V10LabsAI
/

diffu-0.2-adapter

@@ -1,207 +1,66 @@
 ---
-base_model: Qwen/Qwen3-VL-4B-Instruct
 library_name: peft
-pipeline_tag: text-generation
 tags:
-- base_model:adapter:Qwen/Qwen3-VL-4B-Instruct
 - lora
-- transformers
 ---
-# 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:
+- es
+license: apache-2.0
 library_name: peft
+base_model: Qwen/Qwen3-VL-4B-Instruct
 tags:
+- invoice-extraction
+- ocr
+- spanish
 - lora
+- vision
+- finance
+pipeline_tag: image-to-text
 ---
+# diffu-0.2 — Spanish Invoice Data Extractor (Vision)
+**diffu-0.2** is a fine-tuned vision-language model for structured data extraction from Spanish invoice images. Built by [V10 Labs](https://v10labs.com), it extracts supplier details, tax IDs, amounts, and dates from invoice photographs and scans.
+## Performance
+| Model | Accuracy | Type |
+|-------|----------|------|
+| **diffu-0.2 (this model)** | **93.39%** | Fine-tuned, vision |
+| diffu-0.1 (V10 Labs) | 92.82% | Fine-tuned, text-only |
+| Claude Sonnet 4.6 | 61.6% | Generalist, zero-shot |
+| Qwen3-VL-4B (base) | 54.4% | Generalist, zero-shot |
+### Per-Field Accuracy
+| Field | Accuracy |
+|-------|----------|
+| supplier | 92.06% |
+| supplier_cif | 94.12% |
+| invoice_number | 91.35% |
+| date | 95.33% |
+| subtotal | 92.06% |
+| tax_total | 89.25% |
+| total | 92.99% |
+| doc_type | 100.00% |
+## Model Details
+- **Base model**: Qwen/Qwen3-VL-4B-Instruct
+- **Method**: LoRA (r=64, alpha=128)
+- **Target modules**: q_proj, v_proj, k_proj, o_proj, gate_proj, up_proj, down_proj
+- **Training**: 2 epochs, LR=1e-4, effective batch size 16
+- **Image resolution**: 256-1280 × 28 × 28 pixels
+- **Adapter size**: 504 MB
+- **Peak VRAM**: 22.57 GB (training), ~10 GB (inference)
+- **Parse failures**: 0%
+## Output Format
+## Usage
+## About V10 Labs
+V10 Labs builds AI-powered financial intelligence for SMBs in Spain. We train purpose-built models that outperform general-purpose LLMs on domain-specific tasks like invoice processing, accounting classification, and financial analysis.
+[v10labs.com](https://v10labs.com)