Create README.md

README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen2.5-7B-Instruct
+tags:
+  - finance
+  - financial-qa
+  - qlora
+  - unsloth
+  - qwen2.5
+  - quantitative
+datasets:
+  - TheFinAI/flare-finqa
+  - sujet-ai/Sujet-Finance-Instruct-177k
+language:
+  - en
+pipeline_tag: text-generation
+library_name: transformers
+model-index:
+  - name: krishna-finance-7b
+    results: []
+---
+
+# krishna-finance-7b
+
+A fine-tuned **Qwen2.5-7B-Instruct** model specialized for **financial question answering and quantitative reasoning**. Trained on a combination of financial QA and instruction-following datasets to handle earnings analysis, ratio calculations, financial statement interpretation, and investment reasoning.
+
+## Key Details
+
+| | |
+|---|---|
+| **Base model** | Qwen/Qwen2.5-7B-Instruct |
+| **Method** | QLoRA (4-bit NF4, rank 16, alpha 16) |
+| **Library** | Unsloth + TRL SFTTrainer |
+| **Datasets** | TheFinAI/flare-finqa (5K) + Sujet-Finance-Instruct-177k (5K) |
+| **Total examples** | 10,000 |
+| **Hardware** | NVIDIA RTX A5000 (24GB VRAM) on RunPod |
+| **Training time** | ~2.75 hours |
+| **Parameters trained** | 40.4M of 7.66B (0.53%) |
+| **Format** | ChatML (`<\|im_start\|>` / `<\|im_end\|>`) |
+| **Output** | Merged 16-bit safetensors |
+
+## Dataset Composition
+
+The training data blends two complementary sources:
+
+- **FinQA** (5,000 examples) — financial question answering requiring numerical reasoning over earnings reports, balance sheets, and financial tables. Teaches the model to extract numbers, perform calculations, and explain financial logic step by step.
+
+- **Sujet Finance Instruct** (5,000 examples) — broad financial instruction data covering investment analysis, market concepts, risk assessment, portfolio management, and financial planning. Gives the model general financial fluency.
+
+## Usage
+
+### Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("sriksven/krishna-finance-7b")
+tokenizer = AutoTokenizer.from_pretrained("sriksven/krishna-finance-7b")
+
+messages = [
+    {
+        "role": "system",
+        "content": "You are a financial analyst. Answer questions about financial data with precise calculations and step-by-step reasoning.",
+    },
+    {
+        "role": "user",
+        "content": "A company reported revenue of $120M and cost of goods sold of $75M. Operating expenses were $25M. Calculate the gross margin and operating margin.",
+    },
+]
+
+inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
+outputs = model.generate(inputs, max_new_tokens=512)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+### Unsloth (faster inference)
+
+```python
+from unsloth import FastLanguageModel
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name="sriksven/krishna-finance-7b",
+    max_seq_length=2048,
+    load_in_4bit=True,
+)
+FastLanguageModel.for_inference(model)
+```
+
+## Example Capabilities
+
+- **Financial ratio calculation** — gross margin, operating margin, ROE, P/E, debt-to-equity
+- **Earnings analysis** — interpreting revenue trends, YoY growth, segment performance
+- **Financial statement reading** — balance sheet, income statement, cash flow analysis
+- **Investment reasoning** — valuation approaches, risk factors, portfolio considerations
+- **Quantitative QA** — multi-step numerical reasoning over financial data
+
+## Intended Use
+
+- Financial question answering systems
+- Building finance-focused chatbots or copilots
+- Quantitative analysis assistants for analysts and students
+- Research on domain-specific LLM fine-tuning in finance
+
+## Limitations
+
+- Not a financial advisor — outputs should not be used as investment advice
+- Trained on English-language financial data only
+- May hallucinate financial figures not present in the input context
+- No real-time market data access — knowledge limited to training data patterns
+- Not evaluated against established financial NLP benchmarks (FinQA leaderboard, etc.)
+- Best results when using the system prompt format matching training
+
+## Training Infrastructure
+
+| | |
+|---|---|
+| **GPU** | NVIDIA RTX A5000 24GB |
+| **Cloud** | RunPod ($0.27/hr) |
+| **Framework** | Unsloth 2026.5.2 + TRL + Transformers 5.5.0 |
+| **Precision** | BF16 training, 4-bit NF4 base quantization |
+| **Optimizer** | AdamW 8-bit |
+| **Learning rate** | 2e-4, linear decay |
+| **Batch size** | 16 effective (4 per device × 4 accumulation) |
+| **Packing** | Enabled |
+
+## Source Code
+
+Training scripts and configs: [github.com/sriksven/LLM-FineTune-Suite](https://github.com/sriksven/LLM-FineTune-Suite)
+
+## License
+
+Apache 2.0