---
base_model: microsoft/Phi-3-mini-4k-instruct
library_name: transformers
license: apache-2.0
language:
- en
tags:
- Finetuning
- PEFT
- NLP
- LLM
- text-generation-inference
- transformers
- QLoRA
- LoRA
---

# 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 

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- **Repository:** Satyam66/phi3-finetuned-20250414-0740


## Uses

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### Direct Use

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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### Recommendations

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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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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
  lora_alpha: 32,
  lora_bias: false,
  lora_dropout: 0.05,
  r: 16,
  fp16 = True  
  bf16 = False 
#### Speeds, Sizes, Times [optional]

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## Evaluation

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### Results

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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]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]

### Model Architecture and Objective

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## Glossary [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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### Framework versions

- PEFT 0.15.1