---
license: apache-2.0
base_model: meta-llama/Llama-3.2-3B-Instruct
datasets:
- 15juneee/agriculture-advisor-adapted-multilingual-v1
tags:
- agri_ml
- adaption-autoscientist
- lora
language:
- en
---

# Agriculture Advisor Multilingual (agri_ml)

Fine-tuned for agricultural advisory in English, Swahili, Amharic, Hausa and Hindi, trained with
[Adaption AutoScientist](https://docs.adaptionlabs.ai/guides/autoscientist-api/) for the
AutoScientist Challenge (Part 2).

- **Base model:** `meta-llama/Llama-3.2-3B-Instruct`
- **Training data:** [`15juneee/agriculture-advisor-adapted-multilingual-v1`](https://huggingface.co/datasets/15juneee/agriculture-advisor-adapted-multilingual-v1)
  (also on [Kaggle](https://www.kaggle.com/datasets/junesdata/agriculture-advisor-adapted-multilingual-v1))
- **Method:** AutoScientist co-optimised data adaptation and training recipe

## Measured improvement

AutoScientist reported best_win_rate = 0.4851 over 5 iterations against meta-llama/Llama-3.2-3B-Instruct. That is below the 0.50 break-even point, so this model does not improve on its baseline in English - see limitations.

Evaluation methodology, including the position-swap and dual-judge controls, is in
`EVAL.md` in the project repository. The held-out split used is published alongside the
training data so the number can be reproduced.

## Intended use and limitations

Intended for agricultural advisory assistance across English, Swahili, Amharic, Hausa and Hindi. **Not a substitute for local agricultural extension services.** Any pesticide, herbicide or veterinary guidance must be checked against the current product label and local regulations.

**Stated plainly:** on AutoScientist's own evaluation this model scored a 0.4851 win rate against its base - it did *not* beat the baseline. About 40% of its training rows are non-English, which dilutes performance on an English-judged benchmark. It is released as a multilingual-capability artifact, not as an English-performance improvement; prefer the English-only sibling model where English quality is what matters.

## Reproducing

The dataset build, training pipeline and evaluation harness are all scripted; see the
project repository.