Instructions to use AgenticFinLab/PyFi-QwenVL-7B-COT-47K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AgenticFinLab/PyFi-QwenVL-7B-COT-47K with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("/home/yuxuanzhao/LLaMA-Factory/models/qwen2_5-vl-7B-Instruct")
model = PeftModel.from_pretrained(base_model, "AgenticFinLab/PyFi-QwenVL-7B-COT-47K")

Transformers

How to use AgenticFinLab/PyFi-QwenVL-7B-COT-47K with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="AgenticFinLab/PyFi-QwenVL-7B-COT-47K")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("AgenticFinLab/PyFi-QwenVL-7B-COT-47K", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AgenticFinLab/PyFi-QwenVL-7B-COT-47K with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AgenticFinLab/PyFi-QwenVL-7B-COT-47K"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AgenticFinLab/PyFi-QwenVL-7B-COT-47K",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AgenticFinLab/PyFi-QwenVL-7B-COT-47K

SGLang

How to use AgenticFinLab/PyFi-QwenVL-7B-COT-47K with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "AgenticFinLab/PyFi-QwenVL-7B-COT-47K" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AgenticFinLab/PyFi-QwenVL-7B-COT-47K",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "AgenticFinLab/PyFi-QwenVL-7B-COT-47K" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AgenticFinLab/PyFi-QwenVL-7B-COT-47K",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AgenticFinLab/PyFi-QwenVL-7B-COT-47K with Docker Model Runner:
```
docker model run hf.co/AgenticFinLab/PyFi-QwenVL-7B-COT-47K
```

PyFi-QwenVL-7B-COT-47K / README.md

nielsr HF Staff

Improve model card: add metadata, paper link, and description

8904da1 verified about 2 months ago

2.75 kB

base_model: Qwen/Qwen2.5-VL-7B-Instruct
library_name: peft
pipeline_tag: image-text-to-text
license: apache-2.0
tags:
  - base_model:adapter:Qwen/Qwen2.5-VL-7B-Instruct
  - llama-factory
  - lora
  - transformers
  - financial
  - vlm

PyFi-QwenVL-7B-47K

This repository contains a fine-tuned LoRA adapter for Qwen2.5-VL-7B-Instruct optimized for hierarchical financial image understanding. It was introduced in the paper PyFi: Toward Pyramid-like Financial Image Understanding for VLMs via Adversarial Agents.

Model Details

Developed by: Yuqun Zhang, Yuxuan Zhao, Sijia Chen (AgenticFin Lab)
Model Type: Vision-Language Model (VLM) Adapter
Base Model: Qwen2.5-VL-7B-Instruct
Language(s): English
License: Apache 2.0
Resources:

Model Description

PyFi (Pyramid-like Financial Image Understanding) is a framework designed to enhance Vision-Language Models in understanding complex financial images (e.g., stock charts, financial reports) through a pyramid-like reasoning structure. This model allows VLMs to reason through question chains in a progressive, simple-to-complex manner.

This specific checkpoint is fine-tuned on approximately 47,000 reasoning chains from the PyFi-600K dataset. It is trained to handle six hierarchical capability levels:

Perception: Basic visual understanding.
Data Extraction: Foundational information retrieval.
Calculation Analysis: Numerical analysis tasks.
Pattern Recognition: Identifying trends and patterns.
Logical Reasoning: Complex logical analysis.
Decision Support: Strategic decision-making assistance.

Training Details

The model was fine-tuned using Parameter-Efficient Fine-Tuning (LoRA) with full-module adaptation.

Training Data: ~47K sample chains from PyFi-600K.
Optimizer: AdamW
Learning Rate: 1.0e-4
Learning Rate Schedule: Cosine scheduling with a warmup ratio of 0.1
Epochs: 1
Effective Batch Size: 8
Hardware: 4x NVIDIA RTX 5090 GPUs

Citation

If you find PyFi useful in your research, please cite:

@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}
}