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Description
Appa AI explores fine-tuning small language models (LLMs) on personal conversational data. Using PyTorch, Hugging Face Transformers, and Jupyter Notebooks, dataset preparation, tokenization, and LoRA fine-tuning were performed to align model responses with specific conversational styles.
Appa AI is an experimental language model fine-tuned on chat conversation history to mimic personal conversational nuances.
Key Features
Architecture & Infrastructure
Story & Developer Notes
Created to experiment with localized LLM fine-tuning techniques and personal AI assistant alignment.
LLaMA, Mistral fine-tuning research, and personal conversational assistant concepts.
- Dataset tokenization and formatting
- LoRA adapter training
- VRAM memory management in GPU training
- Resume training using cloud GPU instances (RunPod/Colab Pro)
- Quantize model for GGUF local inference
Information
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