+)
+trainer.train()
+```
+
+---
+
+## 📦 Standalone Artifacts in this Repository
+
+- **`images/`**: Content-addressed directory containing every verified document page image (LFS deduplicated).
+- **`images_backup.tar.gz`**: Standalone compressed archive of all page images for raw PyTorch / non-parquet workflows.
+- **`feedback.jsonl`**: Exact raw JSONL feedback log with per-record `batch_id` and `synced_at` watermarks.
+- **`schemas_bundle.json`**: Official Bangladesh Bank JSON schemas, field definitions, and XML templates.
+- **`trade_ontology.json`**: Concept synonym mappings and bilingual hints.
+- **`training_config.json`**: Recommended hyperparameters, target modules, and runtime compatibility settings.

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