fine-tuned-model-name

Model Card for Model ID

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Model Details

Model Description

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This is a "meta-llama/Meta-Llama-3.1-8B" model finetuned on "McAuley-Lab/Amazon-Reviews-2023" dataset. This model aims to predict the price given the product information is passed to it.

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• Finetuned from model [optional]: ["meta-llama/Meta-Llama-3.1-8B"]

Direct Use

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pass the product description and the model will try its best to predict the price of the product.

Recommendations

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How to Get Started with the Model

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Training Details

Training Data

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"McAuley-Lab/Amazon-Reviews-2023" was the training data used.

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Training Procedure

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Preprocessing [optional]

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Training Hyperparameters

-Hyperparameters for QLORA:

LORA_R = 32

LORA_ALPHA = 64

TARGET_MODULES = ["q_proj", "v_proj", "k_proj", "o_proj"]

LORA_DROPOUT = 0.1

QUANT_4_BIT = True

-Hyperparameters for training EPOCHS = 3

BATCH_SIZE = 16

GRADIENT_ACCUMULATION_STEPS = 1

LEARNING_RATE = 1e-4

LR_SCHEDULER_TYPE = 'cosine'

WARMUP_RATIO = 0.03

OPTIMIZER = "paged_adamw_32bit"

Results

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Summary

Model Examination [optional]

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Environmental Impact

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

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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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Glossary [optional]

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