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----
-library_name: transformers
-tags: []
----
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
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-### Model Sources [optional]
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-## Uses
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-### Direct Use
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-## Bias, Risks, and Limitations
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-### Recommendations
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
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-## Training Details
-### Training Data
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-## Evaluation
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-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-## Technical Specifications [optional]
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-## Citation [optional]
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+# 🌾 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):
+  ```json
+  {
+    "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
+```python
+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}]
+```