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Model: ratnasekhar/earnings-copilot-phi3-merged Source: Original Platform
2026-07-15 04:23:10 +08:00
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+---
+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
+```