README.md

metadata

language:
  - es
license: mit
tags:
  - text-classification
  - agriculture
  - climate
  - potato
  - Peru
  - Huancavelica
  - LLaMA
  - environmental-prediction
model-index:
  - name: llama-lateblight-classifier
    results:
      - task:
          type: text-classification
          name: Potato Late Blight Risk Classification
        dataset:
          name: Huancavelica Late Blight Benchmark (Balanced)
          type: tabular
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.97
          - name: F1 (macro)
            type: f1
            value: 0.97
          - name: Precision
            type: precision
            value: 0.97
          - name: Recall
            type: recall
            value: 0.97
pipeline_tag: text-classification
library_name: transformers

🌾 LLaMA Late Blight Classifier (Huancavelica, Peru)

This model is a fine-tuned classifier based on openlm-research/open_llama_3b, trained to predict potato late blight risk levels (Bajo, Moderado, Alto) in the highlands of Huancavelica, Peru. It uses environmental inputs (temperature, humidity, precipitation) and crop variety metadata to output discrete classifications.

---

🤝 Use Case

Direct Use: Agronomic advisory systems or research tools predicting potato late blight risk from structured prompts or API queries.

Not for: Open-ended generation, conversational use, or regions with different pathogen pressures without retraining.

---

🌐 Model Details

• Base model: openlm-research/open_llama_3b
• Architecture: LLaMA-3B with classification head (AutoModelForSequenceClassification)
• Fine-tuning method: Full fine-tuning on a balanced, curated dataset (not LoRA)
• Tokenizer: Compatible LLaMA tokenizer (tokenizer.model included)
• Language: Spanish (with structured Spanish prompts)
• Task: Hard classification (3-class)

---

🎓 Training

• Dataset: 156 training + 24 validation examples (balanced across 3 classes)
• Labels: Bajo, Moderado, Alto
• Format (JSONL):

  {
    "instruction": "Evalúa el riesgo de tizón tardío basado en los datos climáticos y la variedad.",
    "input": "Escenario 1: Temperatura promedio 17.2 °C, Humedad 83%, Precipitación 3.4 mm, Variedad Yungay",
    "output": "Moderado"
  }
  

• Epochs: 10
• Optimizer: AdamW (mixed precision)
• Hardware: 1x A100 40GB (Colab Pro, single GPU)

---

🌿 Evaluation (Balanced Test Set, n = 90)

Class	Precision	Recall	F1	Support
Bajo	1.00	0.90	0.95	30
Moderado	0.91	1.00	0.95	30
Alto	1.00	1.00	1.00	30
Accuracy			0.97	90

---

📈 Intended Use and Limitations

• Designed for: Highland regions in Peru (esp. Huancavelica), with expert-labeled ground truth and local pathogen behavior.
• Limitations:
  • May generalize poorly to lowland areas or different varieties.
  • Not a substitute for in-field disease monitoring.

---

📑 Citation

If you use this model, please cite:

Jorge Luis Alonso, Predicting Potato Late Blight in Huancavelica Using LLaMA Models, 2025

---

🌍 License

MIT License (model + training data)

---

⚡ Quick Inference Example

from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
model = AutoModelForSequenceClassification.from_pretrained("jalonso24/llama-lateblight-classifier")
tokenizer = AutoTokenizer.from_pretrained("jalonso24/llama-lateblight-classifier")
clf = pipeline("text-classification", model=model, tokenizer=tokenizer, top_k=1)

prompt = "Escenario: Temperatura 18.1 °C, Humedad 85%, Variedad Amarilis"
clf(prompt)
# ➞ [{'label': 'Alto', 'score': 0.95}]