from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sweatSmile/Qwen3-4B-Instruct-FinanceQA")
model = AutoModelForCausalLM.from_pretrained("sweatSmile/Qwen3-4B-Instruct-FinanceQA")

# Example usage
prompt = "Context: ARCOTECH Company Name: Arcotech Ltd.\nQuestion: What is the equity share capital?\nAnswer:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Data

Base model: Qwen3-4B-Instruct
Dataset: FinanceQA (3.7k samples)
Final training loss: 0.0652
Training epochs: 2
Parameters: 16.5M trainable (LoRA/QLoRA)

The model was trained on financial company data including:

Equity share capital queries
Shareholders' funds information
Financial ratios and metrics
Company-specific financial data

Limitations

Trained specifically on financial QA format
May not perform well on general conversation
Should be used for financial information retrieval only

License

Apache 2.0