---
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.