| ---
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| license: apache-2.0
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| base_model: meta-llama/Llama-3.2-3B-Instruct
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| datasets:
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| - 15juneee/agriculture-advisor-adapted-multilingual-v1
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| tags:
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| - agri_ml
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| - adaption-autoscientist
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| - lora
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| language:
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| - en
|
| ---
|
|
|
| # Agriculture Advisor Multilingual (agri_ml)
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|
|
| Fine-tuned for agricultural advisory in English, Swahili, Amharic, Hausa and Hindi, trained with
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| [Adaption AutoScientist](https://docs.adaptionlabs.ai/guides/autoscientist-api/) for the
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| AutoScientist Challenge (Part 2).
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|
|
| - **Base model:** `meta-llama/Llama-3.2-3B-Instruct`
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| - **Training data:** [`15juneee/agriculture-advisor-adapted-multilingual-v1`](https://huggingface.co/datasets/15juneee/agriculture-advisor-adapted-multilingual-v1)
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| (also on [Kaggle](https://www.kaggle.com/datasets/junesdata/agriculture-advisor-adapted-multilingual-v1))
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| - **Method:** AutoScientist co-optimised data adaptation and training recipe
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|
|
| ## 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
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| `EVAL.md` in the project repository. The held-out split used is published alongside the
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| training data so the number can be reproduced.
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|
|
| ## Intended use and limitations
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|
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| 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.
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|
|
| **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.
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|
|
| ## Reproducing
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|
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| The dataset build, training pipeline and evaluation harness are all scripted; see the
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| project repository.
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| |
