Model Card for Model ID
This is a finetuned model trained on agricultural datasets for crop disease remedies.
Model Details
phi3-finetuned-20250414-0740 can be used for crop disease remedies.
Peft ,QLoRA ,LoRA ,
transformers are used and supervised finetuning is done for training this model.
LoRA_dropout was taken 0.1, Lora_r=16.
This model was trained on Google Colab free tier giving T4 GPU of 15 GB vRAM and can be used for 12 hours.
Model Description
Finetuned Agricultural Chatbot (Phi-3-mini-4k-instruct)
fine-tuned Microsoft’s Phi-3-mini-4k-instruct, a compact yet powerful instruction-tuned LLM (~3.8B parameters),
specifically for agriculture-related tasks using curated domain-specific datasets.
Built on top of Microsoft’s Phi-3-mini-4k-instruct, a lightweight but capable open-source language model,
this chatbot has been carefully trained using thousands of real-world examples from the agricultural domain.
From crop disease symptoms and soil health tips to pesticide usage and sustainable farming practices,
it has absorbed knowledge from curated, high-quality datasets.
- Developed by: Satyam Kahali (reach me out on LinkedIN "https://www.linkedin.com/in/satyam-kahali-883098235/")
- Model type: Causal Language Model (CausalLM)
- Language(s) (NLP): Python
- License: apache-2.0
- Finetuned from model : microsoft/Phi-3-mini-4k-instruct
Model Sources
- Repository: Satyam66/phi3-finetuned-20250414-0740
Uses
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Training Details
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Training Procedure
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Training Hyperparameters
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lora_alpha: 32,
lora_bias: false,
lora_dropout: 0.05,
r: 16,
fp16 = True
bf16 = False
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Environmental Impact
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Machine Learning Impact calculator presented in
Lacoste et al. (2019).
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