Daniel Han
Unsloth AI
Verified Proof of Work Artifacts
2 items catalogedEach artifact below represents an authenticated research publication, production code repository, or technical architectural framework directly authored or co-created by Daniel Han. Every entry undergoes editorial source verification.
Manual Backpropagation Triton Kernel Derivations for Parameter-Efficient Cross-Entropy
Published mathematical explanations and Triton code demonstrating how computing gradient chain rules directly inside custom SRAM kernels avoids materializing multi-gigabyte intermediate tensors in PyTorch.
Custom Triton kernels require deep mathematical derivations for each distinct attention and normalization layer.
Unsloth: Fast and Memory-Efficient Open-Source LLM Fine-Tuning
Created an open-source library that rewrites PyTorch forward and backward passes into handwritten Triton kernels, supporting LoRA, QLoRA, and full fine-tuning with 0% accuracy loss while preventing out-of-memory errors.
Heavily optimized for NVIDIA Ampere, Ada Lovelace, and Hopper architectures; AMD ROCm support is emerging.