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

Libraries

How to use AgenticFinLab/PyFi-QwenVL-7B-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-47K")

Transformers

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

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

pipe = pipeline("text-generation", model="AgenticFinLab/PyFi-QwenVL-7B-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-47K", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use AgenticFinLab/PyFi-QwenVL-7B-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-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-47K",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use AgenticFinLab/PyFi-QwenVL-7B-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-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-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-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-47K",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

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

nielsr HF Staff

Improve model card and link to paper

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	---
	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
	- finance
	- financial-vlm
	---

	# PyFi-QwenVL-7B-47K

	This repository contains a LoRA adapter for [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) fine-tuned using the PyFi framework for advanced financial image understanding.

	## Model Details

	- Developed by: AgenticFin Lab
	- Model type: Vision-Language Model (VLM) LoRA Adapter
	- Base model: [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct)
	- Finetuning variant: Trained on ~47K sample chains without Chain-of-Thought (CoT)
	- License: Apache 2.0
	- Paper: [PyFi: Toward Pyramid-like Financial Image Understanding for VLMs via Adversarial Agents](https://huggingface.co/papers/2512.14735)
	- Repository: [https://github.com/AgenticFinLab/PyFi](https://github.com/AgenticFinLab/PyFi)

	## Description

	PyFi (Pyramid-like Financial Image Understanding) is a framework designed to enhance VLMs in understanding complex financial images through adversarial agents. The framework uses PyFi-600K, a dataset organized into a reasoning pyramid across 6 capability levels:
	1. Perception: Basic visual understanding.
	2. Data Extraction: Foundational information retrieval.
	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.

	This specific model variant is fine-tuned on the final question-answer pairs of the reasoning chains, rather than the intermediate steps.

	## Usage Examples

	Below are examples of how to use the PyFi framework for financial analysis, as provided in the official repository.

	### MCTS Tree Construction for Financial Analysis

	```python
	from fttracer.mcts.gqa import ImageQASystem

	# Initialize the Image QA system
	system = ImageQASystem()

	# Analyze a financial report image with MCTS tree
	report_path = "examples/financial_report.png"

	# Run MCTS tree construction for comprehensive analysis
	system.main(
	image_path=report_path,
	context_base_path="examples/context/"
	)
	```

	### Endgame QA Generation for Financial Images

	```python
	from fttracer.mcts.gqa import ImageQASystem

	# Initialize the Image QA system
	system = ImageQASystem()

	# Analyze a stock chart and generate endgame QA
	stock_chart_path = "examples/stock_chart.png"

	# Generate endgame QA focused on final analysis
	system.main_gfa(
	image_path=stock_chart_path,
	context_base_path="examples/context/"
	)
	```

	## Evaluation Results

	Fine-tuning Qwen2.5-VL models on the pyramid-structured question chains enables these models to answer complex financial questions more effectively. According to the paper, the PyFi models show significant accuracy improvements, especially in high-level reasoning and decision support tasks compared to their base models.

	## Citation

	If you use PyFi in your research, please cite the following paper:

	```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}
	}
	```