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Doula Isham Rashik Hasan

disham993

AI & ML interests

Machine Learning, Deep Learning, Natural Language Processing

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updated a Space about 4 hours ago

disham993/telecom-churn-dashboard

published a Space about 11 hours ago

disham993/telecom-churn-dashboard

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FutureOS/README

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updated a Space about 4 hours ago

Customer Churn Analysis - Interactive EDA

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Analyze customer churn data to identify patterns and insights

published a Space about 11 hours ago

Customer Churn Analysis - Interactive EDA

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Analyze customer churn data to identify patterns and insights

updated a Space 3 months ago

README

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published a Space 3 months ago

README

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reacted to onekq's post with 🔥 4 months ago

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🐋DeepSeek-v3-0324🐋 ranks higher than v3, now is tied with QwQ-32B
onekq-ai/WebApp1K-models-leaderboard

liked a model 5 months ago

yasserrmd/Text2SQL-1.5B

Text Generation • 2B • Updated Mar 10 • 357 • 7

upvoted an article 5 months ago

Article

Training and Finetuning Embedding Models with Sentence Transformers v3

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May 28, 2024

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reacted to m-ric's post with 🚀🔥👍 5 months ago

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Less is More for Reasoning (LIMO): a 32B model fine-tuned with 817 examples can beat o1-preview on math reasoning! 🤯

Do we really need o1's huge RL procedure to see reasoning emerge? It seems not.
Researchers from Shanghai Jiaotong University just demonstrated that carefully selected examples can boost math performance in large language models using SFT —no huge datasets or RL procedures needed.

Their procedure allows Qwen2.5-32B-Instruct to jump from 6.5% to 57% on AIME and from 59% to 95% on MATH, while using only 1% of the data in previous approaches.

⚡ The Less-is-More Reasoning Hypothesis:
‣ Minimal but precise examples that showcase optimal reasoning patterns matter more than sheer quantity
‣ Pre-training knowledge plus sufficient computational resources at inference levels up math skills

➡️ Core techniques:
‣ High-quality reasoning chains with self-verification steps
‣ 817 handpicked problems that encourage deeper reasoning
‣ Enough inference-time computation to allow extended reasoning

💪 Efficiency gains:
‣ Only 817 examples instead of 100k+
‣ 40.5% absolute improvement across 10 diverse benchmarks, outperforming models trained on 100x more data

This really challenges the notion that SFT leads to memorization rather than generalization! And opens up reasoning to GPU-poor researchers 🚀

Read the full paper here 👉 LIMO: Less is More for Reasoning (2502.03387)