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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]
- Repository: [More Information Needed]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
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
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
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Training Procedure
Preprocessing [optional]
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Training Hyperparameters
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Speeds, Sizes, Times [optional]
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Evaluation
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
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).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
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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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Framework versions
