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[LG] MARLIN: Mixed-Precision Auto-Regressive Parallel Inference on Large Language Models E Frantar, R L. Castro, J Chen, T Hoefler, D Alistarh [ISTA & CITIC] (2024) https://t.co/WztVmKaeRq - This paper presents MARLIN and Sparse-MARLIN, highly optimized mixed-precision kernels… https://t.co/yr4H8wir0Y
Lots of great insights in @cursor_ai 's latest blog on how they modified diff format and speculative edits with fine-tuned Llama 70B to get a 4-5x speed up over GPT4-o! "fast-apply model surpasses GPT-4 and GPT-4o performance and pushes the pareto frontier on the accuracy /… https://t.co/hwz69JyJBH
Cursor is blowing up after Karpathy's post. They have a blog post talking about their secret: fast code diff generation. LLMs don't do well with code diffs so Cursor uses a modified diff format and speculative edits with fine-tuned Llama 70B to get a 4-5x speed up over GPT4-o! https://t.co/llwyVIq82f
Recent advancements in language model optimization have been highlighted through several new techniques that significantly enhance performance. A new efficiency technique has achieved an end-to-end speedup of up to 8.64 times and an 88.16% reduction in memory footprint for large language model (LLM) fine-tuning. Additionally, researchers from Carnegie Mellon University, Moffett AI, and Meta AI introduced 'MagicDec,' which provides up to a 2x speedup in LLaMA models for long-context applications by employing speculative decoding for high-throughput inference. Furthermore, Cursor's latest blog reveals their innovative approach to fast code diff generation, utilizing a modified diff format and speculative edits with a fine-tuned Llama 70B model, achieving a speedup of 4-5 times over GPT-4. These developments reflect ongoing efforts to improve the efficiency and effectiveness of AI language models.
