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alkinun

AtAndDev

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LLMs, Alignment, Merging, Unsloth, DPO, SFT, ORPO, SPIN..

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replied to merve's post about 2 hours ago
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More like sir cartier cash carti yung carti king vamp carti baby boi guapo

upvoted an article 3 days ago
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System Prompt Learning: Teaching LLMs to Learn Problem-Solving Strategies from Experience

By codelion โ€ข
โ€ข 10
reacted to merve's post with ๐Ÿ”ฅ 3 days ago
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1100
New GUI model by Salesforce AI & Uni HK: Jedi
tianbaoxiexxx/Jedi xlangai/Jedi-7B-1080p ๐Ÿค—
Based on Qwen2.5-VL with Apache 2.0 license

prompt with below screenshot โ†’ select "find more"
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replied to merve's post 3 days ago
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HALOOO MY COMMUNITY
posted an update 9 days ago
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2667
deepseek-ai/DeepSeek-R1-0528

This is the end
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reacted to mlabonne's post with ๐Ÿš€๐Ÿ˜Žโค๏ธ๐Ÿ”ฅ๐Ÿ‘ 9 days ago
reacted to codelion's post with ๐Ÿ”ฅ 9 days ago
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Introducing AutoThink: Adaptive reasoning for LLMs that improves performance by 43% on reasoning benchmarks!

Instead of using fixed thinking budgets, AutoThink:
- Classifies query complexity (HIGH/LOW) using adaptive classification
- Dynamically allocates thinking tokens based on complexity
- Uses steering vectors derived from Pivotal Token Search to guide reasoning patterns

Results on DeepSeek-R1-Distill-Qwen-1.5B:
- GPQA-Diamond: 31.06% vs 21.72% baseline (+9.34 points)
- MMLU-Pro: 26.38% vs 25.58% baseline (+0.8 points)
- Uses fewer tokens than baseline approaches

Works with any local reasoning model - DeepSeek, Qwen, Llama, custom models. The technique combines our research on Pivotal Token Search (PTS) implementation and adaptive classification frameworks.

Paper: AutoThink: efficient inference for reasoning LLMs
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5253327

Code and examples:
https://github.com/codelion/optillm/tree/main/optillm/autothink

PTS implementation and technical details:
https://github.com/codelion/pts
https://huggingface.co/blog/codelion/pts

Adaptive classifier framework:
https://github.com/codelion/adaptive-classifier

Would love to hear your thoughts on adaptive resource allocation for LLM reasoning! Have you experimented with similar approaches?
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