Instructions to use AudCor/cpa-qwen3-8b-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AudCor/cpa-qwen3-8b-v0 with Transformers:

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

pipe = pipeline("text-generation", model="AudCor/cpa-qwen3-8b-v0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AudCor/cpa-qwen3-8b-v0")
model = AutoModelForCausalLM.from_pretrained("AudCor/cpa-qwen3-8b-v0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AudCor/cpa-qwen3-8b-v0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AudCor/cpa-qwen3-8b-v0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AudCor/cpa-qwen3-8b-v0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AudCor/cpa-qwen3-8b-v0

SGLang

How to use AudCor/cpa-qwen3-8b-v0 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 "AudCor/cpa-qwen3-8b-v0" \
    --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": "AudCor/cpa-qwen3-8b-v0",
		"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 "AudCor/cpa-qwen3-8b-v0" \
        --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": "AudCor/cpa-qwen3-8b-v0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AudCor/cpa-qwen3-8b-v0 with Docker Model Runner:
```
docker model run hf.co/AudCor/cpa-qwen3-8b-v0
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for CPA-Qwen3-8B-v0

CPA-Qwen3-8B-v0 is a specialized large language model developed by 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.

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):

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

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

Usage

This model is compatible with transformers and unsloth.

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

@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}}
}

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8B params

Tensor type

BF16

Model tree for AudCor/cpa-qwen3-8b-v0

Base model

Qwen/Qwen3-8B-Base

Finetuned

Qwen/Qwen3-8B

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cpa-qwen3-8b-v0