yashhanamghar9099/Yash-Llama2-7B-QLoRA-v1
Yash-Llama2-7B-QLoRA-v1 is an experimental 7 billion parameter Llama 2 model fine-tuned by Yash Hanamghar for text generation and instruction following. This model demonstrates Supervised Fine-Tuning (SFT) using QLoRA and PEFT techniques. It is primarily intended for learning LLM fine-tuning, QLoRA experiments, and educational demonstrations. A small manual evaluation showed an approximate 8% overall score improvement over its base model.
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Model Overview
Yash-Llama2-7B-QLoRA-v1 is an experimental 7 billion parameter language model developed by Yash Hanamghar. It is a fine-tuned version of the NousResearch/Llama-2-7b-chat-hf base model, focusing on text generation and instruction following tasks. The project serves as a practical demonstration of various fine-tuning techniques.
Key Fine-Tuning Techniques Demonstrated
This model showcases the application of several advanced fine-tuning methodologies:
- Supervised Fine-Tuning (SFT)
- QLoRA / LoRA: Efficient fine-tuning methods for large language models.
- PEFT: Parameter-Efficient Fine-Tuning.
- 4-bit quantization and bitsandbytes: Techniques for reducing model size and computational requirements.
- Adapter merging and Hugging Face deployment.
Evaluation and Performance
A small, manual evaluation was conducted using 10 prompts, comparing the fine-tuned model against its base. The results indicated an approximate 8% improvement in overall manual score, with notable gains in clarity and structure. It is important to note that this was an experimental manual evaluation and not a comprehensive LLM benchmark.
Intended Use Cases
This model is primarily designed for:
- Learning and experimenting with LLM fine-tuning processes.
- Exploring QLoRA and LoRA techniques.
- Instruction-following experiments.
- General text generation tasks.
- Educational and research demonstrations.
Limitations
As an experimental model, Yash-Llama2-7B-QLoRA-v1 may produce incorrect information, hallucinations, repetitive responses, or biased outputs. Important outputs should always be independently verified.