Satyam66/phi3-finetuned-20250414-0740

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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

Direct 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

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Training Details

Training Data

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Training Procedure

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Training Hyperparameters

• Training regime: [More Information Needed] lora_alpha: 32, lora_bias: false, lora_dropout: 0.05, r: 16, fp16 = True
  bf16 = False

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Evaluation

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Summary

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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]

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Framework versions

• PEFT 0.15.1