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

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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.

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Training Details

Training Data

The model is fine-tuned on the 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

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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]

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How to Get Started with the Model

Use the code below to load and use the model:

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

This modelcard aims to be a base template for new models. It has been generated using this raw template.

Model Details

Model Description

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Model Sources [optional]

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Uses

Direct Use

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Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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.

[More Information Needed]

Training Details

Training Data

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Training Procedure

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Training Hyperparameters

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Evaluation

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Testing Data

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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