| --- |
| language: |
| - en |
| - ta |
| - hi |
| tags: |
| - agriculture |
| - india |
| - finance |
| - kcc |
| - pmkisan |
| - pmfby |
| - msp |
| - smallholder-farmers |
| - tamil |
| - mixtral |
| - lora |
| - adaptive-data |
| - adaption |
| - autoscientist-challenge |
| license: apache-2.0 |
| base_model: mistralai/Mixtral-8x7B-Instruct-v0.1 |
| datasets: |
| - solokingM/smartkisan-finance-dataset |
| --- |
| |
| # SmartKisan-Finance |
|
|
| A frontier LLM fine-tuned for **Indian smallholder-farmer financial advisory**, covering the |
| 2025-26 / 2026-27 agricultural-finance stack in **English, Tamil and Hindi**. Built for the |
| **Adaption AutoScientist Challenge 2026** (Finance category, Part 1). |
|
|
| ## Model details |
| - **Base model:** `mistralai/Mixtral-8x7B-Instruct-v0.1` (46.7B sparse MoE). |
| - **Method:** AutoScientist by Adaption — **LoRA SFT** (rank 64, α 128, dropout 0.05, all-linear |
| target modules, 4 epochs, lr 2e-4 cosine), data + recipe co-optimized. |
| - **Trained model id:** `adaption_smartkisan_finance` (finetune job `ac0d033a-…`). |
| - **Dataset:** [`solokingM/smartkisan-finance-dataset`](https://huggingface.co/datasets/solokingM/smartkisan-finance-dataset) — a grounded seed expanded with Adaptive Data (dataset `d832d855-…`). |
| - **Languages:** English, Tamil (தமிழ்), Hindi (हिन्दी). |
|
|
| ## Measured improvement vs baseline |
|
|
| **AutoScientist held-out win rate:** the adapted model is preferred **66%** vs **34%** for the |
| base `Mixtral-8x7B-Instruct-v0.1` — a **+32-point** margin (this is the challenge's headline metric). |
|
|
| **Adaptive Data quality (dataset):** 6.0 → 8.1 (**+35% relative**), Grade **C → B**, percentile 7.2 → 17.8. |
|
|
| | Metric | Base (Mixtral-8x7B-Instruct-v0.1) | SmartKisan-Finance | |
| |---|---|---| |
| | AutoScientist win rate | 34% | **66%** | |
|
|
| ### Supplementary fact-accuracy eval |
| [`scripts/eval.py`](scripts/eval.py) scores a 29-question held-out set (disjoint from training — |
| asserted in code) for exact-value fact accuracy, Tamil-script consistency and ROUGE-L. Run it on a |
| GPU (Mixtral-8x7B needs ~25GB+ in 4-bit) and paste the numbers here before submitting: |
|
|
| | Metric | Base | SmartKisan-Finance | |
| |---|---|---| |
| | Fact accuracy | _fill_ % | _fill_ % | |
| | Tamil-script consistency | _fill_ % | _fill_ % | |
| | ROUGE-L (vs reference facts) | _fill_ | _fill_ | |
|
|
| ## What it knows |
| - **KCC 2026** — ₹5L limit, ₹2L collateral-free, 4% effective rate on prompt repayment (7% base; 3% prompt-repayment incentive; 1.5% MISS subvention to banks), digital e-KCC application. |
| - **PM-KISAN** — ₹6,000/yr in 3× ₹2,000 DBT instalments, e-KYC, exclusions, payment-failure fixes. |
| - **PMFBY Kharif 2026** — 2%/1.5%/5% premiums, coverage, new Wild Animal Attack & Paddy Inundation add-ons, claim filing, 12% delay penalty. |
| - **MSP 2025-26 & 2026-27** — per-quintal rates for all Kharif & Rabi crops, procurement agencies (FCI, NAFED, CCI). |
| - **Mandi literacy** — AGMARKNET min/max/modal, eNAM. |
| - **Loan comparison** — KCC vs moneylender vs MFI vs gold loan, with numbers; JLG for the landless. |
| - **Input financing & scheme stacking** — KCC for inputs, PM-KUSUM, warehouse receipt financing, combining schemes. |
| - **General finance literacy** — simple vs compound interest, UPI safety, fraud red flags (call **1930**), PMJJBY/PMSBY/APY, budgeting. |
|
|
| ## How to use |
| This is a **LoRA adapter** over `mistralai/Mixtral-8x7B-Instruct-v0.1`. Load the base model and apply |
| the adapter (PEFT), or use the merged weights if you exported them. A GPU is required (the base is |
| 46.7B params). |
|
|
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| from peft import PeftModel |
| base = "mistralai/Mixtral-8x7B-Instruct-v0.1" |
| tok = AutoTokenizer.from_pretrained(base) |
| model = AutoModelForCausalLM.from_pretrained(base, device_map="auto", load_in_4bit=True) |
| model = PeftModel.from_pretrained(model, "solokingM/SmartKisan-Finance") |
| ``` |
|
|
| ## Intended use |
| Advisory support for Indian small/marginal farmers, CSC operators, Krishi Vigyan Kendra |
| counsellors, and agri-fintech developers. |
|
|
| ## Limitations & safety |
| - Knowledge cutoff ~June 2026; MSP and scheme values change — re-verify against official portals. |
| - **Not a substitute** for a bank officer or government advisory. Informational only. |
| - Tamil/Hindi coverage is strong for national schemes; some state-specific variations may differ. |
| - Verify any loan/insurance decision against pmkisan.gov.in, pmfby.gov.in, RBI and NABARD. |
|
|
| ## Credits |
| Dataset built and adapted with **Adaptive Data**, model trained with **AutoScientist**, by |
| [Adaption](https://adaptionlabs.ai) — AutoScientist Challenge 2026. |
|
|
