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AtAndDev
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LLMs, Alignment, Merging, Unsloth, DPO, SFT, ORPO, SPIN..
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System Prompt Learning: Teaching LLMs to Learn Problem-Solving Strategies from Experience
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New GUI model by Salesforce AI & Uni HK: Jedi
tianbaoxiexxx/Jedi xlangai/Jedi-7B-1080p 🤗
Based on Qwen2.5-VL with Apache 2.0 license
prompt with below screenshot → select "find more"
tianbaoxiexxx/Jedi xlangai/Jedi-7B-1080p 🤗
Based on Qwen2.5-VL with Apache 2.0 license
prompt with below screenshot → select "find more"
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L playlist sorry
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HALOOO MY COMMUNITY
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✂️ AutoAbliteration
I made a Colab notebook to automatically abliterate models.
It's quite general, so you can do interesting stuff like blocking a given language in the model outputs.
💻 Colab: https://colab.research.google.com/drive/1RmLv-pCMBBsQGXQIM8yF-OdCNyoylUR1?usp=sharing
I made a Colab notebook to automatically abliterate models.
It's quite general, so you can do interesting stuff like blocking a given language in the model outputs.
💻 Colab: https://colab.research.google.com/drive/1RmLv-pCMBBsQGXQIM8yF-OdCNyoylUR1?usp=sharing
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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?
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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