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GitHub - unslothai/unsloth: Fine-tuning & Reinforcement Learning for LLMs. ?? Train OpenAI gpt-oss, DeepSeek-R1, Qwen3, Gemma 3, TTS 2x faster with 70% less VRAM.
| | blog.ml.cmu.edu
RLHF 101: A Technical Tutorial on Reinforcement Learning from Human Feedback - Machine Learning Blog | ML@CMU | Carnegie Mellon University
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| | The latest news and publications regarding machine learning, artificial intelligence or related, brought to you by the Machine Learning Blog, a spinoff of the Machine Learning Department at Carnegie Mellon University.
| | www.confident-ai.com
The Ultimate Guide to Fine-Tune LLaMA 3, With LLM Evaluations - Confident AI
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| | In this article, we'll walkthrough how to fine-tune and evaluate a LLaMA-2 model using Hugging Face and DeepEval
| | blog.paperspace.com
Finetune Multimodel LLM:IDEFICS 9B using A100
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| | In this article, we will learn how to make predictions using the 4-bit quantized ?? Idefics-9B model and fine-tune it on a specific dataset.
| | neuralnetworksanddeeplearning.com
Neural networks and deep learning
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| [AI summary] The text provides an in-depth explanation of the backpropagation algorithm in neural networks. It starts by discussing the concept of how small changes in weights propagate through the network to affect the final cost, leading to the derivation of the partial derivatives required for gradient descent. The explanation includes a heuristic argument based on tracking the perturbation of weights through the network, resulting in a chain of partial derivatives. The text also touches on the historical context of how backpropagation was discovered, emphasizing the process of simplifying complex proofs and the role of using weighted inputs (z-values) as intermediate variables to streamline the derivation. Finally, it concludes with a citation and licens...