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Oguzhan Ozcelik

ogozcelik
ucsahin's profile picture serapcokuysal's profile picture yusufgurel's profile picture
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https://ogozcelik.github.io/

AI & ML interests

NLP Engineer

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upvoted a collection 4 days ago
Türkçe VLMler
reacted to Kseniase's post with 👍 7 days ago
9 types of "Chain-of-..." approaches: Chain-of-Thought (CoT) prompting enhances reasoning in AI models by breaking down complex problems into step-by-step logical sequences. It continues proving its effectiveness, especially in top-performing reasoning models. However, there are other similar methods, that expand CoT and can be used for different purposes. Here are 9 of them: 1. Chain-of-Action-Thought (COAT) -> https://huggingface.co./papers/2502.02508 Helps model decide when to keep thinking, double-check their work, or try a different approach, using special guiding tokens. 2. Chain of Draft (CoD) -> https://huggingface.co./papers/2502.18600 It helps model generate short but meaningful reasoning steps, cutting costs and making processing faster 3. Chain-of-Agents -> https://huggingface.co./papers/2406.02818 Uses multi-agent collaboration: Worker agents process text parts in a structured chain, and manager agent summarizes the results 4. Chain-of-RAG ->https://huggingface.co./papers/2501.14342 Creates retrieval chains, instead of retrieving all info at once. It can dynamically adjust its search process and its parameters like step number 5. Chain-of-Shot Prompting (CoS) -> https://huggingface.co./papers/2502.06428 Helps models pick frames crucial for understanding a video, using a binary video summary and video co-reasoning module. 6. Chain of Hindsight (CoH) -> https://huggingface.co./papers/2302.02676 Converts all feedback into sequences to fine-tune the model and refine outputs 7. Chain-of-Note (CoN) -> https://huggingface.co./papers/2311.09210 Generates sequential reading notes for each retrieved document to assess relevance before integrating info into the final answer 8. Chain of Diagnosis (CoD) -> https://huggingface.co./papers/2407.13301 Transforms the diagnostic process into a diagnostic chain 9. Chain(s)-of-Knowledge -> https://www.turingpost.com/p/cok Enhance LLMs by dynamically pulling in external knowledge to improve accuracy and reduce errors
upvoted an article about 1 month ago
Why we (don't) need export control
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upvoted a collection 4 days ago
reacted to Kseniase's post with 👍 7 days ago
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9 types of "Chain-of-..." approaches:

Chain-of-Thought (CoT) prompting enhances reasoning in AI models by breaking down complex problems into step-by-step logical sequences. It continues proving its effectiveness, especially in top-performing reasoning models. However, there are other similar methods, that expand CoT and can be used for different purposes. Here are 9 of them:

1. Chain-of-Action-Thought (COAT) -> Satori: Reinforcement Learning with Chain-of-Action-Thought Enhances LLM Reasoning via Autoregressive Search (2502.02508)
Helps model decide when to keep thinking, double-check their work, or try a different approach, using special guiding tokens.

2. Chain of Draft (CoD) -> Chain of Draft: Thinking Faster by Writing Less (2502.18600)
It helps model generate short but meaningful reasoning steps, cutting costs and making processing faster

3. Chain-of-Agents -> Chain of Agents: Large Language Models Collaborating on Long-Context Tasks (2406.02818)
Uses multi-agent collaboration: Worker agents process text parts in a structured chain, and manager agent summarizes the results

4. Chain-of-RAG ->https://huggingface.co./papers/2501.14342
Creates retrieval chains, instead of retrieving all info at once. It can dynamically adjust its search process and its parameters like step number

5. Chain-of-Shot Prompting (CoS) -> CoS: Chain-of-Shot Prompting for Long Video Understanding (2502.06428)
Helps models pick frames crucial for understanding a video, using a binary video summary and video co-reasoning module.

6. Chain of Hindsight (CoH) -> Chain of Hindsight Aligns Language Models with Feedback (2302.02676)
Converts all feedback into sequences to fine-tune the model and refine outputs

7. Chain-of-Note (CoN) -> Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Models (2311.09210)
Generates sequential reading notes for each retrieved document to assess relevance before integrating info into the final answer

8. Chain of Diagnosis (CoD) -> CoD, Towards an Interpretable Medical Agent using Chain of Diagnosis (2407.13301)
Transforms the diagnostic process into a diagnostic chain

9. Chain(s)-of-Knowledge -> https://www.turingpost.com/p/cok
Enhance LLMs by dynamically pulling in external knowledge to improve accuracy and reduce errors
upvoted an article about 1 month ago
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OmniVision-968M: a new local VLM for edge devices, fast & small but performant
💨 a new vision language model with 9x less image tokens, super efficient
📖 aligned with DPO for reducing hallucinations
⚡️ Apache 2.0 license 🔥

Demo hf.co/spaces/NexaAIDev/omnivlm-dpo-demo
Model https://huggingface.co./NexaAIDev/omnivision-968M
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🚀 Introducing TraVisionLM: Turkish Visual Language Model - The First of Its Kind! 🇹🇷🖼️

I'm thrilled to share TraVisionLM on Hugging Face! With 875M parameters, this lightweight, efficient model handles Turkish instructions for image inputs. Fully compatible with the Transformers library, it’s easy to load, fine-tune, and use—no external libraries needed!

Developed solo, TraVisionLM is a strong foundation for low-resource language research. While still improving, it's a key step for Turkish-language AI. Your feedback is welcome as I refine the model.

🎉 Explore it now:

- Model: ucsahin/TraVisionLM-base
- Demo: https://huggingface.co./spaces/ucsahin/TraVisionLM-Turkish_Visual_Language_Model
- Object Detection Finetune: ucsahin/TraVisionLM-Object-Detection-ft

Let’s push Turkish visual language processing forward!

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🚀 TraVisionLM: Türünün İlk Örneği Türkçe Görsel Dil Modelini Sunuyorum! 🇹🇷🖼️

TraVisionLM modelini Hugging Face'te yayınladım! 875M parametre ile bu hafif ve verimli model, görüntüye dayalı Türkçe talimatları işlemek için tasarlandı. Transformers kütüphanesiyle tamamen uyumlu, yüklemesi, eğitmesi ve kullanması çok kolay—dış kütüphane gerekmez!

Tek başıma geliştirdiğim TraVisionLM, düşük kaynaklı dillerde araştırmalar için sağlam bir temel sunuyor. Geliştirmeye devam ederken geri bildirimlerinizi bekliyorum.

🎉 Hemen keşfedin:

- Model: ucsahin/TraVisionLM-base
- Demo: https://huggingface.co./spaces/ucsahin/TraVisionLM-Turkish_Visual_Language_Model
- Obje Tespiti İnce Ayarı: ucsahin/TraVisionLM-Object-Detection-ft

Türkçe görsel dil işleme sınırlarını birlikte zorlayalım!
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liked a model 7 months ago