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Recent advancements in Large Language Models (LLMs) have shown significant improvements in mathematical reasoning tasks. Tiny LLMs, such as Llama8B, achieved a 96.7% score on the GSM8K math benchmark, surpassing GPT-4, Claude, and Gemini, despite having 200 times fewer parameters. This success is attributed to techniques such as Monte Carlo Tree Search (MCTS) and backpropagation, similar to those used by Google to solve Go. Additionally, vLLM now supports FP8 quantization, optimizing performance and efficiency. Open-source LLMs like Qwen 2 and Nemotron are rapidly advancing, with fine-tunes expected to match top models like Gemini and GPT-4 turbo. Llama-3 70b was replaced in weeks.
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LLMs Combined With Other Techniques Will Be The Next Big Thing! This paper shows that Large Language Models (LLMs) combined with Monte Carlo Tree Search (MCTS) enhances the performance of complex mathematical reasoning tasks considerably! In fact a small 8b Llama-3 beats… https://t.co/saxrbgPMNO
It’s amazing how much headway open source LLMs have made over the last year! Every two months we have a new SOTA LLM. Qwen 2 and Nemotron replaced Llama-3 70b in weeks! Fine-tunes of Qwen-2 will catch up to Gemini and Llama 400b will catch up to GPT-4 turbo in the coming…
