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Behrooz Azarkhalili

ermiaazarkhalili
Q-bert's profile picture victor's profile picture sheilagonzalo's profile picture
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AI & ML interests

LLMs, VLMs, PEFT, RL for LLMs and VLMs.

Recent Activity

liked a model about 21 hours ago
Qwen/Qwen3-235B-A22B-Instruct-2507
updated a model 1 day ago
ermiaazarkhalili/gemma-3-4b-it-grpo-gsm8k_16bit
replied to Kseniase's post 2 days ago
6 Essential Reads on core AI/ML topics: Time to look at some free useful resources that can help you upgrade your knowledge of AI and machine learning! Today we offer you these 6 must-read surveys that can be your perfect guides to the major fields and techniques: 1. Foundations of Large Language Models by Tong Xiao and Jingbo Zhu → https://arxiv.org/abs/2501.09223 Many recommend this 270-page book as a good resource to focus on fundamental concepts, such as pre-training, generative models, prompting, alignment, and inference 2. Large Language Models Post-Training: Surveying Techniques from Alignment to Reasoning -> https://huggingface.co/papers/2503.06072 Read this to master policy optimization (RLHF, DPO, GRPO), supervised and parameter-efficient fine-tuning, reasoning, integration, and adaptation techniques 3. Agentic Large Language Models, a survey by Leiden University → https://arxiv.org/abs/2503.23037 Surveys agentic LLMs across reasoning, tools, and multi-agent collaboration, highlighting their synergy. It also explores their promise, risks and applications in medicine, finance, science. 4. A Survey of Context Engineering for Large Language Models → https://huggingface.co/papers/2507.13334 Defines Context Engineering as systematic info design for LLMs beyond prompting, covering retrieval, processing, management, and architectures like RAG and multi-agent systems 5. A Survey of Generative Categories and Techniques in Multimodal Large Language Models → https://arxiv.org/abs/2506.10016 Covers multimodal models, exploring six generative modalities, key techniques (SSL, RLHF, CoT), architectural trends, and challenges 6. Large Language models for Time Series Analysis: Techniques, Applications, and Challenges → https://arxiv.org/abs/2506.11040 Explains how LLMs transform time series analysis by enhancing pattern recognition and long-term dependency handling + shows how to build them Also, subscribe to the Turing Post: https://www.turingpost.com/subscribe
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