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Ghadaalabdullah
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tasleem-phi3-lora-adapter

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Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

  • Developed by: [Ghaadaabduallah]
  • Funded by [Self-funded/Educational Project]: [More Information Needed]
  • Shared by [Ghaadaabduallah]: [More Information Needed]
  • Model type: [PEFT/LoRA Adapter]
  • Language(s) (NLP): [Arabic (ar)]
  • License: [Microsoft Phi-3 License]
  • Finetuned from model [microsoft/Phi-3-mini-4k-instruct]: [More Information Needed]

Model Sources [optional]

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

To use this LoRA adapter, you must load the base Phi-3 model and then attach the LoRA weights. import torch from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig from peft import PeftModel

MODEL_NAME = "microsoft/Phi-3-mini-4k-instruct" LORA_ADAPTER = "Ghaadaabduallah/tasleem-phi3-lora-adapter"

bnb_config = BitsAndBytesConfig(load_in_4bit=True)

1. Load the base model with 4-bit quantization

model = AutoModelForCausalLM.from_pretrained( MODEL_NAME, quantization_config=bnb_config, device_map="auto", torch_dtype=torch.float16, )

2. Attach the LoRA adapter weights

model = PeftModel.from_pretrained(model, LORA_ADAPTER) tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)

The model is now ready for inference!

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Training Details

Training Data

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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).

  • Hardware Type: [NVIDIA T4]
  • Hours used: [~2 hours]
  • Cloud Provider: [Google Colab / Google Cloud Platform]
  • Compute Region: [More Information Needed]
  • Carbon Emitted: [More Information Needed]

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