Large Language Models are powerful, but their memory and computational requirements can make deployment challenging.
As part of my exploration of LLM optimization, I successfully worked on quantizing Qwen3-0.6B from FP32 to INT8 using Optimum-Quanto and PyTorch.
🔹 Base Model: Qwen/Qwen3-0.6B
🔹 Quantization: INT8 Weight Quantization
🔹 Framework: PyTorch & Optimum-Quanto
🔹 Model Integration: Hugging Face Transformers
🔹 Model Hosting: Hugging Face Hub
🔹 Inference: Google Colab with CUDA GPU
Key Implementations:
- Applied INT8 weight quantization.
- Saved and restored quantized model checkpoints.
- Published the quantized model on Hugging Face.
- Prepared a GPU-based inference workflow.
🤗 Hugging Face Model:
https://huggingface.co/dhivakarg/qwen3-0.6b-int8-quanto
This project strengthened my practical understanding of LLM optimization, model quantization, and efficient inference workflows.
Give it a try and share your feedback! I'd love to hear your thoughts.
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