minhduc168
/

Qwen3-VL-2B-Instruct-unsloth-bnb-4bit-Vietnamese-LoRA

@@ -1,210 +1,130 @@
 ---
-base_model: unsloth/Qwen3-VL-2B-Instruct-unsloth-bnb-4bit
-library_name: peft
-pipeline_tag: text-generation
 tags:
-- base_model:adapter:unsloth/Qwen3-VL-2B-Instruct-unsloth-bnb-4bit
 - lora
-- sft
-- transformers
-- trl
-- unsloth
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]
-### Framework versions
-- PEFT 0.18.1

 ---
+language:
+- vi
 tags:
+- vision-language
+- qwen
+- vlm
 - lora
+- adapter
+- peft
+license: apache-2.0
+datasets:
+- minhduc168/dataset-qwen-vlm-extract-bill
+base_model:
+- unsloth/Qwen3-VL-2B-Instruct-bnb-4bit
+pipeline_tag: image-to-text
 ---
+# Qwen3-VL-2B-Instruct Vietnamese (LoRA Adapter)
+Đây là **LoRA Adapter** được fine-tune để **trích xuất thông tin từ hóa đơn, phiếu thu và đơn thuốc tiếng Việt**.
+Adapter được huấn luyện dựa trên mô hình gốc **[unsloth/Qwen3-VL-2B-Instruct-bnb-4bit](https://huggingface.co/unsloth/Qwen3-VL-2B-Instruct-bnb-4bit)** nhằm tối ưu khả năng hiểu tài liệu và trả về dữ liệu có cấu trúc.
+---
+## 📌 Thông tin mô hình
+- **Loại mô hình:** LoRA (Low-Rank Adaptation)
+- **Pipeline:** Image-to-Text
+- **Mục đích:** Trích xuất dữ liệu có cấu trúc (JSON) từ hình ảnh tài liệu y tế và tài chính.
+- **Ngôn ngữ:** Tối ưu cho **tiếng Việt**
+- **Dataset huấn luyện:** [minhduc168/dataset-qwen-vlm-extract-bill](https://huggingface.co/datasets/minhduc168/dataset-qwen-vlm-extract-bill)
+---
+## 🚀 Ưu điểm của LoRA
+- ✅ **Kích thước nhỏ** — chỉ vài trăm MB thay vì vài GB
+- ✅ **Load nhanh**, giảm yêu cầu VRAM
+- ✅ Không làm thay đổi trọng số base model
+- ✅ Dễ dàng tiếp tục fine-tune trên dataset riêng
+- ✅ Phù hợp cho production hoặc triển khai on-premise
+---
+## 🔧 Hướng dẫn sử dụng
+Để sử dụng Adapter này, bạn cần tải **base model** trước, sau đó nạp LoRA bằng thư viện `peft`.
+```python
+from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
+from peft import PeftModel
+import torch
+base_model_id = "unsloth/Qwen3-VL-2B-Instruct-bnb-4bit"
+adapter_id = "minhduc168/Qwen3-VL-2B-Instruct-Vietnamese-LoRA"
+# 1️⃣ Load base model
+model = Qwen2VLForConditionalGeneration.from_pretrained(
+    base_model_id,
+    device_map="auto"
+)
+# 2️⃣ Load LoRA adapter
+model = PeftModel.from_pretrained(model, adapter_id)
+# 3️⃣ Load processor
+processor = AutoProcessor.from_pretrained(base_model_id)
+model.eval()
+print("Model và LoRA adapter đã sẵn sàng!")
+```
+## ⚠️ Lưu ý quan trọng
+- **Bắt buộc phải tải đúng Base Model** để adapter hoạt động.
+### Cài đặt thư viện cần thiết:
+```bash
+pip install peft transformers bitsandbytes
+```
+## 📊 Dataset
+Model được huấn luyện trên:**[minhduc168/dataset-qwen-vlm-extract-bill](https://huggingface.co/datasets/minhduc168/dataset-qwen-vlm-extract-bill)**
+**Bao gồm:**
+- Hóa đơn bán lẻ
+- Phiếu thu
+- Đơn thuốc
+- Chứng từ tiếng Việt
+Định dạng **instruction-following** giúp model tạo ra kết quả JSON chính xác và ổn định hơn.
+---
+## 🎯 Use Cases
+- Trích xuất thông tin hóa đơn tự động
+- Structured OCR
+- Document AI tiếng Việt
+- Medical / pharmacy bill parsing
+- Fintech document processing
+---
+## 📌 Khi nào nên dùng LoRA này?
+👉 **Khi bạn muốn:**
+- Giảm chi phí GPU
+- Tăng tốc inference
+- Tùy chỉnh model theo domain tiếng Việt
+- Triển khai linh hoạt mà không cần merge model
+---
+## 🔗 Phiên bản khác
+Nếu bạn muốn sử dụng phiên bản **đã merge trọng số** hoặc **GGUF để chạy local**, tham khảo tại:[minhduc168/Qwen3-VL-2B-Instruct-Vietnamese](https://huggingface.co/minhduc168/Qwen3-VL-2B-Instruct-Vietnamese)
+---
+## License
+Apache-2.0
+---
+## 💬 Liên hệ
+Nếu có câu hỏi về dataset hoặc quá trình training, vui lòng mở **Discussion** tại repository này!