Spaces:
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Running
| """ | |
| FinanceBench-LLM — Hugging Face Spaces Gradio Demo | |
| ==================================================== | |
| Interactive demo for the domain-adapted financial QA model. | |
| Runs on free HF Spaces CPU using transformers + PEFT. | |
| Tabs: | |
| 1. Ask Finance Question — Query the fine-tuned model | |
| 2. Evaluation Results — Browse comparison charts and metrics | |
| 3. Model Comparison — Side-by-side Base vs ICL vs LoRA | |
| Author: Amit Lal | |
| Built with NVIDIA NIM, NeMo Customizer, and Hugging Face | |
| """ | |
| import json | |
| import os | |
| from pathlib import Path | |
| import gradio as gr | |
| # ============================================================================== | |
| # Configuration | |
| # ============================================================================== | |
| BASE_MODEL_ID = "meta-llama/Llama-3.1-8B-Instruct" | |
| ADAPTER_ID = "amitlal/financebench-lora-adapter" | |
| # Inference backend — toggle based on Space type | |
| USE_LOCAL_MODEL = False # Set True if running on GPU Space | |
| USE_INFERENCE_API = True # Set True for free CPU Space (uses HF Inference API) | |
| # Sample evaluation results (from Notebook 5) | |
| EVAL_RESULTS = { | |
| "Base (Llama-3.1-8B)": { | |
| "Exact Match": 0.23, | |
| "F1 Score": 0.41, | |
| "Faithfulness": "3.2 / 5", | |
| "Correctness": "2.8 / 5", | |
| "Conciseness": "3.5 / 5", | |
| "ELO Rating": 835, | |
| }, | |
| "ICL (5-shot)": { | |
| "Exact Match": 0.34, | |
| "F1 Score": 0.56, | |
| "Faithfulness": "3.9 / 5", | |
| "Correctness": "3.6 / 5", | |
| "Conciseness": "3.8 / 5", | |
| "ELO Rating": 1023, | |
| }, | |
| "LoRA Fine-tuned": { | |
| "Exact Match": 0.52, | |
| "F1 Score": 0.71, | |
| "Faithfulness": "4.4 / 5", | |
| "Correctness": "4.2 / 5", | |
| "Conciseness": "4.1 / 5", | |
| "ELO Rating": 1142, | |
| }, | |
| } | |
| # Sample financial questions for the demo | |
| SAMPLE_QUESTIONS = [ | |
| "What was Apple's total revenue for fiscal year 2023?", | |
| "What was Microsoft's operating income margin in Q4 2023?", | |
| "How did Amazon's AWS revenue change year-over-year in 2023?", | |
| "What was Tesla's gross profit margin for the automotive segment?", | |
| "What percentage of Alphabet's revenue came from advertising in 2023?", | |
| ] | |
| # ============================================================================== | |
| # Load pre-cached comparison data | |
| # ============================================================================== | |
| COMPARISONS_FILE = Path(__file__).parent / "sample_comparisons.json" | |
| CACHED_COMPARISONS = {} | |
| if COMPARISONS_FILE.exists(): | |
| with open(COMPARISONS_FILE) as f: | |
| CACHED_COMPARISONS = json.load(f) | |
| # ============================================================================== | |
| # Model Loading | |
| # ============================================================================== | |
| model = None | |
| tokenizer = None | |
| def load_model(): | |
| """Load the PEFT model for local inference.""" | |
| global model, tokenizer | |
| if not USE_LOCAL_MODEL: | |
| return | |
| try: | |
| import torch | |
| from peft import PeftModel | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| print(f"Loading base model: {BASE_MODEL_ID}") | |
| tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL_ID) | |
| base_model = AutoModelForCausalLM.from_pretrained( | |
| BASE_MODEL_ID, | |
| torch_dtype=torch.float16, | |
| device_map="auto", | |
| ) | |
| print(f"Loading LoRA adapter: {ADAPTER_ID}") | |
| model = PeftModel.from_pretrained(base_model, ADAPTER_ID) | |
| model.eval() | |
| print("Model loaded successfully!") | |
| except Exception as e: | |
| print(f"Model loading failed: {e}") | |
| print("Falling back to HF Inference API") | |
| def generate_response(question: str, context: str = "") -> str: | |
| """Generate a response using the loaded model or HF Inference API.""" | |
| prompt = f"""You are a precise financial analyst specializing in SEC filings. | |
| Answer questions accurately and concisely based on the provided context. | |
| {f'Context: {context}' if context else ''} | |
| Question: {question} | |
| Answer concisely and accurately:""" | |
| # Option 1: Local PEFT model | |
| if USE_LOCAL_MODEL and model is not None: | |
| try: | |
| import torch | |
| inputs = tokenizer(prompt, return_tensors="pt").to(model.device) | |
| with torch.no_grad(): | |
| outputs = model.generate( | |
| **inputs, | |
| max_new_tokens=256, | |
| temperature=0.1, | |
| do_sample=True, | |
| top_p=0.9, | |
| ) | |
| response = tokenizer.decode( | |
| outputs[0][inputs["input_ids"].shape[1]:], | |
| skip_special_tokens=True, | |
| ) | |
| return response.strip() | |
| except Exception as e: | |
| return f"Error during local inference: {e}" | |
| # Option 2: HF Inference API (free) | |
| if USE_INFERENCE_API: | |
| try: | |
| from huggingface_hub import InferenceClient | |
| client = InferenceClient(token=os.environ.get("HF_TOKEN")) | |
| messages = [ | |
| {"role": "system", "content": "You are a precise financial analyst specializing in SEC filings. Answer questions accurately and concisely based on the provided context."}, | |
| {"role": "user", "content": f"{f'Context: {context}' if context else ''}\nQuestion: {question}\n\nAnswer concisely and accurately:"}, | |
| ] | |
| response = client.chat_completion( | |
| messages=messages, | |
| model=BASE_MODEL_ID, | |
| max_tokens=256, | |
| temperature=0.1, | |
| ) | |
| return response.choices[0].message.content.strip() | |
| except Exception as e: | |
| return f"HF Inference API error: {e}. Please set HF_TOKEN environment variable." | |
| return ( | |
| "No inference backend available. " | |
| "Set USE_LOCAL_MODEL=True (GPU Space) or USE_INFERENCE_API=True (free CPU Space)." | |
| ) | |
| # ============================================================================== | |
| # Gradio Interface | |
| # ============================================================================== | |
| def ask_question(question: str, context: str) -> str: | |
| """Handle the Ask Finance Question tab.""" | |
| if not question.strip(): | |
| return "Please enter a financial question." | |
| return generate_response(question, context) | |
| def get_eval_results() -> str: | |
| """Format evaluation results as a markdown table.""" | |
| header = "| Model | Exact Match | F1 Score | Faithfulness | Correctness | Conciseness | ELO |\n" | |
| header += "|-------|-------------|----------|--------------|-------------|-------------|-----|\n" | |
| rows = "" | |
| for model_name, metrics in EVAL_RESULTS.items(): | |
| rows += f"| {model_name} " | |
| rows += f"| {metrics['Exact Match']} " | |
| rows += f"| {metrics['F1 Score']} " | |
| rows += f"| {metrics['Faithfulness']} " | |
| rows += f"| {metrics['Correctness']} " | |
| rows += f"| {metrics['Conciseness']} " | |
| rows += f"| {metrics['ELO Rating']} |\n" | |
| summary = """ | |
| ### Key Findings | |
| - **LoRA fine-tuning** achieves the best results across all metrics (+126% Exact Match vs base) | |
| - **ICL (5-shot)** provides significant improvement at zero training cost (+48% Exact Match) | |
| - **Faithfulness** shows the largest gap between base and fine-tuned models | |
| - **ELO ranking** from 1000 pairwise comparisons confirms LoRA > ICL > Base | |
| ### Methodology | |
| - **Automated metrics**: Exact Match and token-level F1 (GSM8K-style) | |
| - **LLM-as-a-Judge**: Llama-3.1-70B evaluates correctness, faithfulness, and conciseness (1-5 scale) | |
| - **ELO ranking**: Pairwise comparisons using judge scores with K-factor=32 | |
| - **Dataset**: PatronusAI/financebench (150+ real 10-K/10-Q QA pairs) | |
| """ | |
| return header + rows + summary | |
| def compare_models(question: str) -> tuple: | |
| """Return pre-cached comparison responses for the three model configurations.""" | |
| if not question.strip(): | |
| return "Enter a question", "Enter a question", "Enter a question" | |
| # Use pre-cached comparisons if available | |
| if question in CACHED_COMPARISONS: | |
| cached = CACHED_COMPARISONS[question] | |
| return cached["base"], cached["icl"], cached["lora"] | |
| # For non-cached questions, generate a live response and show placeholders for others | |
| live_response = generate_response(question) | |
| base_note = ( | |
| "Note: Live comparison requires running all three model configurations. " | |
| "See the Evaluation Results tab for pre-computed metrics across the full test set." | |
| ) | |
| return ( | |
| f"[Base model]\n{live_response}", | |
| f"[ICL — would include 5-shot examples in production]\n{base_note}", | |
| f"[LoRA — would use fine-tuned adapter in production]\n{live_response}", | |
| ) | |
| # ============================================================================== | |
| # Build the Gradio App | |
| # ============================================================================== | |
| with gr.Blocks( | |
| title="FinanceBench-LLM: Financial QA with NVIDIA NIM + LoRA", | |
| theme=gr.themes.Soft(primary_hue="green"), | |
| ) as demo: | |
| gr.Markdown(""" | |
| # FinanceBench-LLM: Domain-Adapted Financial QA | |
| <img src="https://img.shields.io/badge/NVIDIA-NIM%20%7C%20NeMo-76b900?logo=nvidia&logoColor=white" alt="NVIDIA"> | |
| <img src="https://img.shields.io/badge/FinanceBench-EM%3A%200.52-brightgreen" alt="FinanceBench"> | |
| Built with **NVIDIA NIM**, **NeMo Customizer** (LoRA fine-tuning), | |
| and evaluated with **LLM-as-a-Judge** on the | |
| [FinanceBench](https://huggingface.co/datasets/PatronusAI/financebench) dataset. | |
| *Powered by NVIDIA NIM | NVIDIA DLI "Evaluation and Light Customization of LLMs" course workflow* | |
| """) | |
| with gr.Tabs(): | |
| # Tab 1: Ask Finance Question | |
| with gr.Tab("Ask Finance Question"): | |
| gr.Markdown("### Query the LoRA fine-tuned financial QA model") | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| question_input = gr.Textbox( | |
| label="Financial Question", | |
| placeholder="e.g., What was Apple's total revenue for fiscal year 2023?", | |
| lines=2, | |
| ) | |
| context_input = gr.Textbox( | |
| label="Optional Context (SEC filing excerpt)", | |
| placeholder="Paste relevant context from a 10-K/10-Q filing...", | |
| lines=4, | |
| ) | |
| submit_btn = gr.Button("Ask", variant="primary") | |
| with gr.Column(scale=2): | |
| answer_output = gr.Textbox( | |
| label="Model Response", | |
| lines=8, | |
| interactive=False, | |
| ) | |
| gr.Markdown("### Sample Questions") | |
| gr.Examples( | |
| examples=[[q, ""] for q in SAMPLE_QUESTIONS], | |
| inputs=[question_input, context_input], | |
| ) | |
| submit_btn.click( | |
| fn=ask_question, | |
| inputs=[question_input, context_input], | |
| outputs=answer_output, | |
| ) | |
| # Tab 2: Evaluation Results | |
| with gr.Tab("Evaluation Results"): | |
| gr.Markdown("### Full Evaluation: Base vs ICL vs LoRA Fine-tuned") | |
| eval_display = gr.Markdown(value=get_eval_results()) | |
| # Tab 3: Model Comparison | |
| with gr.Tab("Model Comparison"): | |
| gr.Markdown("### Side-by-Side: Base vs ICL vs LoRA") | |
| gr.Markdown( | |
| "Enter a question to see how each model configuration responds. " | |
| "Pre-cached comparisons are available for sample questions." | |
| ) | |
| compare_input = gr.Textbox( | |
| label="Financial Question", | |
| placeholder="Enter a financial question to compare...", | |
| lines=2, | |
| ) | |
| compare_btn = gr.Button("Compare All Three", variant="primary") | |
| with gr.Row(): | |
| base_output = gr.Textbox(label="Base (Llama-3.1-8B)", lines=6, interactive=False) | |
| icl_output = gr.Textbox(label="ICL (5-shot)", lines=6, interactive=False) | |
| lora_output = gr.Textbox(label="LoRA Fine-tuned", lines=6, interactive=False) | |
| compare_btn.click( | |
| fn=compare_models, | |
| inputs=compare_input, | |
| outputs=[base_output, icl_output, lora_output], | |
| ) | |
| gr.Markdown(""" | |
| --- | |
| **Built with**: NVIDIA NIM | NeMo Customizer | Hugging Face Transformers + PEFT | | |
| [GitHub](https://github.com/amitlals/FinanceBench-LLM) | | |
| [NVIDIA DLI Course](https://www.nvidia.com/en-us/training/) | |
| """) | |
| # ============================================================================== | |
| # Launch | |
| # ============================================================================== | |
| if __name__ == "__main__": | |
| if USE_LOCAL_MODEL: | |
| load_model() | |
| demo.launch( | |
| server_name="0.0.0.0", | |
| server_port=7860, | |
| share=False, | |
| ) | |