Yehudit E.

Responsibilities:

• Download an LLM from Hugging Face, convert it into a llama.cpp-compatible format, and run it locally using llama.cpp.

• Study foundational Machine Learning and Deep Learning concepts, with an in-depth focus on Large Language Models (LLMs).

• Learn SYCL, emphasizing best practices for writing highly parallel and efficient code that fully exploits GPU capabilities.

• Implement the CONCAT operator for the SYCL backend in llama.cpp, including effective workload distribution across GPU hardware. PR-LINK: Implement CONCAT for SYCL beckend

• Implement the PADREFLECT1D operator for the SYCL backend in llama.cpp, including effective workload distribution across GPU hardware. PR-LINK: Implement PADREFLECT1D for SYCL beckend

• Study the Attention mechanism as described in “Attention Is All You Need” (2017).

• Study FlashAttention from the paper “FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness” (2022).

• Implement Flash Attention in llama.cpp for the SYCL backend, addressing development challenges and managing trade-offs intelligently to achieve maximum performance. PR-LINK: Implement FLASH_ATTN for SYCL beckend

• Measure, benchmark, and optimize the Flash Attention implementation, iterating toward improved throughput and efficiency