---
language: en
license: mit
base_model: microsoft/Phi-3.5-mini-instruct
tags:
- finance
- sec-filings
- qlora
- peft
- kpi-extraction
- financial-analysis
pipeline_tag: text-generation
---

# Earnings Intelligence Copilot — Fine-tuned Phi-3.5-mini (Merged)

A QLoRA fine-tuned and merged version of Phi-3.5-mini-instruct for structured KPI extraction from SEC filings.
Part of the [Earnings Intelligence Copilot](https://github.com/ratnasekhar/earnings-copilot) — a multi-agent 
system that ingests SEC filings, extracts KPIs, and generates citation-grounded investment memos.

## What it does

- Extracts financial KPIs (Revenue, Gross Margin, Operating Income, EPS, Free Cash Flow) from SEC filing chunks as structured JSON
- Returns `{"confidence": "UNVERIFIABLE"}` instead of hallucinating when data is not present
- Always includes a `source_quote` field grounding every answer in the original filing text

## Model Details

| Property | Value |
|---|---|
| Base model | microsoft/Phi-3.5-mini-instruct |
| Fine-tuning method | QLoRA (4-bit NF4 quantization) |
| LoRA rank | r=16, alpha=32 |
| Target modules | q_proj, v_proj, k_proj, o_proj |
| Training examples | 619 balanced examples (50% HIGH / 50% UNVERIFIABLE) |
| Data source | 10-K and 10-Q filings, 20 S&P 500 companies (2020-2024) |
| Training hardware | Kaggle T4 GPU (~75 minutes) |
| Model type | Full merged model (adapter + base combined) |

## Usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained(
    "ratnasekhar/earnings-copilot-phi3-merged",
    trust_remote_code=True
)
model = AutoModelForCausalLM.from_pretrained(
    "ratnasekhar/earnings-copilot-phi3-merged",
    dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True,
    attn_implementation="eager"
)
model.eval()

chunk = "Net sales for Q1 FY2024 were $119.6 billion, an increase of 2% compared to Q1 FY2023."
prompt = (
    "<|user|>\n"
    "You are a financial KPI extraction model. Extract metrics as JSON. "
    "Output {\"confidence\": \"UNVERIFIABLE\"} if not found. Never invent numbers.\n\n"
    "Filing chunk:\n" + chunk + "\n\n"
    "Extract: What was total revenue and its YoY change?<|end|>\n"
    "<|assistant|>\n"
)

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
with torch.no_grad():
    out = model.generate(
        **inputs,
        max_new_tokens=150,
        do_sample=False,
        pad_token_id=tokenizer.eos_token_id,
        use_cache=False
    )
print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))
```

## Example Outputs

**When data IS present:**
```json
{
  "metric": "Total Revenue",
  "value": "$119.6 billion",
  "unit": "Billion",
  "period": "Q1 FY2024",
  "yoy_change": "+2%",
  "source_quote": "Net sales for Q1 FY2024 were $119.6 billion",
  "confidence": "HIGH"
}
```

**When data is NOT present:**
```json
{
  "confidence": "UNVERIFIABLE",
  "reason": "The chunk does not contain any specific revenue figures."
}
```

## Key Design Decision — Class Balance

Raw LLM-generated training data had 93% UNVERIFIABLE examples. Training on this 
imbalanced data would cause the model to always refuse. We deliberately resampled 
to 50/50 HIGH/UNVERIFIABLE to teach both behaviors equally — this is the core 
fine-tuning contribution of the project.

## Related Models

| Model | Description |
|---|---|
| [ratnasekhar/earnings-copilot-mistral-7b](https://huggingface.co/ratnasekhar/earnings-copilot-mistral-7b) | Mistral-7B LoRA adapter (larger, higher quality) |
| ratnasekhar/earnings-copilot-phi3-merged | Phi-3.5-mini merged model (this model, deployable) |

## System Architecture
```
SEC Filings (220 filings, 20 S&P 500 tickers)
        ↓
Qdrant Cloud (2,464 financial table chunks)
        ↓
Phi-3.5-mini — this model (KPI extraction)
        ↓
Verification Agent (cross-checks every number)
        ↓
Citation-grounded Investment Memo
```