---
license: apache-2.0
tags:
- unsloth
- Agriculture
- QA
- LLM
datasets:
- KisanVaani/agriculture-qa-english-only
language:
- en
base_model:
- unsloth/Llama-3.2-3B-Instruct
new_version: ShuklaShreyansh/Agro-QA
pipeline_tag: question-answering
library_name: transformers
---



# Model Card for Agro-QA

This model is fine-tuned for agricultural question-answering tasks. It leverages the Llama-3.2-3B-Instruct model to address a variety of topics in agriculture, such as crop selection, pest management, irrigation, and farming best practices.

## Model Details

### Model Description

- **Developed by:** Shukla Shreyansh
- **Model type:** Question Answering (QA)
- **Language(s) (NLP):** English
- **License:** Apache-2.0
- **Finetuned from model:** [unsloth/Llama-3.2-3B-Instruct](https://huggingface.co/unsloth/Llama-3.2-3B-Instruct)

---

## Uses

### Direct Use

The model is intended for question-answering applications specific to agriculture. It provides insights into farming techniques, crop choices, pest management, and related topics.

### Out-of-Scope Use

The model is not designed for non-agriculture-related questions or tasks requiring specialized domain knowledge outside of agriculture.

---

## Training Details

### Training Data

The model is fine-tuned on the [KisanVaani/agriculture-qa-english-only](https://huggingface.co/datasets/KisanVaani/agriculture-qa-english-only) dataset, a curated collection of questions and answers focused on agricultural topics.

### Training Procedure

- **Training regime:** Mixed precision (FP16)
- **Batch size:** 2 (per device)
- **Epochs:** 1
- **Learning rate:** 2e-4
- **Optimizer:** AdamW with 8-bit precision

---

## Evaluation

### Testing Data

The model is evaluated on a subset of the training dataset to measure its performance in answering agriculture-related questions.

### Metrics

- **Accuracy:** [More Information Needed]
- **F1 Score:** [More Information Needed]

---

## How to Get Started with the Model

Use the code below to load and use the model:

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("ShuklaShreyansh/Agro-QA")

# Load model
model = AutoModelForCausalLM.from_pretrained("ShuklaShreyansh/Agro-QA").to("cuda")

# Example usage
messages = [{"role": "user", "content": "What are the best rabi crops to grow?"}]
inputs = tokenizer.apply_chat_template(messages, tokenize=True, return_tensors="pt").to("cuda")
output = model.generate(input_ids=inputs['input_ids'], max_new_tokens=128)
print(tokenizer.decode(output[0]))
```
# Model Card for Model ID

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## Model Details

### Model Description

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## Uses

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## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

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### Training Procedure

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#### Training Hyperparameters

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#### Summary



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## Environmental Impact

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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).

- **Hardware Type:** [More Information Needed]
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