tinyllama-qlora

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Model Details

Model Description

This repository contains a QLoRA fine-tuned adapter based on TinyLlama. The model was trained using Parameter-Efficient Fine-Tuning (PEFT) techniques, specifically LoRA and 4-bit quantization, to reduce memory requirements while maintaining performance.

The project demonstrates practical usage of:

TinyLlama QLoRA (4-bit Quantization) PEFT (LoRA) Hugging Face Transformers Hugging Face Hub

This adapter was created as part of an AI/ML learning project focused on understanding modern LLM fine-tuning workflows

• Developed by: [Dongala Tejaswi]
• Funded by [optional]: [QLoRA Fine-Tuned Adapter]
• Shared by [optional]: [More Information Needed]
• Model type: [More Information Needed]
• Language(s) (NLP): [English]
• License: [Apache-2.0]
• Finetuned from model [optional]: [TinyLlama/TinyLlama-1.1B-Chat-v1.0]

Model Sources [optional]

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Uses

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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

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How to Get Started with the Model

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Training Details

Training Data

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Training Procedure

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Preprocessing [optional]

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Training Hyperparameters

• Training regime: [More Information Needed] <!--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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Evaluation

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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

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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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Framework versions

• PEFT 0.19.1