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mechanisms; Try it: ollama run nous-hermes-llama2; Eric Hartford’s Wizard Vicuna 13B uncensored Llama 1 13B model fine-tuned to remove alignment; Try it: ollama run wizard-vicuna Example output comparisons We’ve tried running the 7B Llama 2 model against the 7B llama2 uncensored model with the same prompts. Below are the outputs. Disclaimer: Uncensored models carry their own risk. Please use them accordingly. Try running uncensored models yourself with Ollama: https://github.com/jmorganca/ollama

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uction). Consequently, much of the agent demo code focuses on parsing model output Cited as: Weng, Lilian. (Jun 2023). “LLM-powered Autonomous Agents”. Lil’Log. https://lilianweng.github.io/posts/2023-06-23-agent/. Or [1] Wei et al. “Chain of thought prompting elicits reasoning in large language models.” NeurIPS 2022 [2] Yao et al. “Tree of Thoughts: Dliberate Problem Solving with Large Language Models.” arXiv preprint arXiv:2305.10601 (2023). [3] Liu et al. “Chain of Hindsight Aligns Language M

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GitHub | Documentation | Paper LLMs promise to fundamentally change how we use AI across all industries. However, actually serving these models is challenging and can be surprisingly slow even on expensive hardware. Today we are excited to introduce vLLM, an open-source library for fast LLM inference and serving. vLLM utilizes PagedAttention, our new attention algorithm that effectively manages attention keys and values. vLLM equipped with PagedAttention redefines the new state of the art in LLM

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ging with LLMs at the core. While there isn’t a lot of formal education available on this topic yet, we pulled out some of the most useful resources we’ve found. Reference Build a GitHub support bot with GPT3, LangChain, and Python: One of the earliest public explanations of the modern LLM app stack. Some of the advice in here is dated, but in many ways it kicked off widespread adoption and experimentation of new AI apps; Building LLM applications for production: Chip Huyen discusses many of the

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onfidence measures to achieve good performance-efficiency tradeoffs Cited as: Weng, Lilian. (Jan 2023). Large Transformer Model Inference Optimization. Lil’Log. https://lilianweng.github.io/posts/2023-01-10-inference-optimization/. Or [1] Bondarenko et al. “Understanding and overcoming the challenges of efficient transformer quantization” ACL 2021. [2] Dettmers et al. “LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale” NeuriPS 2022 [3] Zadeh et al. “Gobo: Quantizing attention-bas

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mmend this post for more discussion on linearized models and lazy training. Cited as: Weng, Lilian. (Sep 2022). Some math behind neural tangent kernel. Lil’Log. https://lilianweng.github.io/posts/2022-09-08-ntk/. Or [1] Jacot et al. “Neural Tangent Kernel: Convergence and Generalization in Neural Networks.” NeuriPS 2018. [2]Radford M. Neal. “Priors for Infinite Networks.” Bayesian Learning for Neural Networks. Springer, New York, NY, 1996. 29-53. [3] Lee & Bahri et al. “Deep Neural Networks as G

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. Please sign up here if you'd like to help us test it. Here is what alpha users are saying about it: "The first thing me and all my friends noticed was how much faster it is than GitHub Copilot. It is at least 2x faster, maybe 3x. It's a little detail but it makes a big difference."; "It makes web development so much easier. I feel like I'm only writing 50% of the code."; "After using the feature for only week, I can't imagine life without it"; "It's crazy how much faster I can learn new things

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rationales) from 10,234 diverse TV Show and YouTube Lifestyle Vlog video clips Cited as: Weng, Lilian. (Jun 2022). Generalized visual language models. Lil’Log. https://lilianweng.github.io/posts/2022-06-09-vlm/. Or [1] Li et al. “VisualBERT: A Simple and Performant Baseline for Vision and Language.” arXiv preprint:1908.03557 (2019). [2] Wang et al. “SimVLM: Simple Visual Language Model Pretraining with Weak Supervision.” ICLR 2022. [3] Aghajanyan, et al. “CM3: A Causal Masked Multimodal Model o

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e high or the corruption transition matrix is not symmetric. Cited as: Weng, Lilian. (Apr 2022). Learning with not enough data part 3: data generation. Lil’Log. https://lilianweng.github.io/posts/2022-04-15-data-gen/. Or [1] Zhang et al. “Adversarial AutoAgument” ICLR 2020. [2] Kumar et al. “Data Augmentation using Pre-trained Transformer Models.” AACL 2020 Workshop. [3] Anaby-Tavor et al. “Not enough data? Deep learning to rescue!” AAAI 2020. [4] Wang et al. “Want To Reduce Labeling Cost? GPT-3

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etter in time, the threshold $\delta$ decays in time as well Cited as: Weng, Lilian. (Feb 2022). Learning with not enough data part 2: active learning. Lil’Log. https://lilianweng.github.io/posts/2022-02-20-active-learning/. Or [1] Burr Settles. Active learning literature survey. University of Wisconsin, Madison, 52(55-66):11, 2010. [3] Yang et al. “Cost-effective active learning for deep image classification” TCSVT 2016. [4] Yarin Gal et al. “Dropout as a Bayesian Approximation: representing mo

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eudo label distribution to reduce the class overlap Cited as: Weng, Lilian. (Dec 2021). Learning with not enough data part 1: semi-supervised learning. Lil’Log. https://lilianweng.github.io/posts/2021-12-05-semi-supervised/. Or [1] Ouali, Hudelot & Tami. “An Overview of Deep Semi-Supervised Learning” arXiv preprint arXiv:2006.05278 (2020). [2] Sajjadi, Javanmardi & Tasdizen “Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning.” arXiv preprint arXiv:

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utation, constant buffer size and on-the-fly memory defragmentation. Cited as: Weng, Lilian. (Sep 2021). How to train really large models on many GPUs? Lil’Log. https://lilianweng.github.io/posts/2021-09-25-train-large/. Or [1] Li et al. “PyTorch Distributed: Experiences on Accelerating Data Parallel Training” VLDB 2020. [2] Cui et al. “GeePS: Scalable deep learning on distributed GPUs with a GPU-specialized parameter server” EuroSys 2016 [3] Shoeybi et al. “Megatron-LM: Training Multi-Billion P

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I datasets, IS-BERT produces results comparable with SBERT (See Fig. 25 & 30). Cited as: Weng, Lilian. (May 2021). Contrastive representation learning. Lil’Log. https://lilianweng.github.io/posts/2021-05-31-contrastive/. Or [1] Sumit Chopra, Raia Hadsell and Yann LeCun. “Learning a similarity metric discriminatively, with application to face verification.” CVPR 2005. [2] Florian Schroff, Dmitry Kalenichenko and James Philbin. “FaceNet: A Unified Embedding for Face Recognition and Clustering.” CV

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studying the risk of neural toxic degeneration in language models. [Download] Cited as: Weng, Lilian. (Mar 2021). Reducing toxicity in language models. Lil’Log. https://lilianweng.github.io/posts/2021-03-21-lm-toxicity/. Or [1] Vidgen, et al. “Challenges and frontiers in abusive content detection.” Workshop on Abusive Language Online 2019. [2] Zampieri et al. “Predicting the type and target of offensive posts in social media.” NAACL 2019. [3] Vidgen & Deczynski. “Directions in abusive language t

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and masked. The task is to predict this masked salient span Cited as: Weng, Lilian. (Oct 2020). How to build an open-domain question answering system? Lil’Log. https://lilianweng.github.io/posts/2020-10-29-odqa/. Or SQuAD 2.0: the Stanford QA dataset; RACE: a reading comprehension dataset collected from English Examinations that are created for middle school and high school students; TREC QA: the TREC QA collections; MS MARCO: a QA dataset featuring 100,000 real Bing questions and a human gener

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th a random choice (e.g. “swap the output address” or “change the value of a constant”) Cited as: Weng, Lilian. (Aug 2020). Neural architecture search. Lil’Log. https://lilianweng.github.io/posts/2020-08-06-nas/. Or [1] Thomas Elsken, Jan Hendrik Metzen, Frank Hutter. “Neural Architecture Search: A Survey” JMLR 20 (2019) 1-21. [2] Kenneth O. Stanley, et al. “Designing neural networks through neuroevolution” Nature Machine Intelligence volume 1, pages 24–35 (2019). [3] Kenneth O. Stanley & Risto

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atent skill variable. See my previous post for more details. Cited as: Weng, Lilian. (Jun 2020). Exploration strategies in deep reinforcement learning. Lil’Log. https://lilianweng.github.io/posts/2020-06-07-exploration-drl/. Or [1] Pierre-Yves Oudeyer & Frederic Kaplan. “How can we define intrinsic motivation?” Conf. on Epigenetic Robotics, 2008. [2] Marc G. Bellemare, et al. “Unifying Count-Based Exploration and Intrinsic Motivation”. NIPS 2016. [3] Georg Ostrovski, et al. “Count-Based Explorat

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xt{RL}(\theta \vert \pi \text{mm}) + \lambda \mathcal{L} \text{mm}(\theta)$. Cited as: Weng, Lilian. (Jan 2020). Curriculum for reinforcement learning. Lil’Log. https://lilianweng.github.io/posts/2020-01-29-curriculum-rl/. Or [1] Jeffrey L. Elman. “Learning and development in neural networks: The importance of starting small.” Cognition 48.1 (1993): 71-99. [2] Yoshua Bengio, et al. “Curriculum learning.” ICML 2009. [3] Daphna Weinshall, Gad Cohen, and Dan Amir. “Curriculum learning by transfer l

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l the interesting findings above, it should be pretty fun to reproduce them. Some results are easily to reproduce than others. Details are described below. My code is available on github lilianweng/generalization-experiment. This is the trickiest one to reproduce. The authors did give me a lot of good advice and I appreciate it a lot. Here are a couple of noticeable settings in their experiments: There are no regularization terms like weight decay, dropout; In Fig 3, the training set contains 4k

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same type may vary a lot across multiple robots. Batch normalization is applied to fix it by normalizing every dimension across samples in one minibatch. D4PG [paper|code (Search “github d4pg” and you will see a few.)] Distributed Distributional DDPG (D4PG) applies a set of improvements on DDPG to make it run in the distributional fashion. (1) Distributional Critic: The critic estimates the expected Q value as a random variable a distribution $Z w$ parameterized by $w$ and therefore $Q w(s, a) =

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