llama-2-7b-RandomGPT-5K-ar

Model Card for Model ID

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This model is an LoRA adapter file from finetuned Llama-2-7b-hf model. This is an experimental model.

To run it, you need to:

Agree with Meta's agreements to download the Llama-2-13b-chat-hf model from here: https://huggingface.co/meta-llama/Llama-2-13b-chat-hf Clone this repository Clone the Alpaca-LoRA repository from here: https://github.com/tloen/alpaca-lora Use this command to run it: -python generate.py --load_8bit --base_model 'PATH_TO_YOUR_LOCAL_LLAMA_2_7B_CHAT_HF' --lora_weights 'PATH_TO_YOUR_LOCAL_FILE_OF_THIS_MODEL' You must agree with Meta/Llama-2's agreements to use this model.

Model Details

Model Description

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• Developed by: Saja Nakhleh

• Model type: Question answering model

• Language(s) (NLP): Arabic

• Finetuned from model [optional]: llama-2-7b

Model Sources [optional]

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• Paper [optional]: Not Yet

Bias, Risks, and Limitations

This model performs well with hetrogenius data.

Recommendations

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None

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoConfig, AutoModel, AutoTokenizer config = AutoConfig.from_pretrained("sajaw/llama-2-7b-RandomGPT-5K-ar") model = AutoModel.from_pretrained("sajaw/llama-2-7b-RandomGPT-5K-ar") tokenizer = AutoTokenizer.from_pretrained("sajaw/llama-2-7b-RandomGPT-5K-ar")

Training Details

Training Data

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• sajaw/Arasquad3_llama2_version2
• sajaw/GQA_llama2_version

Preprocessing [optional]

Context, questions and answers are concatinated with the instructions in one "message" record

Training Hyperparameters

• Training regime: NA <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

Speeds, Sizes, Times [optional]

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NA

Evaluation

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Testing Data, Factors & Metrics

Testing Data

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We have used 250 samples from AraSquad as true samples to test the model

Factors

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NA

Metrics

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F1-score, precision, recall

Results

F1-score= 0.6818 precision= 0.6564 recall=0.7226

Summary

Model Examination [optional]

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NA

Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

• Hardware Type: kaggle - GPU T4 *2
• Hours used: 9 hours
• Cloud Provider: kaggle
• Compute Region: NA
• Carbon Emitted: NA

Technical Specifications [optional]

Model Architecture and Objective

NA

Compute Infrastructure

NA

Hardware

NA

Software

NA

Citation [optional]

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BibTeX:

APA:

Glossary [optional]

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NA

More Information [optional]

NA

Model Card Authors [optional]

Saja Nakhleh

Model Card Contact

[email protected]