Model Card for Model ID
Model Details
Model Description
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
pass the product description and the model will try its best to predict the price of the product.
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
Use the code below to get started with the model.
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Training Details
Training Data
"McAuley-Lab/Amazon-Reviews-2023" was the training data used.
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Training Procedure
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
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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Hardware
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Software
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