reviews-amazon

Model Card for Model ID

Model Details

Model Description

This model is developed and owned by Abhishek Singh.
It showcases the power of fine-tuning Small Language Models (SLMs) using LoRA-based parameter-efficient training.
Despite being compact, this fine-tuned version outperforms several industry-standard general-purpose LLM benchmarks on the target downstream task.

The project demonstrates how strategically fine-tuned SLMs can achieve exceptional performance at a fraction of the compute cost.

• Developed by: Abhishek Singh
• Model Type: Small Language Model (SLM) with LoRA fine-tuning
• Language(s): English

Model Sources

• Repository: https://huggingface.co/abhishekrathore18/reviews-amazon

Uses

Direct Use

This model can be directly used for the downstream task it was fine-tuned on classification.
It is optimized for low-latency, resource-efficient inference while maintaining high accuracy.

Downstream Use

The model can be further fine-tuned for related tasks or integrated into production pipelines where lightweight and high-performing models are required.

Out-of-Scope Use

• High-stakes decision making
• Use cases requiring broad general reasoning far outside the fine-tuning domain
• Misuse, harmful content generation, or any malicious activity

Bias, Risks, and Limitations

The base model may retain biases from pretraining data.
Fine-tuning reduces task-specific errors but may not fully eliminate inherent biases.

Recommendations

Users should evaluate the model on their own domain data to ensure safety, fairness, and alignment with their use case.

How to Get Started with the Model

from transformers import AutoTokenizer, AutoModelForSequenceClassification

model_id = "abhishekrathore18/reviews-amazon"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

inputs = tokenizer("Your input text", return_tensors="pt")
outputs = model(**inputs)