---
license: apache-2.0
tags:
- text-generation
- finance
- economics
- cpa
- audcor
datasets:
- Josephgflowers/Finance-Instruct-500k
language:
- en
base_model:
- unsloth/Qwen3-8B-unsloth-bnb-4bit
pipeline_tag: text-generation
library_name: transformers
---

# Model Card for CPA-Qwen3-8B-v0

**CPA-Qwen3-8B-v0** is a specialized large language model developed by **[AudCor](https://huggingface.co/AudCor)**, designed specifically to assist with Certified Public Accountant (CPA) tasks, regulatory compliance, and financial reasoning.

## About AudCor
**AudCor** is dedicated to building advanced AI tools tailored for CPA firms. Our mission is to enhance the productivity and accuracy of accounting professionals by integrating state-of-the-art language models with deep domain expertise in auditing, tax, and financial reporting.

## Key Features

*   **CPA Expert Persona:** Fine-tuned to adopt the role of a seasoned CPA, prioritizing accuracy, professional skepticism, and strict adherence to GAAP, IFRS, and tax codes.
*   **Domain Specificity:** Optimized for complex financial queries, including audit risk assessment, tax planning strategies, and regulatory interpretation.
*   **Exam-Grade Reasoning:** Capable of handling the rigorous logic required for CPA exam-level problems and real-world accounting scenarios.

## CPA Exam Insights

Understanding the standards against which this model is benchmarked requires looking at the rigorous CPA Exam scoring process.

![CPA Pass Rates 2025](https://jobseekertools.com/images/blog/cpa-pass-rates-2025.png)


### The Magic Number: 75
The passing score or "Gold Standard" for the CPA Exam is **75** on a scale of 0-99.
*   **Crucial Distinction:** This is a *scaled score*, not a percentage correct.
*   **No Curve:** Candidates are measured against a standard of competence, not against each other.

### Scoring Mechanics: Multi-Stage Testing (MST)
The CPA Exam uses an adaptive testing model for Multiple-Choice Questions (MCQs):

1.  **Testlet 1:** Always "Medium" difficulty.
2.  **Performance Trigger:**
    *   *Strong Performance* $\rightarrow$ **Testlet 2: Difficult** (Higher scoring potential).
    *   *Weak Performance* $\rightarrow$ **Testlet 2: Medium** (Lower scoring potential).

> [!NOTE]
> Getting a "Difficult" second testlet is desirable because difficult questions are weighted more heavily, raising your potential score ceiling.

### Section Scoring Breakdown (2025 Update)
With the CPA Evolution changes, the scoring weights have shifted:

| Section Type | MCQ Weight | TBS (Task-Based Simulations) Weight |
| :--- | :--- | :--- |
| **Core Sections (AUD, FAR, REG)** | 50% | 50% |
| **Discipline Sections** | 50% | 50% |

*Data Source: [JobSeekerTools - CPA Passing Score](https://jobseekertools.com/blog/cpa-passing-score)*

## Usage

This model is compatible with `transformers` and `unsloth`.

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "AudCor/cpa-qwen3-8b-v0"

# Load the model
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Specialized System Prompt
system_prompt = "You are an expert Certified Public Accountant (CPA). Your goal is to provide accurate, professional, and compliant financial advice..."

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": "Explain the revenue recognition principle under ASC 606."},
]

inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to("cuda")
outputs = model.generate(inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Disclaimer

**Not Financial Advice.** This model is an artificial intelligence research tool. While trained on high-quality financial data, it is not a licensed CPA and should not be used as a substitute for professional accounting, tax, or legal advice.

## Citation

```bibtex
@misc{AudCor/cpa-qwen3-8b-v0,
  author    = {AudCor},
  title     = {AudCor/cpa-qwen3-8b-v0: A specialized CPA large language model fine-tuned on Josephgflowers/Finance-Instruct-500k},
  year      = {2025},
  publisher = {Hugging Face},
  journal   = {Hugging Face Model Hub},
  howpublished = {\url{https://huggingface.co/AudCor/cpa-qwen3-8b-v0}}
}
```