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
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 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:
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
- Developed by: [More Information Needed]
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- Language(s) (NLP): [More Information Needed]
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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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Evaluation
Testing Data, Factors & Metrics
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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