| --- |
| 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 |
