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README.md

@@ -1,199 +1,269 @@
 ---
-library_name: transformers
-tags: []
+base_model: Qwen/Qwen2.5-VL-7B-Instruct
+datasets:
+- custom-indian-invoice-dataset
+language:
+- en
+- hi
+- ta
+- ml
+- te
+- kn
+- bn
+license: apache-2.0
+pipeline_tag: image-text-to-text
+tags:
+- qwen2.5-vl
+- vision-language-model
+- invoice-extraction
+- document-understanding
+- ocr
+- indian-invoices
+- gst
+- lora
+- peft
+- unsloth
+- fine-tuned
 ---
 
-# 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. -->
-
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **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 -->
+# Qwen2.5-VL 7B — Indian Invoice Extraction
 
-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).
+Fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) specialized for extracting structured JSON from Indian GST invoices (B2B, B2C, export, IRN/ACK, multi-layout). Trained with QLoRA + Unsloth on an NVIDIA A100 80 GB. Merged via PEFT merge_and_unload().
 
-- **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]
+## Available Versions
 
-### Model Architecture and Objective
+| Version | Link | Use case |
+|---|---|---|
+| Merged bfloat16 | [gouri100/Unsloth_Qwen-2.5_7B-Invoice-962](https://huggingface.co/gouri100/Unsloth_Qwen-2.5_7B-Invoice-962) | Full precision inference |
+| GGUF Q4_K_M | [gouri100/Unsloth_Qwen-2.5_7B-Invoice-962-GGUF](https://huggingface.co/gouri100/Unsloth_Qwen-2.5_7B-Invoice-962-GGUF) | llama.cpp / Ollama — light GPU |
+| GGUF Q8_0 | [gouri100/Unsloth_Qwen-2.5_7B-Invoice-962-GGUF](https://huggingface.co/gouri100/Unsloth_Qwen-2.5_7B-Invoice-962-GGUF) | llama.cpp / Ollama — higher quality |
 
-[More Information Needed]
+---
 
-### Compute Infrastructure
+## Model Summary
 
-[More Information Needed]
+| Property | Value |
+|---|---|
+| **Base model** | Qwen/Qwen2.5-VL-7B-Instruct |
+| **Fine-tuning method** | QLoRA (r=64, alpha=128) |
+| **Merge method** | PEFT merge_and_unload() — bfloat16 safetensors |
+| **Framework** | Unsloth + TRL SFTTrainer |
+| **Hardware** | NVIDIA A100 80 GB |
+| **Task** | Invoice image to Structured JSON |
+| **Input types** | JPG, PNG, PDF (page 1 at 200 DPI) |
+| **Languages** | English, Hindi, Tamil, Malayalam, Telugu, Kannada, Bengali |
+| **License** | Apache 2.0 |
 
-#### Hardware
+---
 
-[More Information Needed]
+## Training Dataset
 
-#### Software
+| Property | Value |
+|---|---|
+| **Total samples** | 962 |
+| **File types** | JPG, PNG, PDF |
+| **PDF handling** | Page 1 extracted at 200 DPI, resized to max 1280px |
+| **Invoice types** | B2B GST, B2C, Export, IRN/ACK |
+| **Annotation** | Manually labeled JSON per invoice |
 
-[More Information Needed]
+---
 
-## Citation [optional]
+## Output JSON Schema
+
+```json
+{
+  "metadata": {
+    "invoice_no": "string",
+    "invoice_date": "YYYY-MM-DD",
+    "irn": "string | null",
+    "ack_no": "string | null",
+    "ack_date": "string | null"
+  },
+  "supplier": {
+    "name": "string",
+    "gstin": "string",
+    "address": "string",
+    "state_code": "string"
+  },
+  "buyer": {
+    "name": "string",
+    "gstin": "string",
+    "address": "string",
+    "state_code": "string"
+  },
+  "line_items": [{
+    "sl_no": "number",
+    "description": "string",
+    "hsn_sac": "string",
+    "qty": "number",
+    "unit": "string",
+    "rate": "number",
+    "amount": "number"
+  }],
+  "tax": {
+    "taxable_value": "number",
+    "cgst_rate": "number",
+    "cgst_amount": "number",
+    "sgst_rate": "number",
+    "sgst_amount": "number",
+    "igst_rate": "number",
+    "igst_amount": "number",
+    "total_tax": "number",
+    "grand_total": "number",
+    "round_off": "number"
+  }
+}
+```
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+---
 
-**BibTeX:**
+## Training Configuration
+
+| Hyperparameter | Value |
+|---|---|
+| **Epochs** | 3 |
+| **Learning rate** | 0.0002 |
+| **LR scheduler** | Cosine |
+| **Warmup ratio** | 0.05 |
+| **Per device batch size** | 2 |
+| **Gradient accumulation steps** | 8 |
+| **Effective batch size** | 16 |
+| **Max sequence length** | 2048 |
+| **Precision** | bfloat16 |
+| **LoRA rank (r)** | 64 |
+| **LoRA alpha** | 128 |
+| **LoRA dropout** | 0.05 |
+| **LoRA target modules** | q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
+| **Vision layers fine-tuned** | Yes |
+| **Gradient checkpointing** | Unsloth optimized |
 
-[More Information Needed]
+---
 
-**APA:**
+## Training Results
 
-[More Information Needed]
+| Metric | Value |
+|---|---|
+| **Final training loss** | 0.2594 |
+| **Total steps** | N/A |
+| **Training time** | 2243.16s (37.4 min) |
+| **Steps per second** | 0.082 |
 
-## Glossary [optional]
+---
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+## Inference
+
+### With transformers (merged model)
+
+```python
+from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor
+from PIL import Image
+import torch, json
+
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    "gouri100/Unsloth_Qwen-2.5_7B-Invoice-962",
+    torch_dtype = torch.bfloat16,
+    device_map  = 'auto',
+)
+processor = AutoProcessor.from_pretrained("gouri100/Unsloth_Qwen-2.5_7B-Invoice-962")
+
+image = Image.open('invoice.jpg').convert('RGB')
+
+SYSTEM_PROMPT = (
+    'You are an expert system for extracting structured data from invoices. '
+    'Return ONLY valid JSON. Do NOT include explanations or extra text.'
+)
+
+messages = [
+    {'role': 'system', 'content': [{'type': 'text', 'text': SYSTEM_PROMPT}]},
+    {'role': 'user', 'content': [
+        {'type': 'image', 'image': image},
+        {'type': 'text',  'text': 'Extract structured invoice data as JSON.'}
+    ]}
+]
+
+inputs = processor.apply_chat_template(
+    messages,
+    add_generation_prompt = True,
+    tokenize              = True,
+    return_tensors        = 'pt',
+    return_dict           = True,
+).to(model.device)
+
+with torch.no_grad():
+    output_ids = model.generate(
+        **inputs,
+        max_new_tokens = 1024,
+        temperature    = 0.1,
+        do_sample      = False,
+    )
+
+decoded = processor.decode(
+    output_ids[0][inputs['input_ids'].shape[1]:],
+    skip_special_tokens = True,
+)
+result = json.loads(decoded)
+print(json.dumps(result, indent=2, ensure_ascii=False))
+```
+
+### Load in 4-bit (lighter GPUs)
+
+```python
+from transformers import BitsAndBytesConfig
+
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit              = True,
+    bnb_4bit_compute_dtype    = torch.bfloat16,
+    bnb_4bit_quant_type       = 'nf4',
+    bnb_4bit_use_double_quant = True,
+)
+model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+    "gouri100/Unsloth_Qwen-2.5_7B-Invoice-962",
+    quantization_config = bnb_config,
+    device_map          = 'auto',
+)
+```
+
+### From PDF
+
+```python
+from pdf2image import convert_from_path
+pages = convert_from_path('invoice.pdf', dpi=200)
+image = pages[0]
+# then follow inference code above
+```
+
+### With Ollama (GGUF)
+
+```bash
+ollama run gouri100/Unsloth_Qwen-2.5_7B-Invoice-962-GGUF
+```
 
-[More Information Needed]
+---
 
-## More Information [optional]
+## Limitations
 
-[More Information Needed]
+- Optimized for Indian GST invoice formats — may underperform on foreign layouts
+- Scans below 100 DPI or heavily skewed images reduce accuracy
+- Handwritten invoices are not supported
+- Multi-page invoices: only page 1 was used during training
+- Always validate extracted JSON against your business logic before use
 
-## Model Card Authors [optional]
+---
 
-[More Information Needed]
+## Citation
 
-## Model Card Contact
+```bibtex
+@misc{qwen2.5-vl-7b-indian-invoice,
+  title        = {Qwen2.5-VL-7B Fine-tuned for Indian Invoice Extraction},
+  author       = {Your Name},
+  year         = {2025},
+  publisher    = {HuggingFace},
+  howpublished = {\url{https://huggingface.co/gouri100/Unsloth_Qwen-2.5_7B-Invoice-962}}
+}
+```
 
-[More Information Needed]
+*Fine-tuned with [Unsloth](https://github.com/unslothai/unsloth) · Merged with [PEFT](https://github.com/huggingface/peft) · Trained on NVIDIA A100 80 GB*