Update model card with metadata, paper link and training details

README.md

@@ -1,208 +1,63 @@
 ---
 base_model: Qwen/Qwen2.5-VL-3B-Instruct
 library_name: peft
-pipeline_tag: text-generation
+pipeline_tag: image-text-to-text
+license: apache-2.0
 tags:
 - base_model:adapter:Qwen/Qwen2.5-VL-3B-Instruct
 - llama-factory
 - lora
 - transformers
+- finance
+- vision-language
 ---
 
-# Model Card for Model ID
+# PyFi-QwenVL-3B-47K
 
-<!-- Provide a quick summary of what the model is/does. -->
+This model is a parameter-efficient fine-tuned version (LoRA) of [Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct) specialized for financial image understanding. It was introduced as part of the **PyFi** framework.
 
+- **Paper:** [PyFi: Toward Pyramid-like Financial Image Understanding for VLMs via Adversarial Agents](https://arxiv.org/abs/2512.14735)
+- **Repository:** [https://github.com/AgenticFinLab/PyFi](https://github.com/AgenticFinLab/PyFi)
+- **Dataset:** [PyFi-600K](https://huggingface.co/datasets/AgenticFinLab/PyFi-600K)
 
+## Model Description
 
-## Model Details
+PyFi (Pyramid-like Financial Image Understanding) is a framework designed to enable Vision Language Models (VLMs) to reason through financial images—such as stock charts, financial reports, and economic diagrams—in a progressive, simple-to-complex manner. 
 
-### Model Description
+This specific checkpoint is the 3B variant fine-tuned on approximately 47,000 reasoning chains. This version was trained **without Chain-of-Thought (CoT)**, focusing on the model's ability to provide the final answer in the financial reasoning pyramid.
 
-<!-- Provide a longer summary of what this model is. -->
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-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
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-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
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-### Model Sources [optional]
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-<!-- Provide the basic links for the model. -->
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-- **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. -->
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-[More Information Needed]
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-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-[More Information Needed]
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-### Out-of-Scope Use
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-[More Information Needed]
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-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-[More Information Needed]
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-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-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]
+The model is designed to handle tasks across six hierarchical capability levels:
+1. **Perception**: Basic visual understanding.
+2. **Data Extraction**: Information retrieval from charts and tables.
+3. **Calculation Analysis**: Numerical analysis tasks.
+4. **Pattern Recognition**: Identifying trends and patterns.
+5. **Logical Reasoning**: Complex logical analysis.
+6. **Decision Support**: Strategic decision-making assistance.
 
 ## 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. -->
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-[More Information Needed]
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-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
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-[More Information Needed]
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-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-[More Information Needed]
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-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
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-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
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-[More Information Needed]
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-[More Information Needed]
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-#### Metrics
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-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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-[More Information Needed]
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-### Results
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-[More Information Needed]
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-#### Summary
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-
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
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-## 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
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-[More Information Needed]
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-### Compute Infrastructure
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-[More Information Needed]
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-#### Hardware
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-[More Information Needed]
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-#### Software
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-[More Information Needed]
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-## 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. -->
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-**BibTeX:**
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-[More Information Needed]
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-**APA:**
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-[More Information Needed]
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-## 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]
+- **Finetuning approach:** LoRA (Parameter-Efficient Fine-Tuning) with full-module adaptation.
+- **Training Data:** 47K sample chains from the PyFi-600K dataset.
+- **Optimizer:** AdamW
+- **Learning Rate:** $1.0 \times 10^{-4}$
+- **Learning Rate Schedule:** Cosine scheduling with a warmup ratio of 0.1.
+- **Training Epochs:** 1
+- **Effective Batch Size:** 8
+- **Hardware:** 4x NVIDIA RTX 5090 GPUs.
 
-## Model Card Authors [optional]
+## Evaluation Results
 
-[More Information Needed]
+In the PyFi benchmark, fine-tuning on pyramid-structured question chains showed significant improvements. The PyFi models (when using CoT) yielded average accuracy improvements of 19.52% for the 3B variant over baseline pre-trained models.
 
-## Model Card Contact
+## Citation
 
-[More Information Needed]
-### Framework versions
+If you use PyFi in your research, please cite:
 
-- PEFT 0.17.1
+```bibtex
+@article{pyfi2025,
+  title={PyFi: Toward Pyramid-like Financial Image Understanding for VLMs via Adversarial Agents},
+  author={Zhang, Yuqun and Zhao, Yuxuan and Chen, Sijia},
+  journal={arXiv preprint arXiv:2512.14735},
+  year={2025}
+}
+```