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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]
- Repository: [(https://huggingface.co/Ghaadaabduallah/tasleem-phi3-lora-adapter)]
- Paper [[N/A]: [More Information Needed]
- Demo [N/A]: [More Information Needed]
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
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!
[More Information Needed]
Training Details
Training Data
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Training Procedure
Preprocessing [optional]
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Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
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Evaluation
Testing Data, Factors & Metrics
Testing Data
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Factors
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Metrics
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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).
- 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]
Technical Specifications [optional]
Model Architecture and Objective
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Compute Infrastructure
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Hardware
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Software
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Citation [optional]
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