110 lines
4.2 KiB
Markdown
110 lines
4.2 KiB
Markdown
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---
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license: apache-2.0
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tags:
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- text-generation
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- finance
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- economics
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- cpa
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- audcor
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datasets:
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- Josephgflowers/Finance-Instruct-500k
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language:
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- en
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base_model:
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- unsloth/Qwen3-8B-unsloth-bnb-4bit
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pipeline_tag: text-generation
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library_name: transformers
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---
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# Model Card for CPA-Qwen3-8B-v0
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**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.
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## About AudCor
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**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.
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## Key Features
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* **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.
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* **Domain Specificity:** Optimized for complex financial queries, including audit risk assessment, tax planning strategies, and regulatory interpretation.
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* **Exam-Grade Reasoning:** Capable of handling the rigorous logic required for CPA exam-level problems and real-world accounting scenarios.
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## CPA Exam Insights
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Understanding the standards against which this model is benchmarked requires looking at the rigorous CPA Exam scoring process.
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### The Magic Number: 75
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The passing score or "Gold Standard" for the CPA Exam is **75** on a scale of 0-99.
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* **Crucial Distinction:** This is a *scaled score*, not a percentage correct.
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* **No Curve:** Candidates are measured against a standard of competence, not against each other.
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### Scoring Mechanics: Multi-Stage Testing (MST)
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The CPA Exam uses an adaptive testing model for Multiple-Choice Questions (MCQs):
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1. **Testlet 1:** Always "Medium" difficulty.
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2. **Performance Trigger:**
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* *Strong Performance* $\rightarrow$ **Testlet 2: Difficult** (Higher scoring potential).
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* *Weak Performance* $\rightarrow$ **Testlet 2: Medium** (Lower scoring potential).
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> [!NOTE]
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> Getting a "Difficult" second testlet is desirable because difficult questions are weighted more heavily, raising your potential score ceiling.
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### Section Scoring Breakdown (2025 Update)
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With the CPA Evolution changes, the scoring weights have shifted:
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| Section Type | MCQ Weight | TBS (Task-Based Simulations) Weight |
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| :--- | :--- | :--- |
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| **Core Sections (AUD, FAR, REG)** | 50% | 50% |
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| **Discipline Sections** | 50% | 50% |
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*Data Source: [JobSeekerTools - CPA Passing Score](https://jobseekertools.com/blog/cpa-passing-score)*
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## Usage
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This model is compatible with `transformers` and `unsloth`.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "AudCor/cpa-qwen3-8b-v0"
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# Load the model
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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# Specialized System Prompt
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system_prompt = "You are an expert Certified Public Accountant (CPA). Your goal is to provide accurate, professional, and compliant financial advice..."
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": "Explain the revenue recognition principle under ASC 606."},
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]
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inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to("cuda")
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outputs = model.generate(inputs, max_new_tokens=256)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Disclaimer
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**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.
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## Citation
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```bibtex
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@misc{AudCor/cpa-qwen3-8b-v0,
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author = {AudCor},
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title = {AudCor/cpa-qwen3-8b-v0: A specialized CPA large language model fine-tuned on Josephgflowers/Finance-Instruct-500k},
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year = {2025},
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publisher = {Hugging Face},
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journal = {Hugging Face Model Hub},
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howpublished = {\url{https://huggingface.co/AudCor/cpa-qwen3-8b-v0}}
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}
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```
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