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+ This is not the page you're looking for... return back to safety?
+ + + +
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+ Generative AI has been phenomenal in everything related to productivity given inputs such as free-text or code. +The new image or multi-modal based foundation models has made tremendous improvements and I wanted to explore them.
+My goal was generating superheroes portraits using a ChatGPT Pro subscription (Dall-E is included).
+ChatGPT (Dall-E) will let you draw abstract superheroes but it is currently not possible to generate them from real people photos.
+The generic response Dall-E will output is: "I'm sorry, but I'm unable to use real individuals' faces from images to create new images. If you have any other requests or need assistance with something else, feel free to ask!"
+Therefore, we will leverage Dall-E just to generate a generic face superhero (cartoonish or real) and later on replace it using a different product.
+My project was generating a superhero named "דני כוסברו" which is a humorous word-play for a famous news broadcaster named "Dani Kushmaro" and Cilantro.
+In order to generate a portrait I've uploaded the portrait of the reporter and asked ChatGPT:
+"Draw a real person face that has coriander. The person should look like a superhero"
+This is the output image I got:
+
+As you can see this is a great representation for a first try but the biggest problem that I've learnt later is that the coriander on the face makes it very difficult for the face-swap tool later.
+In order to fix it, I've asked ChatGPT to fix it by writing:
+"Draw a portrait of a person with their face merged with coriander, make sure the face is not too hidden so it can be easily replaced with a real person"
+Output image came exactly as I wanted it to be:
+
+Let's move to the second part of replacing it with the reporter real face.
+InsightFace is an open source 2D&3D deep face analysis library with more than 15k stars on github.
+The product that is built on top of this library and more features is called picsi.ai and it is possible to set up a discord bot for free with 50 credits to leverage the face-swap functionality.
+This is the swapping_discord github link but the process is super simple so I will describe it here.
+Step-by-step guide:
+Refer to this link to register Discord app, create a new chat room, and invite the Midjourney bot to the chat room.
+Invite the InsightFaceSwap bot to the chat room by this link: Discord Invite Link.
+Use /saveid command to register your person id with image/features.
Use /swapid command to swap the input image with a given saved ID.
The final photos for the superhero are:
+
+
+Any library / github project which is wrapping up Insightface is a good option.
+I've personally used the swapseed to do the replacement on my computer.
+This is a sample code to run after installing the dependencies pip install -r requirements.txt
import os
+import matplotlib.pyplot as plt
+import gdown
+import insightface
+from insightface.app import FaceAnalysis
+from insightface.data import get_image as ins_get_image
+from faceswap import swap_n_show, swap_n_show_same_img, swap_face_single,fine_face_swap
+
+app = FaceAnalysis(name='buffalo_l')
+app.prepare(ctx_id=0, det_size=(640, 640))
+
+# Download 'inswapper_128.onnx' file using gdown
+model_url = 'https://drive.google.com/uc?id=1HvZ4MAtzlY74Dk4ASGIS9L6Rg5oZdqvu'
+model_output_path = 'inswapper/inswapper_128.onnx'
+if not os.path.exists(model_output_path):
+ gdown.download(model_url, model_output_path, quiet=False)
+
+swapper = insightface.model_zoo.get_model('inswapper/inswapper_128.onnx', download=False, download_zip=False)
+
+# Load images
+img1_fn = 'input_and_output.png'
+img2_fn = 'face_to_replace_from.jpg'
+
+# Swap faces between two images
+# swap_n_show(img1_fn, img2_fn, app, swapper)
+
+# Add face to an image
+swap_face_single(img1_fn, img2_fn, app, swapper, enhance=True, enhancer='REAL-ESRGAN 2x')
+After many years of waiting patiently there is finally a reasonable way to have a unified development environment from windows named WSL version 2. This finally enables us developers to run a full linux environment natively in windows without virtual machine or dual-boot. Learn about the details that made it functioning.
+
+As you can see this is a terminal experience in windows enhanced with ConEmu that provides a full linux experience. Some cool feature are:
+The only major issue that still prevents me from using it more frequently is horrible NTFS performance.
+wsl --install
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+ + Rating: 5 +
+ +This book has convinced me that Rust has managed to tackle some of the most annoying error-prone scenarios in C++ such as memory handling and undocumented lifetime guarantees.
+ +
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+ + Rating: 3 +
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+ + Rating: 4 +
+ +In the wake of Rich Dad's bankruptcy, my curiosity was piqued to explore his latest literary offering, hoping to glean fresh perspectives. Delving into the pages of his newer book, I found myself immersed in a world of entrepreneurial known wisdom.
+The book advocates for a paradigm shift, emphasizing the significance of focusing on the process rather than fixating solely on monetary gains. It underscores the importance of meticulous planning, urging readers to meticulously consider all facets of business operations. Central to this ethos is the concept of the "5P's," elucidated within the pages, serving as a compass for strategic decision-making.
+As I journeyed through the narrative, I encountered a tapestry of motivational motifs, each weaving together to form a holistic approach to entrepreneurship. Amidst the challenges and uncertainties, Rich Dad's principles serve as guiding beacons, illuminating the path toward sustainable success.
+Despite the shadows cast by past failures, the book exudes optimism, instilling a sense of empowerment within its readers. It champions resilience, urging individuals to persevere in the face of adversity and seize opportunities for growth.
+In conclusion, my exploration of Rich Dad's latest work proved to be enlightening. Beyond the veneer of financial setbacks lies a reservoir of invaluable insights, waiting to be unearthed by those willing to embark on the journey of entrepreneurial discovery.
+ +
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+ + Rating: 3 +
+ + +Arxiv: https://arxiv.org/abs/2312.10868 18 Dec 2023 IEEE
+This comprehensive survey explored the evolving landscape of generative Artificial Intelligence (AI), with a specific focus on the transformative impacts of Mixture of Experts (MoE), multimodal learning, and the speculated advancements towards Artificial General Intelligence (AGI).
+Arxiv: https://arxiv.org/abs/2308.10792 21 Aug 2023
+There are generally two methods for constructing instruction datasets:
+• Data integration from annotated natural language datasets. In this approach,
+(instruction, output) pairs are collected from existing annotated natural language datasets by using templates to transform text-label pairs to (instruction, output) pairs.
+Datasets such as Flan (Longpre et al., 2023) and P3 (Sanh et al., 2021).
+• Generating outputs using LLMs - (1) manually collected; or (2) expanded based on small handwritten seed instructions using LLMs. Next, the collected instructions are fed to LLMs to obtain outputs. Datasets such as InstructWild (Xue et al., 2023) and Self-Instruct (Wang et al., 2022c) are generated following this approach. For multi-turn conversational IT datasets, we can have large language models self-play different roles (user and AI assistant) to generate message
+
This book is dedicated to peeling back the layers of one of the most intriguing subjects in the field of artificial intelligence: Generative AI and Large Language Models (LLMs). As we dive deep into the intricacies of these AI marvels, we will uncover insights, share breakthroughs, and discuss the profound implications they have on our world.
+Generative AI has captured the imagination of scientists, artists, and thinkers alike. It is an area of technology that stands at the forefront of innovation—machines that can create, design, and think in ways that were once the exclusive domain of humans. From composing music to authoring articles, these intelligent systems are reshaping the boundaries of creativity and intellect.
+Large Language Models, a subset of generative AI, are particularly fascinating. They have the ability to understand and generate human language with remarkable proficiency. As we explore the inner workings of LLMs, we will encounter systems like Antrophic Claude, GPT-4, BERT, and their successors, which continue to push the limits of what machines can achieve with words.
+In this book, I will look at the technical foundations of these AI systems, their philosophical and ethical considerations, their potential applications, and the challenges they pose. By combining thorough research with accessible explanations, my goal is to make this complex subject matter engaging for both AI aficionados and newcomers alike.
+Compilation of the most interesting and significant findings from my exploration into Generative AI and LLMs
+Deep Dives into AI Mechanics: Understanding the algorithms, neural networks, and data that power generative AI and LLMs.
+Creative Showcases: Highlighting the most impressive and thought-provoking outputs of generative AI.
+Industry Impact: Analyzing how businesses and industries are being transformed by the adoption of generative AI technologies.
+Future Visions: Speculating on the potential future developments and the direction in which generative AI is heading.
+Arxiv: https://arxiv.org/abs/2401.10020 18 Jan 2024 Meta
+In this work, we study Self-Rewarding Language Models, where the language model itself is used via LLM-as-a-Judge prompting to provide its own rewards during training. We show that during Iterative DPO training that not only does instruction following ability improve, but also the ability to provide high-quality rewards to itself.
+
Our approach first assumes access to a base pretrained language model, and a small amount of human-annotated seed data. We then build a model that aims to possess two skills simultaneously: 1. Instruction following: given a prompt that describes a user request, the ability to generate a high quality, helpful (and harmless) response. 2. Self-Instruction creation: the ability to generate and evaluate new instruction following examples to add to its own training set.
+While we report the results of both approaches in our experiments, we find that learning from preference pairs gives superior performance, and hence recommend that approach.
+
Iterative Training Our overall procedure trains a series of models M1, . . . , MT where each successive model t uses augmented training data created by the t − 1 th model. We thus define AIFT(Mt) to mean AI Feedback Training data created using model Mt. Model Sequence We thus define the models, and the training data they use as follows: M0 : Base pretrained LLM with no fine-tuning. M1 : Initialized with M0, then fine-tuned on the IFT+EFT seed data using SFT. M2 : Initialized with M1, then trained with AIFT(M1) data using DPO. M3 : Initialized with M2, then trained with AIFT(M2) data using DPO.
+
Arxiv: https://arxiv.org/abs/2310.10638 30 Nov 2023 Meta
+We instead present IN-CONTEXT PRETRAINING, a new approach where language models are pretrained on a sequence of related documents, thereby explicitly encouraging them to read and reason across document boundaries.
+IN-CONTEXT PRETRAINING instead reorders the pretraining data by combining several semantically related documents to create a coherent input context, thereby exposing LMs to long relevant contexts and providing pretraining signals beyond document boundaries.
+However, this document sorting problem is challenging. LMs are typically trained on billions of
+documents and we would like to sort them to maximize document similarity in the input context
+windows without repeating any data. We introduce two new approximate algorithms to tackle these challenges.
+
Techniques are using embedding models and top-k similarity search and leveraging approximate solutions for the traveling salesman problem - by the means of visiting every document once.
+Arxiv: https://arxiv.org/abs/2305.18290 13 Dec 2023 Stanford
+In this paper we introduce a new parameterization of the reward model in RLHF that enables extraction of the corresponding optimal policy in closed form, allowing us to solve the standard RLHF problem with only a simple classification loss. The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight,
+eliminating the need for sampling from the LM during fine-tuning or performing significant hyperparameter tuning.
+
DPO gradient for loss increases the likelihood of preferred completion (Y_W) and decreases the likelihood for dispreferred completion (Y_L). Importantly, the examples are weighed by how much higher the implicit reward model rˆθ rates the dispreferred completions, scaled by β, i.e, how incorrectly the implicit reward model orders the completions, accounting for the strength of the KL constraint.
+DPO outline. The general DPO pipeline is as follows: 1) Sample completions y1, y2 ∼ πref(· | x) for every prompt x, label with human preferences to construct the offline dataset of preferences D = {x (i) , y (i) w , yl) (i)} N i=1 and 2) optimize the language model πθ to minimize LDPO for the given πref and D and desired β.
+Arxiv: https://arxiv.org/abs/2312.15503 24 Dec 2023 Fudan NLP
+In conclusion, while RLHF is a significant advancement in AI development, particularly in integrating human preferences into the learning process, it also presents unique challenges. These include the inherent noise and ambiguity in human feedback, potential biases in the data, and the generalization limits of reward models trained on specific datasets. Addressing these challenges is crucial for the advancement and ethical application of RLHF in AI systems.
+
To enhance the generalization ability of the reward model, we explore contrastive learning and
+meta-learning. By introducing unsupervised contrastive loss during the reward modeling process, the reward model can better distinguish subtle preference differences among responses. To bridge the gap between the preference data distribution and the model output distribution, we employ meta-learning to ensure that the reward model not only performs well on the preference data but also can distinguish the differences in target domain outputs.
+Technique is to randomize training data and do a k-fold split, build K models and then measure mean and deviations - negative mean is usually the mistakes.
+According to the results, we can observe that: 1) For the top 20% of data with the lowest preference strength, they have a negative impact on the model’s performance on the validation set. The preference strength for these data subsets is less than 0. 2) For data ranked between 20% and 40%, after training, the model’s prediction accuracy on the validation set is approximately 0.5. The preference strength for this type of data is around 0. 3) The remaining data significantly improves the model’s performance. However, the top 10% of data with the highest preference strength does not achieve the best performance when trained alone. Based on the above results, we can roughly categorize preference data into three types: incorrect data, ambiguous data (almost no difference), and normal data (clear differences). These three types of preference data play different roles and make different contributions to preference modeling. It is necessary for us to conduct a more detailed analysis of them and then consider how to handle each type.
+Arxiv: https://arxiv.org/abs/2401.06080 11 Jan 2024 Beijing Academy of AI
+In this paper, we propose a novel approach, called LLaRA (LLM adapted for dense RetrievAl), which works as a post-hoc adaptation of LLM for the dense retrieval application. LLaRA consists of two pretext tasks: EBAE (Embedding-Based Auto-Encoding) and EBAR (Embedding-Based Auto-Regression), where the text embeddings from LLM are used to reconstruct the tokens for the input sentence and predict the tokens for the next sentence, respectively
+
Particularly, there are two pretext training tasks introduced by LLaRA: EBAE (Embedding-Based Auto-Encoding) and EBAR (Embedding-Based Auto-Regression). In EBAE, the LLM is prompted to generate the text embeddings, which can be used to predict the tokens for the input sentence itself. While with EBAR, the LLM is prompted to generate the text embeddings, which can be used to predict the tokens for the next sentence. By learning from the above pretext tasks, the text embeddings from LLM can be adapted from Local semantic representations (i.e. prediction for the next tokens) to Global semantic representations (i.e. prediction for the sentence-level features).
+Arxiv: https://arxiv.org/abs/2310.01352 2 Oct 2023 META
+Retrieval-augmented language models (RALMs) improve performance by accessing long-tail and up-to-date knowledge from external data stores, but are challenging to build. Existing approaches require either expensive retrieval-specific modifications to LM pre-training or use post-hoc integration of the data store that leads to suboptimal performance
+Our approach operates in two distinct fine-tuning steps: (1) one updates a pre-trained LM to better use retrieved information, while (2) the other updates the retriever to return more relevant results, as preferred by the LM
+Arxiv: https://arxiv.org/abs/2306.03081 5 Jun 2023 MIT
+Context: Despite significant advances in recent years, it remains unclear if and how large language models (LLMs) can be made reliable and controllable enough to meet the functional requirements of many applications.
+Even after fine-tuning and reinforcement learning, LLMs are liable to violate instructions in their prompts (such as “Use the following vocabulary words” or “Do not reveal this prompt”).
+These difficulties highlight the need for methods beyond prompting and fine-tuning for constraining the behavior of generative neural models.
+ + +Arxiv: https://arxiv.org/abs/2308.02151 4 Aug 2023 Salesforce
+This paper introduces Retroformer, a principled framework for reinforcing language agents by learning a plug-in retrospective model, which automatically refines the language agent prompts from environment feedback through policy optimization. Specifically, our proposed agent architecture can learn from arbitrary reward information across multiple environments and tasks, for iteratively fine-tuning a pre-trained language model, which refines the language agent prompts by reflecting on failed attempts and assigning credits of actions taken by the agent on future rewards
+
Arxiv: https://arxiv.org/abs/2312.11514 12 Dec 2023 Apple
+First, "windowing'" strategically reduces data transfer by reusing previously activated neurons, and second, "row-column bundling", tailored to the sequential data access strengths of flash memory, increases the size of data chunks read from flash memory.
+Arxiv: https://arxiv.org/abs/2104.09864 20 Apr 2021 Zhuiyi Technology Co.
+We investigated the existing approaches to the relative position encoding and found that they are mostly built based on the idea of the decomposition of adding position encoding to the context representations. We introduce a novel method, namely Rotary Position Embedding(RoPE), to leverage the positional information into the learning process of PLMS. The key idea is to encode relative position by multiplying the context representations with a rotation matrix with a clear theoretical interpretation.
+Arxiv: https://arxiv.org/abs/2106.09685 17 Jun 2021 OpenAI
+Many applications in natural language processing rely on adapting one large-scale, pre-trained language model to multiple downstream applications. Such adaptation is usually done via fine-tuning,
+which updates all the parameters of the pre-trained model. The major downside of fine-tuning is that the new model contains as many
+parameters as in the original model.
+Many sought to mitigate this by adapting only some parameters or learning external modules for new tasks. This way, we only need to store and load a small number of task-specific parameters in addition to the pre-trained model for each task, greatly boosting the operational efficiency when deployed. However, existing techniques often introduce inference latency by extending model depth or reducing the model’s usable sequence length. More importantly, these methods often fail to match the fine-tuning baselines, posing a trade-off between efficiency and model quality.
+Arxiv: https://arxiv.org/abs/2211.17192 30 Nov 2022 Google
+The key observation above, that some inference steps are “harder” and some are “easier”, is also a key motivator for our work. We additionally observe that inference from large models is often not bottlenecked on arithmetic operations, but rather on memory bandwidth and communication, so additional computation resources might be available.
+Therefore we suggest increasing concurrency as a complementary approach to using an adaptive amount of computation. Specifically, we are able to accelerate inference without changing the model architectures, without changing the training-procedures or needing to re-train the models, and without changing the model output distribution. This is accomplished via speculative execution.
+
Arxiv: https://arxiv.org/abs/2305.13245 22 May 2023 Google
+
Arxiv: https://arxiv.org/abs/1911.02150 6 Nov 2019 Google
+Multi-head attention layers, as used in the Transformer neural sequence model, are a powerful alternative to RNNs for moving information across and between sequences. While training these layers is generally fast and simple, due to parallelizability across the length of the sequence, incremental inference (where such parallelization is impossible) is often slow, due to the memory-bandwidth cost of repeatedly loading the large "keys'' and "values" tensors. We propose a variant called multi-query attention, where the keys and values are shared across all of the different attention "heads", greatly reducing the size of these tensors and hence the memory bandwidth requirements of incremental decoding. We verify experimentally that the resulting models can indeed be much faster to decode, and incur only minor quality degradation from the baseline.
+We introduce multi-query Attention as a variation of multi-head attention as described in [Vaswani et al., 2017]. Multi-head attention consists of multiple attention layers (heads) in parallel with different linear transformations on the queries, keys, values and outputs. Multi-query attention is identical except that the different heads share a single set of keys and values.
+Arxiv: https://arxiv.org/abs/1701.06538 23 Jan 2017 Google
+The capacity of a neural network to absorb information is limited by its number of
+parameters. Conditional computation, where parts of the network are active on a
+per-example basis, has been proposed in theory as a way of dramatically increasing model capacity without a proportional increase in computation. In practice,
+However, there are significant algorithmic and performance challenges. In this
+work, we address these challenges and finally realize the promise of conditional
+computation, achieving greater than 1000x improvements in model capacity with
+only minor losses in computational efficiency on modern GPU clusters. We introduce a Sparsely-Gated Mixture-of-Experts layer (MoE), consisting of up to
+thousands of feed-forward sub-networks. A trainable gating network determines
+a sparse combination of these experts to use for each example.
+We present model architectures in which a MoE with up to 137 billion parameters is applied convolutionally between stacked LSTM layers. On large language modeling and machine translation benchmarks, these models achieve significantly better results than state-of-the-art at lower computational cost.
+Arxiv: https://arxiv.org/abs/2305.14705 24 May 2023 Google
+Sparse Mixture-of-Experts (MoE) is a neural architecture design that can be utilized to add learnable parameters to Large Language Models (LLMs) without increasing inference cost.
+ + +Arxiv: https://arxiv.org/abs/2311.16452 28 Nov 2023 Microsoft
+We find that prompting innovation can unlock deeper specialist capabilities and show that GPT-4 easily tops prior leading results for medical benchmarks.
+We find that GPT-4 benefits significantly from being allowed to design its prompt, specifically with coming up with its own chain-of-thought to be used for in-context learning. This observation echoes other reports that GPT-4 has an emergent self-improving capability via introspection, such as self-verification
+We note that the automated chain-of-thought reasoning removes dependency on special human expertise and medical datasets. Thus, despite the name Medprompt, extending from the framing context and research trajectory of our investigation of the capabilities of GPT-4 on medical challenge problems, the methodology doesn’t include any components specifically oriented towards medicine.
+Ensembling is a technique for combining the outputs of multiple model runs to arrive at a more robust or accurate result via combining the separate outputs with functions like averaging, consensus, or majority vote. Ensembling methods employing a technique referred to as self-consistency [32] use a sampling method to produce multiple outputs that are then consolidated to identify a consensus output. The diversity of the outputs can be controlled by shifting the “temperature” parameter in a model’s generation, where higher temperatures can be viewed as injecting greater amounts of randomness into the generation process. By reordering or shuffling components of a few-shot prompt, ensembling techniques can also address the order sensitivity commonly found with foundation models [26, 39], thus improving robustness.
+
A key challenge with this approach is that self-generated CoT rationales have an implicit risk of including hallucinated or incorrect reasoning chains. We mitigate this concern by having GPT-4 generate both a rationale and an estimation of the most likely answer to follow from that reasoning chain. If this answer does not match the ground truth label, we discard the sample entirely, under the assumption that we cannot trust the reasoning. While hallucinated or incorrect reasoning can still yield the correct final answer (i.e. false positives), we found that this simple label-verification step acts as an effective filter for false negatives.
+While less severe than other foundation models, GPT-4 can exhibit a propensity to favor certain options in multiple choice answers over others (regardless of the option content), i.e., the model can show position bias [1, 16, 40]. To reduce this bias, we propose shuffling the choices and then checking consistency of the answers for the different sort orders of the multiple choice. As a result, we perform choice shuffle and self-consistency prompting. Self-consistency [32] replaces the naive single-path or greedy decoding with a diverse set of reasoning paths when prompted multiple times at some temperature> 0, a setting that introduces a degree of randomness in generations. With choice shuffling, we shuffle the relative order of the answer choices before generating each reasoning path. We then select the most consistent answer, i.e., the one that is least sensitive to choice shuffling.
+
Arxiv: https://arxiv.org/abs/2312.01552 _ 4 Dec 2023 **Allen Institute **_
+URIAL (Untuned LLMs with Restyled In-context ALignment). Based on these findings, we rethink the alignment of LLMs by posing the research question: how effectively can we align base LLMs without SFT or RLHF? To address this, we introduce a simple, tuning-free alignment method, URIAL. URIAL achieves effective alignment purely through in-context learning (ICL) with base LLMs, requiring as few as three constant stylistic examples and a system prompt
+On the other hand, a recent study, LIMA (Zhou et al., 2023), proposes the “Superficial Alignment Hypothesis,” which argues that alignment tuning might simply teach base LLMs to select a sub distribution of data formats for interacting with users. Zhou et al. (2023) demonstrates that SFT with as few as 1,000 examples can also yield high-quality aligned models, thus providing indirect support for this hypothesis.
+We analyze the effect of alignment tuning by examining the token distribution shift between base LLMs and their aligned counterpart (e.g., Llama-2 and Llama2-chat). Our findings reveal that base LLMs and their alignment-tuned versions perform nearly identically in decoding on the majority of token positions (i.e., they share the top-ranked tokens). Most distribution shifts occur with stylistic tokens (e.g., discourse markers, safety disclaimers). These direct evidence strongly supports the hypothesis that alignment tuning primarily learns to adopt the language style of AI assistants, and that the knowledge required for answering user queries predominantly comes from the base LLMs themselves.
+
URIAL encourages stylistic outputs for ICL examples and a system prompt for in-context alignment.
+Arxiv: https://arxiv.org/abs/2309.11495 25 Sep 2023 Meta
+We develop the Chain-of-Verification (COVE) method whereby the model first (i) drafts an initial response; then (ii) plans verification questions to fact-check its draft; (iii) answers those questions independently so the answers ar`ze not biased by other responses; and (iv) generates its final verified response.
+The hallucination problem can be exacerbated due to the issue of exposure bias (Wang & Sennrich, 2020).
+
Arxiv: https://arxiv.org/abs/2307.06908 13 Jul 2023 AI21 Labs
+The key idea is automatically perturbing factual statements taken from the corpus to create a constant number of false variations (hereafter, 3) for each true statement (Figure 1). The LM’s FACTOR accuracy on our benchmark is defined as the percentage of examples for which it assigns higher likelihood to the factual completion than to any of the false variations.
+
Arxiv: https://openreview.net/pdf/c6a24bc50ce18fe080ef17ee8b448a66bd060e63.pdf 4 Jan 2024
+Arxiv: https://arxiv.org/abs/2305.16381 25 May 2023
+We focus on diffusion models, defining the fine-tuning task as an RL problem, and updating the pre-trained text-to-image diffusion models using policy gradients to maximize the feedback-trained reward. Our approach, coined DPOK, integrates policy optimization with KL regularization.
+Generation of more data includes generating n-1 negative samples and leveraging contrastive loss and generating more images to increase diversity.
+In fine-tuning the loss function will be the expectancy of the sum of all the binary-human-classified dataset and also loss from the pre-training based data (weighted with B) to maintain accuracy of the model (avoid catastrophic forgetting). For the reward loss the idea is for the reward to be log-likelihood but it’s not easy, Therefore we minimize reward-weighted MSE loss instead.
+Setup: Pretrained Stable Diffusion 1.5, fine-tuning using static CLIP language encoder, Reward model is MLP using ViT-L/14 CLIP for image/text embeddings, Dataset 2700 prompts, 27k images, 16k unlabeled and 625k for pretraining.
+SFT: model is updated on a fixed dataset generated by the pre-trained model.
+RL: model is updated using new samples from the previously trained model during online RL fine-tuning.
+Based on the results, adding KL regularization helps in improving both image fidelity and accuracy (mostly image fidelty).
+Arxiv: https://arxiv.org/abs/2212.08751 16 Dec 2022 OpenAI
+In this paper, we explore an alternative method for 3D object generation which produces 3D models in only 1-2 minutes on a single GPU. Our method first generates a single synthetic view using a text-to-image diffusion model, and then produces a 3D point cloud using a second diffusion model which conditions on the generated image.
+Using glade dataset for 2D (fine-tuned on 3D rendering).
+Arxiv: https://arxiv.org/abs/2103.00020 26 Feb 2021 OpenAI
+CLIP pre-trains an image encoder and a text encoder to predict which images were paired with which texts in our dataset. We then use this behavior to turn CLIP into a zero-shot classifier. We convert all of a dataset’s classes into captions such as “a photo of a dog” and predict the class of the caption CLIP estimates best pairs with a given image.
+ + +Arxiv: https://arxiv.org/abs/2401.04088 8 Jan 2024 Mixtral.ai
+We introduce Mixtral 8x7B, a Sparse Mixture of Experts (SMoE) language model. Mixtral has the same architecture as Mistral 7B, with the difference that each layer is composed of 8 feedforward blocks (i.e. experts).
+
Consecutive tokens are often assigned to the same experts. In fact, we observe some degree of positional locality in The Pile datasets. Table 5 shows the proportion of consecutive tokens that get the same expert assignments per domain and layer. Figures are not showing it clearly.
+
Arxiv: https://arxiv.org/abs/2312.11805 19 Dec 2023 Google
+The reasoning capabilities of large language models show promise toward building generalist agents that can tackle more complex multi-step problems.
+
Welcome to the repository of knowledge where the pursuit of understanding Large Language Models (LLMs) becomes a shared adventure. This chapter is dedicated to providing you with a meticulously curated list of research papers, each accompanied by a succinct summary highlighting the core insights. Along with direct links to the original works hosted on arXiv, this collection aims to serve as a gateway to the depths of LLM research and development.
+Prepare to dive into the technical breakthroughs, innovative methodologies, and the latest findings within the realm of LLMs. Whether you are a seasoned researcher, an industry professional, or simply an AI enthusiast, this compilation is set to be an invaluable resource in your journey through the landscape of generative AI.
+Below is the list of selected research papers that have significantly contributed to the field of Large Language Models. Each entry includes a brief summary and an arXiv link to the full paper for a comprehensive read.
+[Survey] Instruction Tuning for Large Language Models
+[RA-DIT] Retrieval-Augmented Dual Instruction Tuning
+[Sequential Monte Carlo] Steering of LLMs using Probabilistic Programs
+[LLM-in-a-Flash] Efficient LLM Inference with Limited Memory
+[RoPE] RoFormer: Enhanced Transformer with Rotary Position Embedding
+[LORA] LOw-RAnk Adaptation of LLM
+[Speculative] Fast Inference from Transformers via Speculative Decoding
+[GQA] Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints
+[Multi-Heads Sharing] Fast Transformer Decoding: One Write-Head is All You Need
+[MoE] Outrageously Large Neural Networks: The Sparsely-Gated Mixture-Of-Experts Layer
+[MoE] Mixture-of-Experts Meets Instruction Tuning: A Winning Combination for LLM
+[MedPrompt] Can Generalist Foundation Models Outcompete Special-Purpose Tuning?
+[URIAL] The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning
+[CoVE] Chain-of-Verification Reduces Hallucinations in LLM Models
+[Point-E] A System for Generating 3D Point Clouds from Complex Prompts
+[CLIP] Connecting text and images
+As we embark on this scholarly expedition, remember that this is just the beginning. The field of LLMs is ever-evolving, with new discoveries and insights emerging regularly. Keep this page bookmarked, and revisit often to stay updated with the latest research that shapes the future of generative AI and LLMs.
+ + +
GSM8K - https://huggingface.co/datasets/gsm8k/viewer/main/train
+ + +
PEFT Methods: LoRA, HINT, Qlora, LOMO, Delta-tuning
+Evaludation Methods: HELM, Low-resource Instruction Tuning
+
In this section we will explore both datasets and LLMs implementation and examples.
+Welcome to the comprehensive guide on the datasets and fine-tuning processes that are pivotal in crafting instruction-tuned Large Language Models (LLMs). This section serves as an essential resource for researchers, practitioners, and AI enthusiasts who are delving into the specifics of instruction tuning and its applications in LLMs.
+We will explore a variety of datasets specifically curated to train and evaluate instruction-tuned models, outline the methods and practices that have defined the field, and showcase domain-specific applications that bring out the versatility of these AI powerhouses.
+These databases range from general-purpose instruction sets to task-specific collections, all aimed at enhancing the ability of LLMs to understand and execute complex instructions with precision.
+For easy access and exploration, the following is a list of some of the most prominent datasets in the field of instruction tuning for LLMs:
+These resources are further augmented by visual examples of task instructions and their applications, enabling a more intuitive grasp of how instruction tuning operates within LLMs.
+As we move forward, we will also delve into benchmarks like GSM8K, a dataset designed to evaluate the problem-solving capabilities of LLMs in a structured and rigorous manner.
+GSM8K - https://huggingface.co/datasets/gsm8k/viewer/main/train
+This is just the beginning. Stay with us as we uncover more about the fine-tuning processes and the specific LLMs that have been enhanced through these methods, and ultimately, explore the impact of domain-specific instruction tuning on the capabilities of these AI models.
+In the dynamic and rapidly evolving world of artificial intelligence, Large Language Models (LLMs) stand as monumental achievements, showcasing the sheer potential of machine learning and natural language processing. From aiding in complex decision-making processes to generating creative content, LLMs have become integral to advancing the frontiers of what machines can understand and articulate.
+The journey of LLMs has been marked by several pioneering models that have set new benchmarks in the field:
+These models, and others like them, have revolutionized our understanding of language's complexity and the capabilities of AI to mimic human linguistic skills.
+The applications of LLMs are as varied as they are impactful. From creating more engaging virtual assistants to aiding in legal document analysis, LLMs are enhancing productivity, creativity, and problem-solving across sectors. Here are some domains where LLMs have made significant contributions:
+ + +Click here to be redirected.
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+
+
+ 
+ Hey, I am an experienced hands-on senior software engineer involved in all the aspects of the products lifecycle.
+This blog will share some of my insights, interview tips and projects.
+You can also visit my GitHub and follow some of my projects.
+ + + + + +Changing jobs brings considerable challenges that many avoid. Common psychological barriers include:
+Fear of rejection, often stemming from impostor syndrome.
+Apprehension about new beginnings.
+Lack of motivation.
+Perceived lack of time and agency.
+Concern about retaliation.
+Inability to identify better opportunities.
+However, none of these fears are truly justified and can be logically overcome.
+The first step in deciding to switch jobs is examining one's motivations. Next, weigh the pros and cons visually. Finally, create a decision grid and consult trusted connections like family, friends or colleagues before settling on a firm yes or no.
+Some common personal motivations are:
+Desire for improved compensation (e.g. moving to a higher-paying senior role).
+Current role feels unchallenging, boring and/or repetitive.
+Toxic work environment (work-life balance, stress, lack of influence).
+Poor team and leadership (unenjoyable colleagues, no mentors, high attrition).
+Each of these motivations can be evaluated on its own merits and should provide enough rationale to determine whether one should stay or move on.
+ + +It's important to recognize that no job is 100% perfect. However, with the right approach, you can identify opportunities that are the best fit for your skills, interests and professional goals.
+To discover appealing roles, first ensure you have sufficient options to evaluate and select the most suitable position for you. Useful strategies include:
+Investing time in optimizing your LinkedIn profile and activity so recruiters can more easily find you.
+Scheduling informational meetings with former colleagues to uncover unofficial job leads at their companies.
+Telling friends, family, and broader network about your job search plans so they can share relevant openings.
+Scanning your existing professional connections and employer to identify where people in your field are getting hired.
+Researching the job market comprehensively to understand salary ranges, in-demand skills, company cultures, etc. This provides helpful context.
+Customizing your resume and cover letters to align with each potential job's requirements.
+Seeking referral opportunities from your network to increase chances of getting interviews.
+Practicing your interview skills to feel confident during the hiring process.
+Asking thoughtful questions of hiring managers and employees to assess company/role fit.
+Negotiating job offers strategically to maximize compensation, flexibility, growth opportunities, etc.
+With preparation and commitment to exploring all promising leads, you can find a job that checks most of your key boxes for an enjoyable and successful career move.
+ + +Book two mock interviews within the next 14 days with companies you are not interested in working for. Ideally, schedule the first phone screening 7 days out. This will motivate you to prepare even when time is tight. Remember, most companies allow candidates to re-interview after 6-12 months if needed, so don't stress.
+Although being a jack-of-all-trades with multiple programming languages is preferable on the job, it can be counterproductive in interviews. The key is optimizing the time between conceiving a solution and writing syntactically correct code. Therefore, picking a functional language you know very well is recommended.
+Select the programming language you actively use and feel most proficient in as your go-to for interview coding. This ensures you can demonstrate your skills accurately.
+Thoroughly review the job description and research the company to anticipate likely interview questions and expectations.
+Practice discussing your resume, projects, and skills out loud to improve your articulation and confidence.
+Prepare stories that highlight times you successfully solved complex problems, overcame challenges, collaborated, etc.
+Brush up on computer science fundamentals like data structures and algorithms. Review coding patterns and syntax for your chosen language.
+Plan for behavioral and technical questions, but also prepare some insightful questions to ask the interviewer to show interest.
+Dress professionally and arrive early to interviews to make a good impression.
+With practice and dedication to sharpening your interview abilities, you can showcase your qualifications persuasively and land the perfect job.
+ + +Developing effective interview skills presents difficulties for those established in their current roles. Pursuing employment opportunities with other companies commonly necessitates substantial time and effort. However, seeking new positions while still employed offers advantages, including the abilities to:
+Negotiate improved employment agreements with greater confidence.
+Demonstrate self-assurance throughout the process.
+Provide relevant, timely examples and metrics from one's work history.
+With experience transitioning between over five positions (both vertical promotions and lateral moves), I have chosen to record these steps and refine the process to benefit both myself and others.
+ + +Constant lookup - O(1) - uses hash function and list of entries for collisions.
Binary Tree - Has up to 2 child nodes.
BST (Binary Search Tree) - Binary tree where left <= parent <= right - since BST is not balanced it has O(N) bound.
AVL tree - Balanced(self-balanced) BST, common operation is rotation/shift (L, R, LR, RL) - all balanced trees will have O(Log(N)) bound.
B-Tree - Balanced(self-balanced) BST, generalization of binary tree, nodes has more children. Used for databases to require less reads (n keys).
Trie (prefix tree) - Tree to locate specific keys (mostly strings by traversing individual characters). A node's position in the trie defines the key with which it is associated.
Adjacency matrix - size N*M - binary value(0/1) to represent existance of an edge.
Nodes and edges sets
| Operation | Bound | Explanation |
|---|---|---|
| Peak | O(1) | Read |
| Poll/Extract/Insert | O(Log(N)) | It is still requires to fix the underlying tree |
| Build Heap | O(N) | Unlike the intuition O(N*Log(N)) is not strict and since the tree is balanced the complexity is bound to the height ` (0 * n/2) + (1 * n/4) + (2 * n/8) + ... + (h * 1)). |
Bloom Filter (membership problem) - no false negatives (always return the member), built by 2D binary array of buckets (B) * number of hash functions (L) = BxL.
Count Min Sketch (approximate heavy-hitters problem) - similar to bloom filter with integer(count) array, error correlates to number of L and B size (we can’t choose epsilon = 0 since it equals to infinity memory).
| Algorithm | Worst | Average | Best | Space | Notes |
|---|---|---|---|---|---|
| Quicksort | O(n^2) | O(n*log*(n)) | O(n*log*(n)) | O(log*(n)) | |
| Mergesort | O(n*log*(n)) | O(n*log*(n)) | O(n*log*(n)) | O(n) | |
| Bubble Sort | O(n^2) | O(n^2) | O(n)) | O(1) | Never use! |
| Insertion Sort | O(n^2) | O(n^2) | O(n)) | O(1) | Less swaps - more efficient |
| Selection Sort | O(n^2) | O(n^2) | O(n^2)) | O(1) | Less swaps |
| Bucket Sort | O(n^2) | O(n+k) | O(n+k)) | O(n) | |
| Counting Sort | O(n+k) | O(n+k) | O(n+k)) | O(k) |
Most popular / optimized on average sorting algorithm for small data-sets (unstable - position based on pivot selection). O(N^2) worst case O(N *Log(N)) average case.
Lomuto partitioning (last elm), Hoare partitioning (random / middle - mostly performs better).
+QuickSelect is a selection algorithm to find kth smallest element (Ohare), it’s a partial quick-sort algorithm - sort only one side of recursion partitioning - O(logn) or it’s O(n)
+ 
// Sorts a (portion of an) array, divides it into partitions, then sorts those
+algorithm quicksort(A, lo, hi) is
+ if lo >= 0 && hi >= 0 && lo < hi then
+ p := partition(A, lo, hi)
+ quicksort(A, lo, p) // Note: the pivot is now included
+ quicksort(A, p + 1, hi)
+
+ // Divides array into two partitions
+ algorithm partition(A, lo, hi) is
+ // Pivot value
+ pivot := A[ floor((hi + lo) / 2) ] // The value in the middle of the array
+ // Left index
+ i := lo - 1
+ // Right index
+ j := hi + 1
+
+ loop forever
+ // Move the left index to the right at least once and while the element
+ // at the left index is less than the pivot
+ do i := i + 1 while A[i] < pivot
+
+ // Move the right index to the left at least once and while the element
+ // at the right index is greater than the pivot
+ do j := j - 1 while A[j] > pivot
+
+ // If the indices crossed, return
+ if i ≥ j then return j
+
+ // Swap the elements at the left and right indices
+ swap A[i] with A[j]
+Simplest sorting algorithm (stable) - O(N^2) worst and average case.
i ← 1
+ while i < length(A)
+ j ← i
+ while j > 0 and A[j-1] > A[j]
+ swap A[j] and A[j-1]
+ j ← j - 1
+ end while
+ i ← i + 1
+ end while
+Very efficient for large data-sets - O(N*Log(N)) average and worst case.
// Sorting the entire array is accomplished by TopDownMergeSort(A, B, length(A)).
+
+// Array A[] has the items to sort; array B[] is a work array.
+void TopDownMergeSort(A[], B[], n)
+{
+ CopyArray(A, 0, n, B); // one time copy of A[] to B[]
+ TopDownSplitMerge(B, 0, n, A); // sort data from B[] into A[]
+}
+
+// Split A[] into 2 runs, sort both runs into B[], merge both runs from B[] to A[]
+// iBegin is inclusive; iEnd is exclusive (A[iEnd] is not in the set).
+void TopDownSplitMerge(B[], iBegin, iEnd, A[])
+{
+if (iEnd - iBegin <= 1) // if run size == 1
+ return; // consider it sorted
+ // split the run longer than 1 item into halves
+ iMiddle = (iEnd + iBegin) / 2; // iMiddle = mid point
+ // recursively sort both runs from array A[] into B[]
+ TopDownSplitMerge(A, iBegin, iMiddle, B); // sort the left run
+ TopDownSplitMerge(A, iMiddle, iEnd, B); // sort the right run
+ // merge the resulting runs from array B[] into A[]
+ TopDownMerge(B, iBegin, iMiddle, iEnd, A);
+}
+
+// Left source half is A[ iBegin:iMiddle-1].
+// Right source half is A[iMiddle:iEnd-1 ].
+// Result is B[ iBegin:iEnd-1 ].
+void TopDownMerge(A[], iBegin, iMiddle, iEnd, B[])
+{
+ i = iBegin, j = iMiddle;
+ // While there are elements in the left or right runs...
+ for (k = iBegin; k < iEnd; k++) {
+ // If left run head exists and is <= existing right run head.
+ if (i < iMiddle && (j >= iEnd || A[i] <= A[j])) {
+ B[k] = A[i];
+ i = i + 1;
+ } else {
+ B[k] = A[j];
+ j = j + 1;
+ }
+ }
+ }
+
+ void CopyArray(A[], iBegin, iEnd, B[])
+ {
+ for (k = iBegin; k < iEnd; k++)
+ B[k] = A[k];
+ }
+Used as a distribution sort.
+Runtime = O(N^2) worst case, O(n + n^2/k + k) average case when k = buckets
+Space = O(N*k)
Example:
+| Bucket 1 | Bucket 2 | Bucket 3 |
|---|---|---|
| 0-9 | 10-99 | 10-999 |
| 1,3 | 333 |
Used to sort numbers according to their base (specific case of bucket sort)
+Runtime: O(nw) time, where n is the number of keys, and w is the key length.
Used when the keys distribution is low compared to number of elements
+Runtime O(N+K) runtime. Bucket sort can be used instead (but required dynamic allocation of memory)
function CountingSort(input, k)
+ count ← array of k + 1 zeros
+ output ← array of same length as input
+
+ for i = 0 to length(input) - 1 do
+ j = key(input[i])
+ count[j] += 1
+
+ for i = 1 to k do
+ count[i] += count[i - 1]
+
+ for i = length(input) - 1 downto 0 do
+ j = key(input[i])
+ count[j] -= 1
+ output[count[j]] = input[i]
+
+ return output
+A critical first step in preparing for any programming-related job interview is ensuring you have command of fundamental computer science data structures and algorithms. While you won't need to have every detail memorized, being generally familiar with the most common options is essential.
+Here are some of the key data structures and algorithms to review and practice implementing:
+When studying these data structures and algorithms, focus on:
+With strong grasp of these fundamental building blocks, you will have the core CS knowledge needed to analyze and solve problems efficiently in interviews. Be ready to walk through your code, explain your design choices, discuss complexity, and compare approaches.
+Going beyond familiarity to deep expertise with data structures and algorithms will help you stand out and succeed in landing your ideal programming role.
+ + +++Given the root node of a binary search tree and two integers low and high, return the sum of values of all nodes with a value in the inclusive range [low, high].
+
Explanation
+Traversal of the tree in any order is fine and make sure you don't traverse the unnecessary parts of the tree. Recrusion or iterative solution are acceptable (make sure you can explain that any recursive solution can be converted to an iterative solution).
+Solution
+class RangeSumBST {
+ public int rangeSumBST(TreeNode root, int low, int high) {
+ Queue<TreeNode> queue = new LinkedList<TreeNode>();
+ queue.add(root);
+
+ int sum = 0;
+ while (!queue.isEmpty()) {
+ TreeNode node = queue.poll();
+ if (node.val >= low && node.val <= high)
+ sum += node.val;
+
+ if (node.left != null && node.val >= low)
+ queue.add(node.left);
+
+ if (node.right != null && node.val <= high)
+ queue.add(node.right);
+ }
+ return sum;
+ }
+}
+Question
+++Evaluate the value of an arithmetic expression in > Reverse Polish Notation.
+Valid operators are +, -, *, and /. Each operand may > be an integer or another expression.
+Note that division between two integers should > truncate toward zero.
+It is guaranteed that the given RPN expression is > always valid. That means the expression would always > evaluate to a result, and there will not be any > division by zero operation.
+
Explanation
+To perform operation in polish notation we need a stack data-structure (first in last out). We will read the tokens until we encounter operation and then perform the calculation.
+Solution
+class ReversePolishNotation {
+ private static boolean isNumeric(String token) {
+ try {
+ Double.parseDouble(token);
+ return true;
+ } catch(NumberFormatException e) {
+ return false;
+ }
+ }
+
+ private static int performCalculation(int left, int right, String operand) {
+ if (operand.equals("+")) {
+ return left + right;
+ } else if (operand.equals("-")) {
+ return left - right;
+ } else if (operand.equals("*")) {
+ return left * right;
+ } else if (operand.equals("/")) {
+ return left / right;
+ } else {
+ System.out.println("left: " + left + " right: " + right + " operand: " + operand);
+ throw new RuntimeException("Unexpected operand");
+ }
+ }
+
+ public int evalRPN(String[] tokens) {
+ Stack<Integer> numbers = new Stack<Integer>();
+ for (int i = 0; i < tokens.length; i++) {
+ String token = tokens[i];
+ if (!isNumeric(token)) {
+ int right = numbers.pop();
+ int left = numbers.pop();
+ int result = performCalculation(left, right, token);
+ numbers.add(result);
+ } else {
+ numbers.add(Integer.parseInt(token));
+ }
+ }
+ return numbers.pop();
+ }
+}
+Question
+++Given an array of integers nums and an integer target, return indices of the two numbers such that they add up to target.
+
Explanation
+In this question we need to find elem + X = target => X = target - elem;
+A common solution is to create a map from number to index (one pass). +Then leverage it during traversal to check if the element exist (second pass).
+Since this problem is a sum of two elements we can do it in a single pass (the second element will have the first one in the data-structre).
+Solution
+public class TwoSum {
+ public int[] twoSum(int[] nums, int target) {
+ Map<Integer, Integer> numberToIndex = new HashMap<>();
+ int[] result = new int[2]; // number, index
+ for (int i = 0; i < nums.length; i++) {
+ int needed = target - nums[i];
+ if (numberToIndex.containsKey(needed)) {
+ Integer index = numberToIndex.get(needed);
+ result[0] = i;
+ result[1] = index;
+ return result;
+ }
+
+ numberToIndex.put(nums[i], i);
+ }
+
+ throw new IllegalArgumentException("Invalid input");
+ }
+Question
+++You are given an array prices where prices[i] is the price of a given stock on the ith day.
+
++You want to maximize your profit by choosing a single day to buy one stock and choosing a different day in the future to sell that stock.
+
++Return the maximum profit you can achieve from this transaction. If you cannot achieve any profit, return 0.
+
Explanation
+We are looking for the largest difference (min / max). Therefore we need to do a simple book keeping and keep track of the current minimum and profit we have. While we traverse the array we update the min if necessary and record the profit if it's bigger than what we have. I wrote my initial solution but there is no need to keep track of the current profit (we can just record max profit when needed).
+Solution
+class BestTimeToSellStock {
+ public int maxProfit(int[] prices) {
+ int size = prices.length;
+ if (size <= 1)
+ return 0;
+
+ int currentMin = prices[0];
+ int currentProfit = 0;
+ int maxProfit = 0;
+
+ for (int i = 1; i < size; i++) {
+ if (prices[i] < currentMin) {
+ maxProfit = Math.max(maxProfit, currentProfit);
+ currentProfit = 0;
+ currentMin = prices[i];
+ } else {
+ int candidateProfit = prices[i] - currentMin;
+ currentProfit = Math.max(candidateProfit, currentProfit);
+ }
+ }
+ maxProfit = Math.max(maxProfit, currentProfit);
+
+ return maxProfit;
+ }
+}
+Question
+A decimal number is called deci-binary if each of its digits is either 0 or 1 without any leading zeros. For example, 101 and 1100 are deci-binary, while 112 and 3001 are not.
+Given a string n that represents a positive decimal integer, return the minimum number of positive deci-binary numbers needed so that they sum up to n.
+Example 1:
+Input: n = "32" +Output: 3 +Explanation: 10 + 11 + 11 = 32 +Example 2:
+Input: n = "82734" +Output: 8 +Example 3:
+Input: n = "27346209830709182346" +Output: 9
+Constraints:
+\( 1 \le n.length \le 105 \)
+n consists of only digits.
+n does not contain any leading zeros and represents a positive integer.
+Explanation
+This question relies on logic, since we can use only deci-binary numbers (zero or 1 in each digit) it means that in order to build a specific digit k we will need at-least k different deci numbers. Therefore, the answer is the maximum of all digits in n max(k)
Solution
+class MinPartitionsSolutions {
+ public int minPartitions(String n) {
+ int max = 0;
+ for(int i = 0; i < n.length(); i++){
+ int digit = n.charAt(i) - '0';
+ if (digit > max) {
+ max = digit;
+ }
+ }
+ return max;
+ }
+}
+Question
+Given the head of a linked list head, in which each node contains an integer value.
+Between every pair of adjacent nodes, insert a new node with a value equal to the greatest common divisor of them.
+Return the linked list after insertion.
+The greatest common divisor of two numbers is the largest positive integer that evenly divides both numbers.
+Example 1:
+Input: head = [18,6,10,3] +Output: [18,6,6,2,10,1,3] +Explanation: The 1st diagram denotes the initial linked list and the 2nd diagram denotes the linked list after inserting the new nodes (nodes in blue are the inserted nodes).
+Input: head = [7] +Output: [7] +Explanation: The 1st diagram denotes the initial linked list and the 2nd diagram denotes the linked list after inserting the new nodes. +There are no pairs of adjacent nodes, so we return the initial linked list.
+Constraints:
+The number of nodes in the list is in the range [1, 5000]. +\( 1 \le Node.val \le 1000 \)
+Explanation
+The idea is to break the solution into two different parts.
+Implement GCD. The recursive way is the easiest, a side note is that it's usually not recommended to implement recursion but in this case because it is tail recursion the compiler or JVM would be able to optimize it if needed.
+Add a new node from the GCD value.
+Solution
+
+/**
+ * Definition for singly-linked list.
+ * public class ListNode {
+ * int val;
+ * ListNode next;
+ * ListNode() {}
+ * ListNode(int val) { this.val = val; }
+ * ListNode(int val, ListNode next) { this.val = val; this.next = next; }
+ * }
+ **/
+class GreatestCommonDivisorsSolution {
+ public ListNode insertGreatestCommonDivisors(ListNode head) {
+ ListNode curr = head;
+ while (curr != null && curr.next != null) {
+ int newVal = gcd(curr.val, curr.next.val);
+ // insert new node between curr and curr.next with newVal
+ curr.next = new ListNode(newVal, curr.next);
+ curr = curr.next.next;
+ }
+ return head;
+ }
+
+ private int gcd(int a, int b) {
+ if (b == 0) return a;
+ return gcd(b, a % b);
+ }
+}
+
+Question
+++Design a data structure that follows the constraints of a Least Recently Used (LRU) cache.
+
++Implement the LRUCache class:
+
++LRUCache(int capacity) Initialize the LRU cache with positive size capacity. +int get(int key) Return the value of the key if the key exists, otherwise return -1. +void put(int key, int value) Update the value of the key if the key exists. Otherwise, add the > key-value pair to the cache. If the number of keys exceeds the capacity from this operation, > evict the least recently used key. +The functions get and put must each run in O(1) average time complexity.
+
Explanation
+LRU cache is a challenging problem, the problem is mostly focused on how to store the keys efficiently for removal by time inserted. Therefore in addition to the k/v hashtable we need an additional data-structure that allows retrieval, deletion and modification. Heap / Tree based solution are all applicable solutions but requires O(Log(N)) for insertion and removal.
+In this problem it is possible to do a constant time when choosing a doubly linked list which allows to add to head and evicti from tail. It also possible to perform modification in constant (move to HEAD).
+This is a java implemention of DoublyLinkedList and the LRU Cache.
+Solution
+class DoublyLinkedList {
+ public Node root;
+ public Node tail;
+
+ public Node addToHead(final int key) {
+ if (root == null) {
+ this.root = new Node(key, null, null);
+ this.tail = this.root;
+ } else {
+ Node node = new Node(key, this.root, null);
+ this.root.previous = node;
+ this.root = node;
+ }
+ return this.root;
+ }
+
+ public void moveToHead(final Node node) throws IllegalArgumentException {
+ if (this.root == null) {
+ throw new RuntimeException("Invalid head state");
+ }
+
+ if (this.root == node) { // node is already head
+ return;
+ }
+
+ if (this.tail == node) { // node is the tail - fix it
+ this.tail = this.tail.previous;
+ }
+
+ if (node.previous != null) { // fix existing previous of node
+ node.previous.next = node.next;
+ }
+
+ if (node.next != null) { // fix existing next of node
+ node.next.previous = node.previous;
+ }
+
+ // set as head
+ node.previous = null;
+ node.next = this.root;
+ this.root.previous = node;
+ this.root = node;
+ }
+
+ public Node evictTail() {
+ if (this.root == null) { // list is empty
+ return null;
+ }
+
+ if (this.root == this.tail) {
+ Node reference = this.root;
+ this.root = null;
+ this.tail = null;
+ return reference;
+ }
+
+ Node existingTail = this.tail;
+ existingTail.previous.next = null;
+ this.tail = existingTail.previous;
+ return existingTail;
+ }
+}
+
+class LRUCache {
+ final int capacity;
+ int size;
+ final Map<Integer, CacheEntry> cache;
+ final DoublyLinkedList sortedTtlList;
+
+ static class CacheEntry {
+ public CacheEntry(int value, Node node) {
+ this.value = value;
+ this.node = node;
+ }
+
+ public int value;
+ public Node node;
+ }
+
+ public LRUCache(int capacity) {
+ this.capacity = capacity;
+ this.size = 0;
+ this.cache = new HashMap<>();
+ this.sortedTtlList = new DoublyLinkedList();
+ }
+
+ public int get(int key) {
+ if (!cache.containsKey(key)) {
+ return -1;
+ }
+
+ CacheEntry entry = cache.get(key);
+ sortedTtlList.moveToHead(entry.node);
+ return entry.value;
+ }
+
+ public void put(int key, int value) {
+ if (cache.containsKey(key)) {
+ CacheEntry entry = cache.get(key);
+ sortedTtlList.moveToHead(entry.node);
+ entry.value = value;
+ } else {
+ if (this.size < this.capacity) {
+ this.size = this.size + 1;
+ } else {
+ Node nodeToRemove = sortedTtlList.evictTail();
+ cache.remove(nodeToRemove.key);
+ }
+ final Node newNode = sortedTtlList.addToHead(key);
+ cache.put(key, new CacheEntry(value, newNode));
+ }
+ }
+}
+Question
+++Given a string s, return the longest palindromic substring in s.
+
Input: s = "babad"
+Output: "bab"
+Explanation: "aba" is also a valid answer.
+Explanation
+The simplest solution is calling isPalindrome for every substring. Checking palindrome is O(N) and all substrings are O(N^2) - therefore O(N^3).
This problem can be converted to a dynamic programming problem where Palindrome(i, j) = Palindrome(i+1, j-1) && s[i] == s[j] (same for odd and even cases). This will require O(N^2) time and O(N^2) memory.
There is a simpler way to think about this problem. We can think about a palindrome as a center-based string and then check the palindrome from all of the centers.
+Solution
+public class LongestPalindrome {
+ private static class PalindromeSequence {
+ public int startIndex;
+ public int length;
+
+ PalindromeSequence() {
+ this.startIndex = 0;
+ this.length = 1;
+ }
+ }
+
+ private void checkAndUpdateLongestPalindrome(String s, int leftIndex, int rightIndex, PalindromeSequence maxPalindrome) {
+ while (leftIndex >= 0 && rightIndex < s.length() && s.charAt(leftIndex) == s.charAt(rightIndex)) {
+ leftIndex -= 1;
+ rightIndex += 1;
+ }
+
+ int existingLength = rightIndex - leftIndex - 1;
+ if (existingLength > maxPalindrome.length) {
+ maxPalindrome.startIndex = leftIndex + 1;
+ maxPalindrome.length = existingLength;
+ }
+ }
+
+ public String longestPalindrome(String s) {
+ PalindromeSequence maxPalindrome = new PalindromeSequence();
+
+ for (int i = 0; i < s.length(); i++) {
+ int rightIndex = i;
+ int leftIndex = i - 1;
+ checkAndUpdateLongestPalindrome(s, leftIndex, rightIndex, maxPalindrome);
+
+ leftIndex = i - 1;
+ rightIndex = i + 1;
+ checkAndUpdateLongestPalindrome(s, leftIndex, rightIndex, maxPalindrome);
+ }
+
+ return s.substring(maxPalindrome.startIndex, maxPalindrome.startIndex + maxPalindrome.length);
+ }
+}
+Dynamic programming alternative for reference
+public class LongestPalindromeDynamicProgramming {
+ public String longestPalindrome(String s) {
+ int strLength = s.length();
+ int maxLength = 0;
+ int maxStartIndex = 0;
+
+ boolean[][] dp = new boolean[strLength][strLength];
+ for (int i = strLength - 1; i >= 0; i--) {
+ for (int j = i; j < strLength; j++) {
+ dp[i][j] = s.charAt(i) == s.charAt(j) && (j - i < 2 || dp[i+1][j-1]);
+ int existingLength = j - i + 1;
+ if (dp[i][j] && existingLength > maxLength) {
+ maxStartIndex = i;
+ maxLength = j - i + 1;
+ }
+ }
+ }
+ return s.substring(maxStartIndex, maxStartIndex + maxLength);
+ }
+}
+When tackling a technical interview coding challenge, a systematic approach is key:
+First conceptualize a brute force solution and explain it thoroughly without coding. This demonstrates you can reason through the problem.
+Next, identify potential optimizations and edge cases. Consult the interviewer to validate your thinking. Communication is vital!
+Finally, code the most optimal solution in a structured, readable style. While concise code has merits, prioritize:
+Beware solutions online that sacrifice readability for brevity. Well-structured, easy-to-follow code that showcases your abilities is ideal for interviews.
+Additional Best Practices:
+Ask clarifying questions before diving in. Validate requirements and constraints.
+Think through test cases out loud to catch edge cases early.
+Describe your approach and code aloud as you write it.
+Explain time/space complexity analysis of your solution.
+Suggest follow-up enhancements to show long-term thinking.
+With care taken to communicate effectively, write readable code, and demonstrate strong CS fundamentals, you can tackle any interview coding challenge successfully. The key is staying calm, collected, and methodical in your problem solving approach.
+ + +Mastering System Design for Technical Interviews
+System design questions are pivotal in technical interviews, directly influencing how interviewers evaluate and rank candidates for senior engineering roles. However, many engineers underestimate the difficulty of system design and rely on insufficient resources that often provide misleading guidance.
+In my experience, both free and paid system design trainings frequently miss the mark on adequately preparing candidates. Therefore, in this blog, I will walk through solutions to common system design interview questions, sharing the approach and techniques I've found most effective.
+Listen closely to thoroughly understand all requirements before proposing any solutions. Practice active listening and asking clarifying questions at appropriate times. Document requirements clearly on a whiteboard or paper.
+Only after requirements are fully defined, begin architecting the high-level design. Explain considerations like expected users, requests per second, latency limits, storage volumes, etc.
+Map out key architectural components and core data flows end-to-end (e.g user clicks button, client-side call, load balancer, authentication, backend service A, etc.)
+Deconstruct each component further specifying detailed APIs, data schemas, databases, caching, etc. as needed.
+Avoid jumping to solutions prematurely before nailing down requirements. Build trust by demonstrating care and precision.
+Know when to ask probing questions to unblock progress versus nodding along.
+Balance high-level architecture with lower-level implementation details at appropriate junctures.
+With a structured, requirements-focused approach and strong communication skills, system design interviews can be mastered through practice over time. I look forward to sharing specific techniques for top technology companies' system design questions in future posts.
+ + +Click here to be redirected.
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Software Blog\nHey, I am an experienced hands-on senior software engineer involved in all the aspects of the products lifecycle.\nThis blog will share some of my insights, interview tips and projects.\nYou can also visit my GitHub and follow some of my projects.\n","id":"https://gel.github.io/","title":"Gal's Pragmatic Software Blog"},"https://gel.github.io/blog/":{"body":"Blog\n","id":"https://gel.github.io/blog/","title":""},"https://gel.github.io/blog/dalle-faceswap/":{"body":"Generative AI has been phenomenal in everything related to productivity given inputs such as free-text or code.\nThe new image or multi-modal based foundation models has made tremendous improvements and I wanted to explore them.\nMy goal was generating superheroes portraits using a ChatGPT Pro subscription (Dall-E is included).\nGenerating Superheroes Portraits with ChatGPT (Dall-E)\nChatGPT (Dall-E) will let you draw abstract superheroes but it is currently not possible to generate them from real people photos.\nThe generic response Dall-E will output is: \"I'm sorry, but I'm unable to use real individuals' faces from images to create new images. If you have any other requests or need assistance with something else, feel free to ask!\" \nTherefore, we will leverage Dall-E just to generate a generic face superhero (cartoonish or real) and later on replace it using a different product.\nMy project was generating a superhero named \"דני כוסברו\" which is a humorous word-play for a famous news broadcaster named \"Dani Kushmaro\" and Cilantro.\nIn order to generate a portrait I've uploaded the portrait of the reporter and asked ChatGPT:\n\"Draw a real person face that has coriander. The person should look like a superhero\"\nThis is the output image I got:\n\nAs you can see this is a great representation for a first try but the biggest problem that I've learnt later is that the coriander on the face makes it very difficult for the face-swap tool later.\nIn order to fix it, I've asked ChatGPT to fix it by writing:\n\"Draw a portrait of a person with their face merged with coriander, make sure the face is not too hidden so it can be easily replaced with a real person\"\nOutput image came exactly as I wanted it to be:\n\nLet's move to the second part of replacing it with the reporter real face.\nFace Swap - Simple (picsi.ai)\nInsightFace is an open source 2D&3D deep face analysis library with more than 15k stars on github.\nThe product that is built on top of this library and more features is called picsi.ai and it is possible to set up a discord bot for free with 50 credits to leverage the face-swap functionality.\nThis is the swapping_discord github link but the process is super simple so I will describe it here.\nStep-by-step guide:\n\n\nRefer to this link to register Discord app, create a new chat room, and invite the Midjourney bot to the chat room.\n\n\nInvite the InsightFaceSwap bot to the chat room by this link: Discord Invite Link.\n\n\nUse /saveid command to register your person id with image/features.\n\n\nUse /swapid command to swap the input image with a given saved ID.\n\n\nThe final photos for the superhero are:\n\n\nFace Swap - Open-source (Offline)\nAny library / github project which is wrapping up Insightface is a good option.\nI've personally used the swapseed to do the replacement on my computer.\nThis is a sample code to run after installing the dependencies pip install -r requirements.txt\nimport os\nimport matplotlib.pyplot as plt\nimport gdown\nimport insightface\nfrom insightface.app import FaceAnalysis\nfrom insightface.data import get_image as ins_get_image\nfrom faceswap import swap_n_show, swap_n_show_same_img, swap_face_single,fine_face_swap\n\napp = FaceAnalysis(name='buffalo_l')\napp.prepare(ctx_id=0, det_size=(640, 640))\n\n# Download 'inswapper_128.onnx' file using gdown\nmodel_url = 'https://drive.google.com/uc?id=1HvZ4MAtzlY74Dk4ASGIS9L6Rg5oZdqvu'\nmodel_output_path = 'inswapper/inswapper_128.onnx'\nif not os.path.exists(model_output_path):\n gdown.download(model_url, model_output_path, quiet=False)\n\nswapper = insightface.model_zoo.get_model('inswapper/inswapper_128.onnx', download=False, download_zip=False)\n\n# Load images\nimg1_fn = 'input_and_output.png'\nimg2_fn = 'face_to_replace_from.jpg'\n\n# Swap faces between two images\n# swap_n_show(img1_fn, img2_fn, app, swapper)\n\n# Add face to an image\nswap_face_single(img1_fn, img2_fn, app, swapper, enhance=True, enhancer='REAL-ESRGAN 2x')\n\n","id":"https://gel.github.io/blog/dalle-faceswap/","title":"GenAI Real Face Superhero"},"https://gel.github.io/blog/wsl-environment/":{"body":"After many years of waiting patiently there is finally a reasonable way to have a unified development environment from windows named WSL version 2. This finally enables us developers to run a full linux environment natively in windows without virtual machine or dual-boot. Learn about the details that made it functioning.\nNative Terminal\n\nAs you can see this is a terminal experience in windows enhanced with ConEmu that provides a full linux experience. Some cool feature are:\n\nSupport various linux versions / distros (ubuntu is the default).\nVSCode remote integration. \nGPU Support (Nvidia CUDA).\n\nThe only major issue that still prevents me from using it more frequently is horrible NTFS performance.\nInstallation\nwsl --install\n\n","id":"https://gel.github.io/blog/wsl-environment/","title":"Unified Development Environment"},"https://gel.github.io/books/":{"body":"","id":"https://gel.github.io/books/","title":""},"https://gel.github.io/books/programming/":{"body":"","id":"https://gel.github.io/books/programming/","title":""},"https://gel.github.io/books/programming/pragmatic-programmer/":{"body":"Best introduction book for intermediate software engineers.\n","id":"https://gel.github.io/books/programming/pragmatic-programmer/","title":"The Pragmatic Programmer: From Journeyman to Master @ Andy Hunt, Dave Thomas"},"https://gel.github.io/books/programming/the-rust-programming-language/":{"body":"This book has convinced me that Rust has managed to tackle some of the most annoying error-prone scenarios in C++ such as memory handling and undocumented lifetime guarantees.\n","id":"https://gel.github.io/books/programming/the-rust-programming-language/","title":"The Rust Programming Language, 2nd Edition @ Steve Klabnik, Carol Nichols"},"https://gel.github.io/books/reading/":{"body":"","id":"https://gel.github.io/books/reading/","title":""},"https://gel.github.io/books/reading/anger-management-for-parents/":{"body":"","id":"https://gel.github.io/books/reading/anger-management-for-parents/","title":"Anger Management for Parents: How to Manage your Emotions & Raise a Happy and Confident Child @ Susan Garcia"},"https://gel.github.io/books/reading/chasing-daylight/":{"body":"","id":"https://gel.github.io/books/reading/chasing-daylight/","title":"Chasing Daylight: How My Forthcoming Death Transformed My Life @ Eugene O'Kelly"},"https://gel.github.io/books/reading/how-to-talk-so-little-kids-will-listen/":{"body":"","id":"https://gel.github.io/books/reading/how-to-talk-so-little-kids-will-listen/","title":"How to Talk so Little Kids Will Listen: A Survival Guide to Life with Children Ages 2-7 @ Joanna Faber"},"https://gel.github.io/books/reading/morning-rituals/":{"body":"","id":"https://gel.github.io/books/reading/morning-rituals/","title":"Morning Rituals @ Katie Stone"},"https://gel.github.io/books/reading/never-split-the-difference/":{"body":"","id":"https://gel.github.io/books/reading/never-split-the-difference/","title":"Never Split the Difference: Negotiating as if Your Life Depended on It @ Chris Voss"},"https://gel.github.io/books/reading/oh-crap-potty-training/":{"body":"","id":"https://gel.github.io/books/reading/oh-crap-potty-training/","title":"Oh Crap! Potty Training: Everything Modern Parents Need to Know to Do It Once and Do It Right @ Jamie Glowacki"},"https://gel.github.io/books/reading/primal-endurance/":{"body":"","id":"https://gel.github.io/books/reading/primal-endurance/","title":"Primal Endurance: Escape Chronic Cardio and Carbohydrate Dependency and Become a Fat Burning Beast! @ Mark Sisson"},"https://gel.github.io/books/reading/rich-dad-before-you-quit/":{"body":"In the wake of Rich Dad's bankruptcy, my curiosity was piqued to explore his latest literary offering, hoping to glean fresh perspectives. Delving into the pages of his newer book, I found myself immersed in a world of entrepreneurial known wisdom.\nThe book advocates for a paradigm shift, emphasizing the significance of focusing on the process rather than fixating solely on monetary gains. It underscores the importance of meticulous planning, urging readers to meticulously consider all facets of business operations. Central to this ethos is the concept of the \"5P's,\" elucidated within the pages, serving as a compass for strategic decision-making.\nAs I journeyed through the narrative, I encountered a tapestry of motivational motifs, each weaving together to form a holistic approach to entrepreneurship. Amidst the challenges and uncertainties, Rich Dad's principles serve as guiding beacons, illuminating the path toward sustainable success.\nDespite the shadows cast by past failures, the book exudes optimism, instilling a sense of empowerment within its readers. It champions resilience, urging individuals to persevere in the face of adversity and seize opportunities for growth.\nIn conclusion, my exploration of Rich Dad's latest work proved to be enlightening. Beyond the veneer of financial setbacks lies a reservoir of invaluable insights, waiting to be unearthed by those willing to embark on the journey of entrepreneurial discovery.\n","id":"https://gel.github.io/books/reading/rich-dad-before-you-quit/","title":"Rich Dad's Before You Quit Your Job @ Robert T. Kiyosaki"},"https://gel.github.io/books/reading/rich-dad-poor-dad/":{"body":"","id":"https://gel.github.io/books/reading/rich-dad-poor-dad/","title":"Rich Dad, Poor Dad @ Robert T. Kiyosaki"},"https://gel.github.io/books/reading/sapiens/":{"body":"","id":"https://gel.github.io/books/reading/sapiens/","title":"Sapiens: A Brief History of Humankind @ Yuval Noah Harari"},"https://gel.github.io/books/reading/secrets-of-millionaire-mind/":{"body":"","id":"https://gel.github.io/books/reading/secrets-of-millionaire-mind/","title":"Secrets of the Millionaire Mind: Mastering the Inner Game of Wealth @ T. Harv Eker"},"https://gel.github.io/books/reading/smarter-faster-better/":{"body":"","id":"https://gel.github.io/books/reading/smarter-faster-better/","title":"Smarter Faster Better: The Secrets of Being Productive in Life and Business @ Charles Duhigg"},"https://gel.github.io/books/reading/surrounded-by-idiots/":{"body":"","id":"https://gel.github.io/books/reading/surrounded-by-idiots/","title":"Surrounded by Idiots @ Thomas Erikson"},"https://gel.github.io/books/reading/the-7-habits/":{"body":"","id":"https://gel.github.io/books/reading/the-7-habits/","title":"The 7 Habits of Highly Effective People: Powerful Lessons in Personal Change @ Author Covey, Stephen R."},"https://gel.github.io/books/reading/the-cancer-code/":{"body":"","id":"https://gel.github.io/books/reading/the-cancer-code/","title":"The Cancer Code: A Revolutionary New Understanding of a Medical Mystery @ Jason Fung"},"https://gel.github.io/books/reading/the-complete-guide-to-fasting/":{"body":"","id":"https://gel.github.io/books/reading/the-complete-guide-to-fasting/","title":"The Complete Guide to Fasting: Heal Your Body Through Intermittent, Alternate-Day, and Extended Fasting @ Jason Fung"},"https://gel.github.io/books/reading/the-innovation-stack/":{"body":"","id":"https://gel.github.io/books/reading/the-innovation-stack/","title":"The Innovation Stack: Building an Unbeatable Business One Crazy Idea at a Time @ Jim McKelvey"},"https://gel.github.io/books/reading/the-obesity-code/":{"body":"","id":"https://gel.github.io/books/reading/the-obesity-code/","title":"The Obesity Code: Unlocking the Secrets of Weight Loss @ Jason Fung"},"https://gel.github.io/books/reading/the-psychology-of-money/":{"body":"","id":"https://gel.github.io/books/reading/the-psychology-of-money/","title":"The Psychology of Money @ Morgan Housel"},"https://gel.github.io/books/reading/the-subtle-art/":{"body":"","id":"https://gel.github.io/books/reading/the-subtle-art/","title":"The Subtle Art of Not Giving a F*ck: A Counterintuitive Approach to Living a Good Life @ Mark Manson"},"https://gel.github.io/books/reading/unlocking-the-keto-code/":{"body":"","id":"https://gel.github.io/books/reading/unlocking-the-keto-code/","title":"Unlocking the Keto Code: The Revolutionary New Science of Keto That Offers More Benefits Without Deprivation @ Steven R Gundry"},"https://gel.github.io/generative-ai/1-intro/":{"body":"Embarking on a Journey into the Heart of AI🔗\nThis book is dedicated to peeling back the layers of one of the most intriguing subjects in the field of artificial intelligence: Generative AI and Large Language Models (LLMs). As we dive deep into the intricacies of these AI marvels, we will uncover insights, share breakthroughs, and discuss the profound implications they have on our world.\nGenerative AI has captured the imagination of scientists, artists, and thinkers alike. It is an area of technology that stands at the forefront of innovation—machines that can create, design, and think in ways that were once the exclusive domain of humans. From composing music to authoring articles, these intelligent systems are reshaping the boundaries of creativity and intellect.\nLarge Language Models, a subset of generative AI, are particularly fascinating. They have the ability to understand and generate human language with remarkable proficiency. As we explore the inner workings of LLMs, we will encounter systems like Antrophic Claude, GPT-4, BERT, and their successors, which continue to push the limits of what machines can achieve with words.\nIn this book, I will look at the technical foundations of these AI systems, their philosophical and ethical considerations, their potential applications, and the challenges they pose. By combining thorough research with accessible explanations, my goal is to make this complex subject matter engaging for both AI aficionados and newcomers alike.\nA Preview of the Journey Ahead🔗\n\n\nCompilation of the most interesting and significant findings from my exploration into Generative AI and LLMs\n\n\nDeep Dives into AI Mechanics: Understanding the algorithms, neural networks, and data that power generative AI and LLMs.\n\n\nCreative Showcases: Highlighting the most impressive and thought-provoking outputs of generative AI.\n\n\nIndustry Impact: Analyzing how businesses and industries are being transformed by the adoption of generative AI technologies.\n\n\nFuture Visions: Speculating on the potential future developments and the direction in which generative AI is heading.\n\n\n","id":"https://gel.github.io/generative-ai/1-intro/","title":"Generative AI Foundations"},"https://gel.github.io/generative-ai/1-intro/1-llm-survey/":{"body":"\nA Survey of Reshaping the GenAI Research Landscape\nInstruction Tuning for Large Language Models\n\n[Survey] A Survey of Reshaping the GenAI Research Landscape🔗\nArxiv: https://arxiv.org/abs/2312.10868 18 Dec 2023 IEEE\nThis comprehensive survey explored the evolving landscape of generative Artificial Intelligence (AI), with a specific focus on the transformative impacts of Mixture of Experts (MoE), multimodal learning, and the speculated advancements towards Artificial General Intelligence (AGI).\n[Survey] Instruction Tuning for Large Language Models🔗\nArxiv: https://arxiv.org/abs/2308.10792 21 Aug 2023\nThere are generally two methods for constructing instruction datasets:\n• Data integration from annotated natural language datasets. In this approach,\n(instruction, output) pairs are collected from existing annotated natural language datasets by using templates to transform text-label pairs to (instruction, output) pairs.\nDatasets such as Flan (Longpre et al., 2023) and P3 (Sanh et al., 2021).\n• Generating outputs using LLMs - (1) manually collected; or (2) expanded based on small handwritten seed instructions using LLMs. Next, the collected instructions are fed to LLMs to obtain outputs. Datasets such as InstructWild (Xue et al., 2023) and Self-Instruct (Wang et al., 2022c) are generated following this approach. For multi-turn conversational IT datasets, we can have large language models self-play different roles (user and AI assistant) to generate message\n\n","id":"https://gel.github.io/generative-ai/1-intro/1-llm-survey/","title":"LLM Survey"},"https://gel.github.io/generative-ai/2-llm-research/":{"body":"A Curated Collection of LLM Research Papers🔗\nWelcome to the repository of knowledge where the pursuit of understanding Large Language Models (LLMs) becomes a shared adventure. This chapter is dedicated to providing you with a meticulously curated list of research papers, each accompanied by a succinct summary highlighting the core insights. Along with direct links to the original works hosted on arXiv, this collection aims to serve as a gateway to the depths of LLM research and development.\nPrepare to dive into the technical breakthroughs, innovative methodologies, and the latest findings within the realm of LLMs. Whether you are a seasoned researcher, an industry professional, or simply an AI enthusiast, this compilation is set to be an invaluable resource in your journey through the landscape of generative AI.\nResearch Papers Summary and Insights🔗\nBelow is the list of selected research papers that have significantly contributed to the field of Large Language Models. Each entry includes a brief summary and an arXiv link to the full paper for a comprehensive read.\nLLM Pretraining / Fine-Tuning🔗\n\n\n[Survey] Instruction Tuning for Large Language Models\n\n\n[RA-DIT] Retrieval-Augmented Dual Instruction Tuning\n\n\n[Sequential Monte Carlo] Steering of LLMs using Probabilistic Programs\n\n\nLLM Agents🔗\n\n[RetroFormer] Retrospective LL Agents with Policy Gradient Optimization\n\nLLM Optimization🔗\n\n\n[LLM-in-a-Flash] Efficient LLM Inference with Limited Memory\n\n\n[RoPE] RoFormer: Enhanced Transformer with Rotary Position Embedding\n\n\n[LORA] LOw-RAnk Adaptation of LLM\n\n\n[Speculative] Fast Inference from Transformers via Speculative Decoding\n\n\n[GQA] Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints\n\n\n[Multi-Heads Sharing] Fast Transformer Decoding: One Write-Head is All You Need\n\n\n[MoE] Outrageously Large Neural Networks: The Sparsely-Gated Mixture-Of-Experts Layer\n\n\n[MoE] Mixture-of-Experts Meets Instruction Tuning: A Winning Combination for LLM\n\n\nLLM Prompting🔗\n\n\n[MedPrompt] Can Generalist Foundation Models Outcompete Special-Purpose Tuning? \n\n\n[URIAL] The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning\n\n\n[CoVE] Chain-of-Verification Reduces Hallucinations in LLM Models\n\n\nLLM Benchmarks & Evaluation🔗\n\n[Benchmark] Generating Benchmarks for Factuality Evaluation of Language Models\n\nLLM Multi-Modal / Vision🔗\n\n\n[Point-E] A System for Generating 3D Point Clouds from Complex Prompts\n\n\n[CLIP] Connecting text and images\n\n\nLLM Models🔗\n\n[Gemini] A Family of Highly Capable Multimodal Models\n\n\nAs we embark on this scholarly expedition, remember that this is just the beginning. The field of LLMs is ever-evolving, with new discoveries and insights emerging regularly. Keep this page bookmarked, and revisit often to stay updated with the latest research that shapes the future of generative AI and LLMs.\n","id":"https://gel.github.io/generative-ai/2-llm-research/","title":"LLM Research"},"https://gel.github.io/generative-ai/2-llm-research/1-llm-pretraining-finetuning/":{"body":"[Self-Reward] Self Rewarding Language Models🔗\nArxiv: https://arxiv.org/abs/2401.10020 18 Jan 2024 Meta\nIn this work, we study Self-Rewarding Language Models, where the language model itself is used via LLM-as-a-Judge prompting to provide its own rewards during training. We show that during Iterative DPO training that not only does instruction following ability improve, but also the ability to provide high-quality rewards to itself.\n\nOur approach first assumes access to a base pretrained language model, and a small amount of human-annotated seed data. We then build a model that aims to possess two skills simultaneously: 1. Instruction following: given a prompt that describes a user request, the ability to generate a high quality, helpful (and harmless) response. 2. Self-Instruction creation: the ability to generate and evaluate new instruction following examples to add to its own training set.\nWhile we report the results of both approaches in our experiments, we find that learning from preference pairs gives superior performance, and hence recommend that approach.\n\nIterative Training Our overall procedure trains a series of models M1, . . . , MT where each successive model t uses augmented training data created by the t − 1 th model. We thus define AIFT(Mt) to mean AI Feedback Training data created using model Mt. Model Sequence We thus define the models, and the training data they use as follows: M0 : Base pretrained LLM with no fine-tuning. M1 : Initialized with M0, then fine-tuned on the IFT+EFT seed data using SFT. M2 : Initialized with M1, then trained with AIFT(M1) data using DPO. M3 : Initialized with M2, then trained with AIFT(M2) data using DPO.\n\n[In-Context Pretraining] Language Modeling Beyond Document Boundaries🔗\nArxiv: https://arxiv.org/abs/2310.10638 30 Nov 2023 Meta\nWe instead present IN-CONTEXT PRETRAINING, a new approach where language models are pretrained on a sequence of related documents, thereby explicitly encouraging them to read and reason across document boundaries.\nIN-CONTEXT PRETRAINING instead reorders the pretraining data by combining several semantically related documents to create a coherent input context, thereby exposing LMs to long relevant contexts and providing pretraining signals beyond document boundaries.\nHowever, this document sorting problem is challenging. LMs are typically trained on billions of\ndocuments and we would like to sort them to maximize document similarity in the input context\nwindows without repeating any data. We introduce two new approximate algorithms to tackle these challenges.\n\nTechniques are using embedding models and top-k similarity search and leveraging approximate solutions for the traveling salesman problem - by the means of visiting every document once.\n[DPO] Direct Preference Optimization: Your LM is Secretly a Reward Model🔗\nArxiv: https://arxiv.org/abs/2305.18290 13 Dec 2023 Stanford\nIn this paper we introduce a new parameterization of the reward model in RLHF that enables extraction of the corresponding optimal policy in closed form, allowing us to solve the standard RLHF problem with only a simple classification loss. The resulting algorithm, which we call Direct Preference Optimization (DPO), is stable, performant, and computationally lightweight,\neliminating the need for sampling from the LM during fine-tuning or performing significant hyperparameter tuning.\n\nDPO gradient for loss increases the likelihood of preferred completion (Y_W) and decreases the likelihood for dispreferred completion (Y_L). Importantly, the examples are weighed by how much higher the implicit reward model rˆθ rates the dispreferred completions, scaled by β, i.e, how incorrectly the implicit reward model orders the completions, accounting for the strength of the KL constraint. \nDPO outline. The general DPO pipeline is as follows: 1) Sample completions y1, y2 ∼ πref(· | x) for every prompt x, label with human preferences to construct the offline dataset of preferences D = {x (i) , y (i) w , yl) (i)} N i=1 and 2) optimize the language model πθ to minimize LDPO for the given πref and D and desired β. \n[RLHF] Secrets of RLHF in LLMs Part II: Reward Modeling🔗\nArxiv: https://arxiv.org/abs/2312.15503 24 Dec 2023 Fudan NLP\nIn conclusion, while RLHF is a significant advancement in AI development, particularly in integrating human preferences into the learning process, it also presents unique challenges. These include the inherent noise and ambiguity in human feedback, potential biases in the data, and the generalization limits of reward models trained on specific datasets. Addressing these challenges is crucial for the advancement and ethical application of RLHF in AI systems.\n\nTo enhance the generalization ability of the reward model, we explore contrastive learning and\nmeta-learning. By introducing unsupervised contrastive loss during the reward modeling process, the reward model can better distinguish subtle preference differences among responses. To bridge the gap between the preference data distribution and the model output distribution, we employ meta-learning to ensure that the reward model not only performs well on the preference data but also can distinguish the differences in target domain outputs.\nTechnique is to randomize training data and do a k-fold split, build K models and then measure mean and deviations - negative mean is usually the mistakes. \nAccording to the results, we can observe that: 1) For the top 20% of data with the lowest preference strength, they have a negative impact on the model’s performance on the validation set. The preference strength for these data subsets is less than 0. 2) For data ranked between 20% and 40%, after training, the model’s prediction accuracy on the validation set is approximately 0.5. The preference strength for this type of data is around 0. 3) The remaining data significantly improves the model’s performance. However, the top 10% of data with the highest preference strength does not achieve the best performance when trained alone. Based on the above results, we can roughly categorize preference data into three types: incorrect data, ambiguous data (almost no difference), and normal data (clear differences). These three types of preference data play different roles and make different contributions to preference modeling. It is necessary for us to conduct a more detailed analysis of them and then consider how to handle each type.\n[LLARA] Making LLMs A Better Foundation For Dense Retrieval🔗\nArxiv: https://arxiv.org/abs/2401.06080 11 Jan 2024 Beijing Academy of AI\nIn this paper, we propose a novel approach, called LLaRA (LLM adapted for dense RetrievAl), which works as a post-hoc adaptation of LLM for the dense retrieval application. LLaRA consists of two pretext tasks: EBAE (Embedding-Based Auto-Encoding) and EBAR (Embedding-Based Auto-Regression), where the text embeddings from LLM are used to reconstruct the tokens for the input sentence and predict the tokens for the next sentence, respectively\n\nParticularly, there are two pretext training tasks introduced by LLaRA: EBAE (Embedding-Based Auto-Encoding) and EBAR (Embedding-Based Auto-Regression). In EBAE, the LLM is prompted to generate the text embeddings, which can be used to predict the tokens for the input sentence itself. While with EBAR, the LLM is prompted to generate the text embeddings, which can be used to predict the tokens for the next sentence. By learning from the above pretext tasks, the text embeddings from LLM can be adapted from Local semantic representations (i.e. prediction for the next tokens) to Global semantic representations (i.e. prediction for the sentence-level features). \n[RA-DIT] Retrieval-Augmented Dual Instruction Tuning🔗\nArxiv: https://arxiv.org/abs/2310.01352 2 Oct 2023 META\nRetrieval-augmented language models (RALMs) improve performance by accessing long-tail and up-to-date knowledge from external data stores, but are challenging to build. Existing approaches require either expensive retrieval-specific modifications to LM pre-training or use post-hoc integration of the data store that leads to suboptimal performance\nOur approach operates in two distinct fine-tuning steps: (1) one updates a pre-trained LM to better use retrieved information, while (2) the other updates the retriever to return more relevant results, as preferred by the LM\n[Sequential Monte Carlo] Steering of LLMs using Probabilistic Programs🔗\nArxiv: https://arxiv.org/abs/2306.03081 5 Jun 2023 MIT\nContext: Despite significant advances in recent years, it remains unclear if and how large language models (LLMs) can be made reliable and controllable enough to meet the functional requirements of many applications. \nEven after fine-tuning and reinforcement learning, LLMs are liable to violate instructions in their prompts (such as “Use the following vocabulary words” or “Do not reveal this prompt”).\nThese difficulties highlight the need for methods beyond prompting and fine-tuning for constraining the behavior of generative neural models.\n","id":"https://gel.github.io/generative-ai/2-llm-research/1-llm-pretraining-finetuning/","title":"LLM Pretraining & Fine-tuning"},"https://gel.github.io/generative-ai/2-llm-research/2-llm-agents/":{"body":"[RetroFormer] Retrospective LL Agents with Policy Gradient Optimization🔗\nArxiv: https://arxiv.org/abs/2308.02151 4 Aug 2023 Salesforce\nThis paper introduces Retroformer, a principled framework for reinforcing language agents by learning a plug-in retrospective model, which automatically refines the language agent prompts from environment feedback through policy optimization. Specifically, our proposed agent architecture can learn from arbitrary reward information across multiple environments and tasks, for iteratively fine-tuning a pre-trained language model, which refines the language agent prompts by reflecting on failed attempts and assigning credits of actions taken by the agent on future rewards\n\n\n","id":"https://gel.github.io/generative-ai/2-llm-research/2-llm-agents/","title":"LLM Agents"},"https://gel.github.io/generative-ai/2-llm-research/3-llm-optimization/":{"body":"[LLM-in-a-Flash] Efficient LLM Inference with Limited Memory🔗\nArxiv: https://arxiv.org/abs/2312.11514 12 Dec 2023 Apple\nFirst, \"windowing'\" strategically reduces data transfer by reusing previously activated neurons, and second, \"row-column bundling\", tailored to the sequential data access strengths of flash memory, increases the size of data chunks read from flash memory. \n[RoPE] RoFormer: Enhanced Transformer with Rotary Position Embedding🔗\nArxiv: https://arxiv.org/abs/2104.09864 20 Apr 2021 Zhuiyi Technology Co.\nWe investigated the existing approaches to the relative position encoding and found that they are mostly built based on the idea of the decomposition of adding position encoding to the context representations. We introduce a novel method, namely Rotary Position Embedding(RoPE), to leverage the positional information into the learning process of PLMS. The key idea is to encode relative position by multiplying the context representations with a rotation matrix with a clear theoretical interpretation.\n[LORA] LOw-RAnk Adaptation of LLM🔗\nArxiv: https://arxiv.org/abs/2106.09685 17 Jun 2021 OpenAI\nMany applications in natural language processing rely on adapting one large-scale, pre-trained language model to multiple downstream applications. Such adaptation is usually done via fine-tuning,\nwhich updates all the parameters of the pre-trained model. The major downside of fine-tuning is that the new model contains as many\nparameters as in the original model.\nMany sought to mitigate this by adapting only some parameters or learning external modules for new tasks. This way, we only need to store and load a small number of task-specific parameters in addition to the pre-trained model for each task, greatly boosting the operational efficiency when deployed. However, existing techniques often introduce inference latency by extending model depth or reducing the model’s usable sequence length. More importantly, these methods often fail to match the fine-tuning baselines, posing a trade-off between efficiency and model quality.\n[Speculative] Fast Inference from Transformers via Speculative Decoding🔗\nArxiv: https://arxiv.org/abs/2211.17192 30 Nov 2022 Google\nThe key observation above, that some inference steps are “harder” and some are “easier”, is also a key motivator for our work. We additionally observe that inference from large models is often not bottlenecked on arithmetic operations, but rather on memory bandwidth and communication, so additional computation resources might be available.\nTherefore we suggest increasing concurrency as a complementary approach to using an adaptive amount of computation. Specifically, we are able to accelerate inference without changing the model architectures, without changing the training-procedures or needing to re-train the models, and without changing the model output distribution. This is accomplished via speculative execution.\n\n[GQA] Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints🔗\nArxiv: https://arxiv.org/abs/2305.13245 22 May 2023 Google\n\n[Multi-Heads Sharing]Fast Transformer Decoding: One Write-Head is All You Need🔗\nArxiv: https://arxiv.org/abs/1911.02150 6 Nov 2019 Google\nMulti-head attention layers, as used in the Transformer neural sequence model, are a powerful alternative to RNNs for moving information across and between sequences. While training these layers is generally fast and simple, due to parallelizability across the length of the sequence, incremental inference (where such parallelization is impossible) is often slow, due to the memory-bandwidth cost of repeatedly loading the large \"keys'' and \"values\" tensors. We propose a variant called multi-query attention, where the keys and values are shared across all of the different attention \"heads\", greatly reducing the size of these tensors and hence the memory bandwidth requirements of incremental decoding. We verify experimentally that the resulting models can indeed be much faster to decode, and incur only minor quality degradation from the baseline.\nWe introduce multi-query Attention as a variation of multi-head attention as described in [Vaswani et al., 2017]. Multi-head attention consists of multiple attention layers (heads) in parallel with different linear transformations on the queries, keys, values and outputs. Multi-query attention is identical except that the different heads share a single set of keys and values.\n[MoE] Outrageously Large Neural Networks: The Sparsely-Gated Mixture-Of-Experts Layer🔗\nArxiv: https://arxiv.org/abs/1701.06538 23 Jan 2017 Google\nThe capacity of a neural network to absorb information is limited by its number of\nparameters. Conditional computation, where parts of the network are active on a\nper-example basis, has been proposed in theory as a way of dramatically increasing model capacity without a proportional increase in computation. In practice,\nHowever, there are significant algorithmic and performance challenges. In this\nwork, we address these challenges and finally realize the promise of conditional\ncomputation, achieving greater than 1000x improvements in model capacity with\nonly minor losses in computational efficiency on modern GPU clusters. We introduce a Sparsely-Gated Mixture-of-Experts layer (MoE), consisting of up to\nthousands of feed-forward sub-networks. A trainable gating network determines\na sparse combination of these experts to use for each example. \nWe present model architectures in which a MoE with up to 137 billion parameters is applied convolutionally between stacked LSTM layers. On large language modeling and machine translation benchmarks, these models achieve significantly better results than state-of-the-art at lower computational cost.\n[MoE] Mixture-of-Experts Meets Instruction Tuning: A Winning Combination for LLM🔗\nArxiv: https://arxiv.org/abs/2305.14705 24 May 2023 Google\nSparse Mixture-of-Experts (MoE) is a neural architecture design that can be utilized to add learnable parameters to Large Language Models (LLMs) without increasing inference cost. \n","id":"https://gel.github.io/generative-ai/2-llm-research/3-llm-optimization/","title":"LLM Optimization"},"https://gel.github.io/generative-ai/2-llm-research/4-llm-prompting/":{"body":"LLM Prompting - In-Context-Learning🔗\n[MedPrompt] Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine🔗\nArxiv: https://arxiv.org/abs/2311.16452 28 Nov 2023 Microsoft\nWe find that prompting innovation can unlock deeper specialist capabilities and show that GPT-4 easily tops prior leading results for medical benchmarks.\nWe find that GPT-4 benefits significantly from being allowed to design its prompt, specifically with coming up with its own chain-of-thought to be used for in-context learning. This observation echoes other reports that GPT-4 has an emergent self-improving capability via introspection, such as self-verification\nWe note that the automated chain-of-thought reasoning removes dependency on special human expertise and medical datasets. Thus, despite the name Medprompt, extending from the framing context and research trajectory of our investigation of the capabilities of GPT-4 on medical challenge problems, the methodology doesn’t include any components specifically oriented towards medicine.\nEnsembling is a technique for combining the outputs of multiple model runs to arrive at a more robust or accurate result via combining the separate outputs with functions like averaging, consensus, or majority vote. Ensembling methods employing a technique referred to as self-consistency [32] use a sampling method to produce multiple outputs that are then consolidated to identify a consensus output. The diversity of the outputs can be controlled by shifting the “temperature” parameter in a model’s generation, where higher temperatures can be viewed as injecting greater amounts of randomness into the generation process. By reordering or shuffling components of a few-shot prompt, ensembling techniques can also address the order sensitivity commonly found with foundation models [26, 39], thus improving robustness.\n\nA key challenge with this approach is that self-generated CoT rationales have an implicit risk of including hallucinated or incorrect reasoning chains. We mitigate this concern by having GPT-4 generate both a rationale and an estimation of the most likely answer to follow from that reasoning chain. If this answer does not match the ground truth label, we discard the sample entirely, under the assumption that we cannot trust the reasoning. While hallucinated or incorrect reasoning can still yield the correct final answer (i.e. false positives), we found that this simple label-verification step acts as an effective filter for false negatives.\nWhile less severe than other foundation models, GPT-4 can exhibit a propensity to favor certain options in multiple choice answers over others (regardless of the option content), i.e., the model can show position bias [1, 16, 40]. To reduce this bias, we propose shuffling the choices and then checking consistency of the answers for the different sort orders of the multiple choice. As a result, we perform choice shuffle and self-consistency prompting. Self-consistency [32] replaces the naive single-path or greedy decoding with a diverse set of reasoning paths when prompted multiple times at some temperature> 0, a setting that introduces a degree of randomness in generations. With choice shuffling, we shuffle the relative order of the answer choices before generating each reasoning path. We then select the most consistent answer, i.e., the one that is least sensitive to choice shuffling.\n\n[URIAL] The Unlocking Spell on Base LLMs: Rethinking Alignment via In-Context Learning🔗\nArxiv: https://arxiv.org/abs/2312.01552 _ 4 Dec 2023 **Allen Institute **_\nURIAL (Untuned LLMs with Restyled In-context ALignment). Based on these findings, we rethink the alignment of LLMs by posing the research question: how effectively can we align base LLMs without SFT or RLHF? To address this, we introduce a simple, tuning-free alignment method, URIAL. URIAL achieves effective alignment purely through in-context learning (ICL) with base LLMs, requiring as few as three constant stylistic examples and a system prompt\nOn the other hand, a recent study, LIMA (Zhou et al., 2023), proposes the “Superficial Alignment Hypothesis,” which argues that alignment tuning might simply teach base LLMs to select a sub distribution of data formats for interacting with users. Zhou et al. (2023) demonstrates that SFT with as few as 1,000 examples can also yield high-quality aligned models, thus providing indirect support for this hypothesis.\nWe analyze the effect of alignment tuning by examining the token distribution shift between base LLMs and their aligned counterpart (e.g., Llama-2 and Llama2-chat). Our findings reveal that base LLMs and their alignment-tuned versions perform nearly identically in decoding on the majority of token positions (i.e., they share the top-ranked tokens). Most distribution shifts occur with stylistic tokens (e.g., discourse markers, safety disclaimers). These direct evidence strongly supports the hypothesis that alignment tuning primarily learns to adopt the language style of AI assistants, and that the knowledge required for answering user queries predominantly comes from the base LLMs themselves.\n\n\n\n\nURIAL encourages stylistic outputs for ICL examples and a system prompt for in-context alignment.\n[CoVE] Chain-of-Verification Reduces Hallucinations in LLM Models🔗\nArxiv: https://arxiv.org/abs/2309.11495 25 Sep 2023 Meta\nWe develop the Chain-of-Verification (COVE) method whereby the model first (i) drafts an initial response; then (ii) plans verification questions to fact-check its draft; (iii) answers those questions independently so the answers ar`ze not biased by other responses; and (iv) generates its final verified response.\nThe hallucination problem can be exacerbated due to the issue of exposure bias (Wang & Sennrich, 2020).\n\n","id":"https://gel.github.io/generative-ai/2-llm-research/4-llm-prompting/","title":"LLM Prompting"},"https://gel.github.io/generative-ai/2-llm-research/5-llm-benchmarks/":{"body":"[Benchmark] Generating Benchmarks for Factuality Evaluation of Language Models🔗\nArxiv: https://arxiv.org/abs/2307.06908 13 Jul 2023 AI21 Labs\nThe key idea is automatically perturbing factual statements taken from the corpus to create a constant number of false variations (hereafter, 3) for each true statement (Figure 1). The LM’s FACTOR accuracy on our benchmark is defined as the percentage of examples for which it assigns higher likelihood to the factual completion than to any of the false variations. \n\n","id":"https://gel.github.io/generative-ai/2-llm-research/5-llm-benchmarks/","title":"LLM Benchmarks & Evaludations"},"https://gel.github.io/generative-ai/2-llm-research/6-llm-multimodal/":{"body":"Training Diffusion Models with RL🔗\nArxiv: https://openreview.net/pdf/c6a24bc50ce18fe080ef17ee8b448a66bd060e63.pdf 4 Jan 2024\n\nNormalization over contrastive prompts.\nPrompt synthesis via LLM.\nIncorporating textual inconsistency into the score (calculate distance in embedding space) - avoid synthetically close, semantically different.\n\n[DPOK] RL for Fine-tuning Text-to-Image Diffusion Models🔗\nArxiv: https://arxiv.org/abs/2305.16381 25 May 2023\nWe focus on diffusion models, defining the fine-tuning task as an RL problem, and updating the pre-trained text-to-image diffusion models using policy gradients to maximize the feedback-trained reward. Our approach, coined DPOK, integrates policy optimization with KL regularization.\nGeneration of more data includes generating n-1 negative samples and leveraging contrastive loss and generating more images to increase diversity.\nIn fine-tuning the loss function will be the expectancy of the sum of all the binary-human-classified dataset and also loss from the pre-training based data (weighted with B) to maintain accuracy of the model (avoid catastrophic forgetting). For the reward loss the idea is for the reward to be log-likelihood but it’s not easy, Therefore we minimize reward-weighted MSE loss instead.\nSetup: Pretrained Stable Diffusion 1.5, fine-tuning using static CLIP language encoder, Reward model is MLP using ViT-L/14 CLIP for image/text embeddings, Dataset 2700 prompts, 27k images, 16k unlabeled and 625k for pretraining.\nSFT: model is updated on a fixed dataset generated by the pre-trained model.\nRL: model is updated using new samples from the previously trained model during online RL fine-tuning.\nBased on the results, adding KL regularization helps in improving both image fidelity and accuracy (mostly image fidelty).\n[Point-E] A System for Generating 3D Point Clouds from Complex Prompts🔗\nArxiv: https://arxiv.org/abs/2212.08751 16 Dec 2022 OpenAI\nIn this paper, we explore an alternative method for 3D object generation which produces 3D models in only 1-2 minutes on a single GPU. Our method first generates a single synthetic view using a text-to-image diffusion model, and then produces a 3D point cloud using a second diffusion model which conditions on the generated image. \nUsing glade dataset for 2D (fine-tuned on 3D rendering).\n[CLIP] Connecting text and images🔗\nArxiv: https://arxiv.org/abs/2103.00020 26 Feb 2021 OpenAI\nCLIP pre-trains an image encoder and a text encoder to predict which images were paired with which texts in our dataset. We then use this behavior to turn CLIP into a zero-shot classifier. We convert all of a dataset’s classes into captions such as “a photo of a dog” and predict the class of the caption CLIP estimates best pairs with a given image.\n","id":"https://gel.github.io/generative-ai/2-llm-research/6-llm-multimodal/","title":"LLM Multi-Modal"},"https://gel.github.io/generative-ai/2-llm-research/7-llm-models/":{"body":"[Mixtral] Mixtral of Experts🔗\nArxiv: https://arxiv.org/abs/2401.04088 8 Jan 2024 Mixtral.ai\nWe introduce Mixtral 8x7B, a Sparse Mixture of Experts (SMoE) language model. Mixtral has the same architecture as Mistral 7B, with the difference that each layer is composed of 8 feedforward blocks (i.e. experts).\n\n\nG denotes n dimensionality of the gating network (router), E is the expert network.\n\nConsecutive tokens are often assigned to the same experts. In fact, we observe some degree of positional locality in The Pile datasets. Table 5 shows the proportion of consecutive tokens that get the same expert assignments per domain and layer. Figures are not showing it clearly.\n\n[Gemini] A Family of Highly Capable Multimodal Models🔗\nArxiv: https://arxiv.org/abs/2312.11805 19 Dec 2023 Google\nThe reasoning capabilities of large language models show promise toward building generalist agents that can tackle more complex multi-step problems.\n\n\n","id":"https://gel.github.io/generative-ai/2-llm-research/7-llm-models/","title":"LLM Models"},"https://gel.github.io/generative-ai/3-llm-implementation/":{"body":"In this section we will explore both datasets and LLMs implementation and examples.\nDatasets and Fine-Tuning: The Building Blocks of Instruction-Tuned Large Language Models🔗\nWelcome to the comprehensive guide on the datasets and fine-tuning processes that are pivotal in crafting instruction-tuned Large Language Models (LLMs). This section serves as an essential resource for researchers, practitioners, and AI enthusiasts who are delving into the specifics of instruction tuning and its applications in LLMs.\nWe will explore a variety of datasets specifically curated to train and evaluate instruction-tuned models, outline the methods and practices that have defined the field, and showcase domain-specific applications that bring out the versatility of these AI powerhouses.\nThese databases range from general-purpose instruction sets to task-specific collections, all aimed at enhancing the ability of LLMs to understand and execute complex instructions with precision.\nFor easy access and exploration, the following is a list of some of the most prominent datasets in the field of instruction tuning for LLMs:\n\nUnifiedQA: https://github.com/allenai/unifiedqa\nOpen Instruction Generalist: https://github.com/LAION-AI/Open-Instruction-Generalist\nUnifiedSKG: https://github.com/hkunlp/unifiedskg\nNatural Instructions V1: https://github.com/allenai/natural-instructions-v1\nNatural Instructions: https://github.com/allenai/natural-instructions\nBigScience P3: https://huggingface.co/datasets/bigscience/P3\nXMTF: https://github.com/bigscience-workshop/xmtf\nFLAN: https://github.com/google-research/FLAN\nCOIG: https://github.com/BAAI-Zlab/COIG\nUnnatural Instructions: https://github.com/orhonovich/unnatural-instructions\nSelf-Instruct: https://github.com/yizhongw/self-instruct\nInstructionWild: https://github.com/XueFuzhao/InstructionWild\nEvol-Instruct: https://github.com/nlpxucan/evol-instruct\nStanford ALPACA: https://github.com/tatsu-lab/stanford_alpaca\nLogiCoT: https://github.com/csitfun/LogiCoT\nDatabricks-Dolly-15K: https://huggingface.co/datasets/databricks/databricks-dolly-15k\nGPT-4 LLM: https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM\nGAIR Lima: https://huggingface.co/datasets/GAIR/lima\nGuanaco Dataset: https://huggingface.co/datasets/JosephusCheung/GuanacoDataset\nOpen Assistant: https://github.com/LAION-AI/Open-Assistant\nBaize Chatbot: https://github.com/project-baize/baize-chatbot\nUltraChat Data: https://github.com/thunlp/UltraChat#data\n\nThese resources are further augmented by visual examples of task instructions and their applications, enabling a more intuitive grasp of how instruction tuning operates within LLMs.\nAs we move forward, we will also delve into benchmarks like GSM8K, a dataset designed to evaluate the problem-solving capabilities of LLMs in a structured and rigorous manner.\nGSM8K - https://huggingface.co/datasets/gsm8k/viewer/main/train\nThis is just the beginning. Stay with us as we uncover more about the fine-tuning processes and the specific LLMs that have been enhanced through these methods, and ultimately, explore the impact of domain-specific instruction tuning on the capabilities of these AI models.\nThe Era of Large Language Models: Unveiling the Titans of Text🔗\nIn the dynamic and rapidly evolving world of artificial intelligence, Large Language Models (LLMs) stand as monumental achievements, showcasing the sheer potential of machine learning and natural language processing. From aiding in complex decision-making processes to generating creative content, LLMs have become integral to advancing the frontiers of what machines can understand and articulate.\nThe journey of LLMs has been marked by several pioneering models that have set new benchmarks in the field:\n\nGPT (Generative Pre-trained Transformer) series by OpenAI\nClaude\nGemini\nBERT (Bidirectional Encoder Representations from Transformers) by Google\nT5 (Text-to-Text Transfer Transformer) by Google AI\nBLOOM by BigScience\nOPT (Open Pre-trained Transformer) by Meta AI\n\nThese models, and others like them, have revolutionized our understanding of language's complexity and the capabilities of AI to mimic human linguistic skills.\nThe applications of LLMs are as varied as they are impactful. From creating more engaging virtual assistants to aiding in legal document analysis, LLMs are enhancing productivity, creativity, and problem-solving across sectors. Here are some domains where LLMs have made significant contributions:\n","id":"https://gel.github.io/generative-ai/3-llm-implementation/","title":"LLM Implementation"},"https://gel.github.io/generative-ai/3-llm-implementation/1-datasets/":{"body":"Datasets - Instruction Tuning🔗\n\n\n \n Figure: Natural language instructions databases\n\n1 https://github.com/allenai/unifiedqa\n\n2 https://github.com/LAION-AI/Open-Instruction-Generalist\n\n3 https://github.com/hkunlp/unifiedskg\n\n4 https://github.com/allenai/natural-instructions-v1\n\n5 https://github.com/allenai/natural-instructions\n\n6 https://huggingface.co/datasets/bigscience/P3\n\n7 https://github.com/bigscience-workshop/xmtf\n\n8 https://github.com/google-research/FLAN\n\n9 https://github.com/BAAI-Zlab/COIG\n\n10 https://github.com/orhonovich/unnatural-instructions\n\n11 https://github.com/yizhongw/self-instruct\n\n12 https://github.com/XueFuzhao/InstructionWild\n\n13 https://github.com/nlpxucan/evol-instruct\n\n14 https://github.com/tatsu-lab/stanford_alpaca\n\n15 https://github.com/csitfun/LogiCoT\n\n16 https://huggingface.co/datasets/databricks/databricks-dolly-15k\n\n17 https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM\n\n18 https://huggingface.co/datasets/GAIR/lima\n\n19 https://huggingface.co/datasets/JosephusCheung/GuanacoDataset\n\n20 https://github.com/LAION-AI/Open-Assistant\n\n21 https://github.com/project-baize/baize-chatbot\n\n22 https://github.com/thunlp/UltraChat#data\n \n \n\n\n\n \n\nDatasets - Benchmarks🔗\nGSM8K - https://huggingface.co/datasets/gsm8k/viewer/main/train\n","id":"https://gel.github.io/generative-ai/3-llm-implementation/1-datasets/","title":"Datasets"},"https://gel.github.io/generative-ai/3-llm-implementation/2-llms/":{"body":"Instruction-Fine-Tuned Datasets LLMs🔗\n\n\n \n 1 https://huggingface.co/bigscience/bloomz\n\n2 https://huggingface.co/google/flan-t5-xxl\n\n3 https://github.com/tatsu-lab/stanford_alpaca\n\n4 https://github.com/lm-sys/FastChat\n\n5 https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM\n\n6 https://github.com/nlpxucan/WizardLM\n\n7 https://github.com/THUDM/ChatGLM2-6B\n\n8 https://huggingface.co/facebook/opt-iml-30b\n\n9 https://github.com/databrickslabs/dolly\n \n 10 https://huggingface.co/tiiuae/falcon-40b-instruct\n\n11 https://huggingface.co/JosephusCheung/Guanaco\n\n12 https://huggingface.co/openaccess-ai-collective/minotaur-15b\n\n13 https://huggingface.co/NousResearch/Nous-Hermes-13b\n\n14 https://github.com/allenai/open-instruct\n\n15 https://github.com/RUC-GSAI/YuLan-Chat\n\n16 https://github.com/OpenLMLab/MOSS\n\n17 https://github.com/jondurbin/airoboros\n\n18 https://github.com/thunlp/UltraChat\n \n \n\nPEFT Methods: LoRA, HINT, Qlora, LOMO, Delta-tuning\nEvaludation Methods: HELM, Low-resource Instruction Tuning\nDomain-Specific Fine-Tuned LLMs🔗\n\n\n \n 1 https://github.com/prakharguptaz/Instructdial\n\n2 https://github.com/BeyonderXX/InstructUIE\n\n3 https://github.com/amazon-science/instruction-tuning-for-absa\n\n4 https://github.com/facebookresearch/EditEval\n\n5 https://github.com/vipulraheja/coedit\n\n6 https://github.com/vishakhpk/creative-instructions\n \n 7 https://huggingface.co/spaces/allen-eric/radiology-gpt\n\n8 https://github.com/Kent0n-Li/ChatDoctor\n\n9 https://github.com/SCIR-HI/Med-ChatGLM\n\n10 https://github.com/liutiedong/goat\n\n11 https://github.com/nlpxucan/WizardLM\n \n \n\n","id":"https://gel.github.io/generative-ai/3-llm-implementation/2-llms/","title":"Large-Language-Models"},"https://gel.github.io/interviews/1-intro/":{"body":"Developing effective interview skills presents difficulties for those established in their current roles. Pursuing employment opportunities with other companies commonly necessitates substantial time and effort. However, seeking new positions while still employed offers advantages, including the abilities to:\n\n\nNegotiate improved employment agreements with greater confidence.\n\n\nDemonstrate self-assurance throughout the process.\n\n\nProvide relevant, timely examples and metrics from one's work history.\n\n\nWith experience transitioning between over five positions (both vertical promotions and lateral moves), I have chosen to record these steps and refine the process to benefit both myself and others.\n","id":"https://gel.github.io/interviews/1-intro/","title":"Interviews Introduction"},"https://gel.github.io/interviews/1-intro/1-making-a-career-move/":{"body":"Changing jobs brings considerable challenges that many avoid. Common psychological barriers include:\n\n\nFear of rejection, often stemming from impostor syndrome.\n\n\nApprehension about new beginnings.\n\n\nLack of motivation.\n\n\nPerceived lack of time and agency.\n\n\nConcern about retaliation.\n\n\nInability to identify better opportunities.\n\n\nHowever, none of these fears are truly justified and can be logically overcome.\nThe Process🔗\nThe first step in deciding to switch jobs is examining one's motivations. Next, weigh the pros and cons visually. Finally, create a decision grid and consult trusted connections like family, friends or colleagues before settling on a firm yes or no.\nSome common personal motivations are:\n\n\nDesire for improved compensation (e.g. moving to a higher-paying senior role).\n\n\nCurrent role feels unchallenging, boring and/or repetitive.\n\n\nToxic work environment (work-life balance, stress, lack of influence).\n\n\nPoor team and leadership (unenjoyable colleagues, no mentors, high attrition).\n\n\nEach of these motivations can be evaluated on its own merits and should provide enough rationale to determine whether one should stay or move on.\n","id":"https://gel.github.io/interviews/1-intro/1-making-a-career-move/","title":"Making a Career Move"},"https://gel.github.io/interviews/1-intro/2-finding-the-best-job/":{"body":"It's important to recognize that no job is 100% perfect. However, with the right approach, you can identify opportunities that are the best fit for your skills, interests and professional goals.\nTo discover appealing roles, first ensure you have sufficient options to evaluate and select the most suitable position for you. Useful strategies include:\n\n\nInvesting time in optimizing your LinkedIn profile and activity so recruiters can more easily find you.\n\n\nScheduling informational meetings with former colleagues to uncover unofficial job leads at their companies.\n\n\nTelling friends, family, and broader network about your job search plans so they can share relevant openings.\n\n\nScanning your existing professional connections and employer to identify where people in your field are getting hired.\n\n\nResearching the job market comprehensively to understand salary ranges, in-demand skills, company cultures, etc. This provides helpful context.\n\n\nCustomizing your resume and cover letters to align with each potential job's requirements.\n\n\nSeeking referral opportunities from your network to increase chances of getting interviews.\n\n\nPracticing your interview skills to feel confident during the hiring process.\n\n\nAsking thoughtful questions of hiring managers and employees to assess company/role fit.\n\n\nNegotiating job offers strategically to maximize compensation, flexibility, growth opportunities, etc.\n\n\nWith preparation and commitment to exploring all promising leads, you can find a job that checks most of your key boxes for an enjoyable and successful career move.\n","id":"https://gel.github.io/interviews/1-intro/2-finding-the-best-job/","title":"Finding an Optimal Job"},"https://gel.github.io/interviews/1-intro/3-passing-the-interview/":{"body":"Schedule Practice Interviews🔗\nBook two mock interviews within the next 14 days with companies you are not interested in working for. Ideally, schedule the first phone screening 7 days out. This will motivate you to prepare even when time is tight. Remember, most companies allow candidates to re-interview after 6-12 months if needed, so don't stress.\nChoose One Primary Coding Language🔗\nAlthough being a jack-of-all-trades with multiple programming languages is preferable on the job, it can be counterproductive in interviews. The key is optimizing the time between conceiving a solution and writing syntactically correct code. Therefore, picking a functional language you know very well is recommended.\nSelect the programming language you actively use and feel most proficient in as your go-to for interview coding. This ensures you can demonstrate your skills accurately.\nOther Key Strategies🔗\n\n\nThoroughly review the job description and research the company to anticipate likely interview questions and expectations.\n\n\nPractice discussing your resume, projects, and skills out loud to improve your articulation and confidence.\n\n\nPrepare stories that highlight times you successfully solved complex problems, overcame challenges, collaborated, etc.\n\n\nBrush up on computer science fundamentals like data structures and algorithms. Review coding patterns and syntax for your chosen language.\n\n\nPlan for behavioral and technical questions, but also prepare some insightful questions to ask the interviewer to show interest.\n\n\nDress professionally and arrive early to interviews to make a good impression.\n\n\nWith practice and dedication to sharpening your interview abilities, you can showcase your qualifications persuasively and land the perfect job.\n","id":"https://gel.github.io/interviews/1-intro/3-passing-the-interview/","title":"Acing the Interview Process"},"https://gel.github.io/interviews/2-data-structres-and-algorithms/":{"body":"A critical first step in preparing for any programming-related job interview is ensuring you have command of fundamental computer science data structures and algorithms. While you won't need to have every detail memorized, being generally familiar with the most common options is essential. \nHere are some of the key data structures and algorithms to review and practice implementing:\n\nLinked Lists - singly, doubly, circular\nStacks \nQueues\nTrees - binary, binary search, AVL, red-black, segment \nHeaps - min-heap, max-heap\nHash Tables\nGraphs - adjacency list, adjacency matrix, weighted\nSorting Algorithms - quicksort, mergesort, heapsort, insertion sort\nSearch Algorithms - binary search, DFS, BFS \nDynamic Programming - memoization, tabulation\n\nWhen studying these data structures and algorithms, focus on:\n\nUse cases - when is each most appropriate?\nTime and space complexity - Big O analysis\nCommon operations - insertion, deletion, traversal, sorting, etc.\nImplementation tradeoffs \nStrengths and limitations\nCoding them from scratch in your chosen language \n\nWith strong grasp of these fundamental building blocks, you will have the core CS knowledge needed to analyze and solve problems efficiently in interviews. Be ready to walk through your code, explain your design choices, discuss complexity, and compare approaches.\nGoing beyond familiarity to deep expertise with data structures and algorithms will help you stand out and succeed in landing your ideal programming role.\n","id":"https://gel.github.io/interviews/2-data-structres-and-algorithms/","title":"Data structures and Algorithms"},"https://gel.github.io/interviews/2-data-structres-and-algorithms/1-data-structures/":{"body":"\nHashtable\nTree\nGraph\nHeap\nBasic Data Structures\nProbablistic Data Structures\n\n\nHashtable🔗\nConstant lookup - O(1) - uses hash function and list of entries for collisions.\nTree🔗\nBinary Tree - Has up to 2 child nodes.\nBST (Binary Search Tree) - Binary tree where left <= parent <= right - since BST is not balanced it has O(N) bound.\nAVL tree - Balanced(self-balanced) BST, common operation is rotation/shift (L, R, LR, RL) - all balanced trees will have O(Log(N)) bound.\nB-Tree - Balanced(self-balanced) BST, generalization of binary tree, nodes has more children. Used for databases to require less reads (n keys).\nTrie (prefix tree) - Tree to locate specific keys (mostly strings by traversing individual characters). A node's position in the trie defines the key with which it is associated.\nGraph🔗\nAdjacency matrix - size N*M - binary value(0/1) to represent existance of an edge.\nNodes and edges sets\nHeap (Min / Max / Priority Queue)🔗\nOperationBoundExplanation\nPeakO(1)Read\nPoll/Extract/InsertO(Log(N))It is still requires to fix the underlying tree\nBuild HeapO(N)Unlike the intuition O(N*Log(N)) is not strict and since the tree is balanced the complexity is bound to the height ` (0 * n/2) + (1 * n/4) + (2 * n/8) + ... + (h * 1)).\n\nBasic Data Structures🔗\n\nArray\nStack\nQueue\nDequeue\nList\nDoubly-linked List\n\nProbablistic Data Structures🔗\nBloom Filter (membership problem) - no false negatives (always return the member), built by 2D binary array of buckets (B) * number of hash functions (L) = BxL.\nCount Min Sketch (approximate heavy-hitters problem) - similar to bloom filter with integer(count) array, error correlates to number of L and B size (we can’t choose epsilon = 0 since it equals to infinity memory).\n","id":"https://gel.github.io/interviews/2-data-structres-and-algorithms/1-data-structures/","title":"Data Structures"},"https://gel.github.io/interviews/2-data-structres-and-algorithms/2-algorithms/":{"body":"\nSorting Algorithms\n\nQuicksort\nInsertion Sort\nMerge Sort\nBucket Sort\nCounting Sort\n\n\n\n\nSorting Algorithms🔗\nAlgorithmWorstAverageBestSpaceNotes\nQuicksortO(n^2)O(n*log*(n))O(n*log*(n))O(log*(n))\nMergesortO(n*log*(n))O(n*log*(n))O(n*log*(n))O(n)\nBubble SortO(n^2)O(n^2)O(n))O(1)Never use!\nInsertion SortO(n^2)O(n^2)O(n))O(1)Less swaps - more efficient\nSelection SortO(n^2)O(n^2)O(n^2))O(1)Less swaps\nBucket SortO(n^2)O(n+k)O(n+k))O(n)\nCounting SortO(n+k)O(n+k)O(n+k))O(k)\n\nQuicksort🔗\nMost popular / optimized on average sorting algorithm for small data-sets (unstable - position based on pivot selection). O(N^2) worst case O(N *Log(N)) average case. \nLomuto partitioning (last elm), Hoare partitioning (random / middle - mostly performs better).\nQuickSelect is a selection algorithm to find kth smallest element (Ohare), it’s a partial quick-sort algorithm - sort only one side of recursion partitioning - O(logn) or it’s O(n)\n// Sorts a (portion of an) array, divides it into partitions, then sorts those\nalgorithm quicksort(A, lo, hi) is \n if lo >= 0 && hi >= 0 && lo < hi then\n p := partition(A, lo, hi) \n quicksort(A, lo, p) // Note: the pivot is now included\n quicksort(A, p + 1, hi) \n\n // Divides array into two partitions\n algorithm partition(A, lo, hi) is \n // Pivot value\n pivot := A[ floor((hi + lo) / 2) ] // The value in the middle of the array\n // Left index\n i := lo - 1 \n // Right index\n j := hi + 1\n\n loop forever \n // Move the left index to the right at least once and while the element\n // at the left index is less than the pivot \n do i := i + 1 while A[i] < pivot \n \n // Move the right index to the left at least once and while the element \n // at the right index is greater than the pivot \n do j := j - 1 while A[j] > pivot \n\n // If the indices crossed, return\n if i ≥ j then return j\n \n // Swap the elements at the left and right indices\n swap A[i] with A[j]\n\nInsertion Sort🔗\nSimplest sorting algorithm (stable) - O(N^2) worst and average case.\n i ← 1\n while i < length(A)\n j ← i\n while j > 0 and A[j-1] > A[j]\n swap A[j] and A[j-1]\n j ← j - 1\n end while\n i ← i + 1\n end while\n\nMerge Sort🔗\nVery efficient for large data-sets - O(N*Log(N)) average and worst case.\n// Sorting the entire array is accomplished by TopDownMergeSort(A, B, length(A)). \n\n// Array A[] has the items to sort; array B[] is a work array.\nvoid TopDownMergeSort(A[], B[], n)\n{\n CopyArray(A, 0, n, B); // one time copy of A[] to B[]\n TopDownSplitMerge(B, 0, n, A); // sort data from B[] into A[]\n}\n\n// Split A[] into 2 runs, sort both runs into B[], merge both runs from B[] to A[]\n// iBegin is inclusive; iEnd is exclusive (A[iEnd] is not in the set).\nvoid TopDownSplitMerge(B[], iBegin, iEnd, A[])\n{\nif (iEnd - iBegin <= 1) // if run size == 1\n return; // consider it sorted\n // split the run longer than 1 item into halves\n iMiddle = (iEnd + iBegin) / 2; // iMiddle = mid point\n // recursively sort both runs from array A[] into B[]\n TopDownSplitMerge(A, iBegin, iMiddle, B); // sort the left run\n TopDownSplitMerge(A, iMiddle, iEnd, B); // sort the right run\n // merge the resulting runs from array B[] into A[]\n TopDownMerge(B, iBegin, iMiddle, iEnd, A);\n}\n\n// Left source half is A[ iBegin:iMiddle-1].\n// Right source half is A[iMiddle:iEnd-1 ].\n// Result is B[ iBegin:iEnd-1 ].\nvoid TopDownMerge(A[], iBegin, iMiddle, iEnd, B[])\n{\n i = iBegin, j = iMiddle;\n // While there are elements in the left or right runs...\n for (k = iBegin; k < iEnd; k++) {\n // If left run head exists and is <= existing right run head.\n if (i < iMiddle && (j >= iEnd || A[i] <= A[j])) {\n B[k] = A[i];\n i = i + 1;\n } else {\n B[k] = A[j];\n j = j + 1;\n }\n }\n }\n\n void CopyArray(A[], iBegin, iEnd, B[])\n {\n for (k = iBegin; k < iEnd; k++)\n B[k] = A[k];\n }\n\nBucket Sort🔗\nUsed as a distribution sort.\nRuntime = O(N^2) worst case, O(n + n^2/k + k) average case when k = buckets\nSpace = O(N*k)\n\nSet up an array of initially empty \"buckets\".\nScatter: Go over the original array, putting each object in its bucket.\nSort each non-empty bucket.\nGather: Visit the buckets in order and put all elements back into the original array.\n\nExample:\nBucket 1Bucket 2Bucket 3\n0-910-9910-999\n1,3333\n\nRadix / digital sort🔗\nUsed to sort numbers according to their base (specific case of bucket sort)\nRuntime: O(nw) time, where n is the number of keys, and w is the key length.\nCounting Sort🔗\nUsed when the keys distribution is low compared to number of elements\nRuntime O(N+K) runtime. Bucket sort can be used instead (but required dynamic allocation of memory)\nfunction CountingSort(input, k) \n count ← array of k + 1 zeros\n output ← array of same length as input\n \n for i = 0 to length(input) - 1 do\n j = key(input[i])\n count[j] += 1\n\n for i = 1 to k do\n count[i] += count[i - 1]\n\n for i = length(input) - 1 downto 0 do\n j = key(input[i])\n count[j] -= 1\n output[count[j]] = input[i]\n\n return output\n\n","id":"https://gel.github.io/interviews/2-data-structres-and-algorithms/2-algorithms/","title":"Algorithms"},"https://gel.github.io/interviews/3-coding-problems/":{"body":"When tackling a technical interview coding challenge, a systematic approach is key:\n\n\nFirst conceptualize a brute force solution and explain it thoroughly without coding. This demonstrates you can reason through the problem.\n\n\nNext, identify potential optimizations and edge cases. Consult the interviewer to validate your thinking. Communication is vital!\n\n\nFinally, code the most optimal solution in a structured, readable style. While concise code has merits, prioritize:\n\n\n\nProper object-oriented design principles \nDescriptive variable/function names\nModularization into helper functions \nComments explaining complex parts\n\nBeware solutions online that sacrifice readability for brevity. Well-structured, easy-to-follow code that showcases your abilities is ideal for interviews.\nAdditional Best Practices:\n\n\nAsk clarifying questions before diving in. Validate requirements and constraints.\n\n\nThink through test cases out loud to catch edge cases early.\n\n\nDescribe your approach and code aloud as you write it.\n\n\nExplain time/space complexity analysis of your solution.\n\n\nSuggest follow-up enhancements to show long-term thinking.\n\n\nWith care taken to communicate effectively, write readable code, and demonstrate strong CS fundamentals, you can tackle any interview coding challenge successfully. The key is staying calm, collected, and methodical in your problem solving approach.\n","id":"https://gel.github.io/interviews/3-coding-problems/","title":"Solving Coding Interview Problems"},"https://gel.github.io/interviews/3-coding-problems/1-logical-and-maintainable/":{"body":"\nRange Sum of BST - Easy - LeetCode 938\nEvaluate Reverse Polish Notation - Easy - LeetCode 150\nTwo Sum - Easy - LeetCode 1\nBest Time to Buy and Sell Stock - Easy - LeetCode 121\nPartitioning Into Minimum Number Of Deci-Binary Numbers - Medium - LeetCode 1689\nInsert Greatest Common Divisors in Linked List - Medium - LeetCode 2807\n\n\nRange Sum of BST - Easy - LeetCode 938🔗\n\nGiven the root node of a binary search tree and two integers low and high, return the sum of values of all nodes with a value in the inclusive range [low, high].\n\nExplanation\nTraversal of the tree in any order is fine and make sure you don't traverse the unnecessary parts of the tree. Recrusion or iterative solution are acceptable (make sure you can explain that any recursive solution can be converted to an iterative solution).\nSolution\nclass RangeSumBST {\n public int rangeSumBST(TreeNode root, int low, int high) {\n Queue<TreeNode> queue = new LinkedList<TreeNode>();\n queue.add(root);\n \n int sum = 0;\n while (!queue.isEmpty()) {\n TreeNode node = queue.poll();\n if (node.val >= low && node.val <= high)\n sum += node.val;\n \n if (node.left != null && node.val >= low)\n queue.add(node.left);\n \n if (node.right != null && node.val <= high)\n queue.add(node.right);\n }\n return sum;\n }\n}\n\n\nEvaluate Reverse Polish Notation - Easy - LeetCode 150🔗\nQuestion\n\nEvaluate the value of an arithmetic expression in > Reverse Polish Notation.\nValid operators are +, -, *, and /. Each operand may > be an integer or another expression.\nNote that division between two integers should > truncate toward zero.\nIt is guaranteed that the given RPN expression is > always valid. That means the expression would always > evaluate to a result, and there will not be any > division by zero operation.\n\nExplanation\nTo perform operation in polish notation we need a stack data-structure (first in last out). We will read the tokens until we encounter operation and then perform the calculation.\nSolution\nclass ReversePolishNotation {\n private static boolean isNumeric(String token) {\n try { \n Double.parseDouble(token); \n return true;\n } catch(NumberFormatException e) { \n return false; \n }\n }\n \n private static int performCalculation(int left, int right, String operand) {\n if (operand.equals(\"+\")) {\n return left + right;\n } else if (operand.equals(\"-\")) {\n return left - right;\n } else if (operand.equals(\"*\")) {\n return left * right;\n } else if (operand.equals(\"/\")) {\n return left / right;\n } else {\n System.out.println(\"left: \" + left + \" right: \" + right + \" operand: \" + operand);\n throw new RuntimeException(\"Unexpected operand\");\n }\n }\n \n public int evalRPN(String[] tokens) {\n Stack<Integer> numbers = new Stack<Integer>();\n for (int i = 0; i < tokens.length; i++) {\n String token = tokens[i];\n if (!isNumeric(token)) {\n int right = numbers.pop();\n int left = numbers.pop();\n int result = performCalculation(left, right, token);\n numbers.add(result);\n } else {\n numbers.add(Integer.parseInt(token));\n }\n }\n return numbers.pop();\n }\n}\n\n\nTwo Sum - Easy - LeetCode 1🔗\nQuestion\n\nGiven an array of integers nums and an integer target, return indices of the two numbers such that they add up to target.\n\nExplanation\nIn this question we need to find elem + X = target => X = target - elem;\nA common solution is to create a map from number to index (one pass).\nThen leverage it during traversal to check if the element exist (second pass).\nSince this problem is a sum of two elements we can do it in a single pass (the second element will have the first one in the data-structre).\nSolution\npublic class TwoSum {\n public int[] twoSum(int[] nums, int target) {\n Map<Integer, Integer> numberToIndex = new HashMap<>();\n int[] result = new int[2]; // number, index\n for (int i = 0; i < nums.length; i++) {\n int needed = target - nums[i];\n if (numberToIndex.containsKey(needed)) {\n Integer index = numberToIndex.get(needed);\n result[0] = i;\n result[1] = index;\n return result;\n }\n\n numberToIndex.put(nums[i], i);\n }\n\n throw new IllegalArgumentException(\"Invalid input\");\n }\n\n\nBest Time to Buy and Sell Stock - Easy - LeetCode 121🔗\nQuestion\n\nYou are given an array prices where prices[i] is the price of a given stock on the ith day.\n\n\nYou want to maximize your profit by choosing a single day to buy one stock and choosing a different day in the future to sell that stock.\n\n\nReturn the maximum profit you can achieve from this transaction. If you cannot achieve any profit, return 0.\n\nExplanation\nWe are looking for the largest difference (min / max). Therefore we need to do a simple book keeping and keep track of the current minimum and profit we have. While we traverse the array we update the min if necessary and record the profit if it's bigger than what we have. I wrote my initial solution but there is no need to keep track of the current profit (we can just record max profit when needed).\nSolution\nclass BestTimeToSellStock {\n public int maxProfit(int[] prices) {\n int size = prices.length;\n if (size <= 1)\n return 0;\n \n int currentMin = prices[0];\n int currentProfit = 0;\n int maxProfit = 0;\n \n for (int i = 1; i < size; i++) {\n if (prices[i] < currentMin) {\n maxProfit = Math.max(maxProfit, currentProfit);\n currentProfit = 0;\n currentMin = prices[i];\n } else {\n int candidateProfit = prices[i] - currentMin;\n currentProfit = Math.max(candidateProfit, currentProfit);\n }\n }\n maxProfit = Math.max(maxProfit, currentProfit);\n \n return maxProfit;\n }\n}\n\nPartitioning Into Minimum Number Of Deci-Binary Numbers - Medium - LeetCode 1689🔗\nQuestion\nA decimal number is called deci-binary if each of its digits is either 0 or 1 without any leading zeros. For example, 101 and 1100 are deci-binary, while 112 and 3001 are not.\nGiven a string n that represents a positive decimal integer, return the minimum number of positive deci-binary numbers needed so that they sum up to n.\nExample 1:\nInput: n = \"32\"\nOutput: 3\nExplanation: 10 + 11 + 11 = 32\nExample 2:\nInput: n = \"82734\"\nOutput: 8\nExample 3:\nInput: n = \"27346209830709182346\"\nOutput: 9\nConstraints:\n\\( 1 \\le n.length \\le 105 \\)\nn consists of only digits.\nn does not contain any leading zeros and represents a positive integer.\nExplanation\nThis question relies on logic, since we can use only deci-binary numbers (zero or 1 in each digit) it means that in order to build a specific digit k we will need at-least k different deci numbers. Therefore, the answer is the maximum of all digits in n max(k)\nSolution\nclass MinPartitionsSolutions {\n public int minPartitions(String n) {\n int max = 0;\n for(int i = 0; i < n.length(); i++){\n int digit = n.charAt(i) - '0';\n if (digit > max) {\n max = digit;\n }\n }\n return max;\n }\n}\n\nInsert Greatest Common Divisors in Linked List - Medium - LeetCode 2807🔗\nQuestion\nGiven the head of a linked list head, in which each node contains an integer value.\nBetween every pair of adjacent nodes, insert a new node with a value equal to the greatest common divisor of them.\nReturn the linked list after insertion.\nThe greatest common divisor of two numbers is the largest positive integer that evenly divides both numbers.\nExample 1:\nInput: head = [18,6,10,3]\nOutput: [18,6,6,2,10,1,3]\nExplanation: The 1st diagram denotes the initial linked list and the 2nd diagram denotes the linked list after inserting the new nodes (nodes in blue are the inserted nodes).\n\nWe insert the greatest common divisor of 18 and 6 = 6 between the 1st and the 2nd nodes.\nWe insert the greatest common divisor of 6 and 10 = 2 between the 2nd and the 3rd nodes.\nWe insert the greatest common divisor of 10 and 3 = 1 between the 3rd and the 4th nodes.\nThere are no more adjacent nodes, so we return the linked list.\nExample 2:\n\nInput: head = [7]\nOutput: [7]\nExplanation: The 1st diagram denotes the initial linked list and the 2nd diagram denotes the linked list after inserting the new nodes.\nThere are no pairs of adjacent nodes, so we return the initial linked list.\nConstraints:\nThe number of nodes in the list is in the range [1, 5000].\n\\( 1 \\le Node.val \\le 1000 \\)\nExplanation\nThe idea is to break the solution into two different parts.\n\n\nImplement GCD. The recursive way is the easiest, a side note is that it's usually not recommended to implement recursion but in this case because it is tail recursion the compiler or JVM would be able to optimize it if needed.\n\n\nAdd a new node from the GCD value.\n\n\nSolution\n\n/**\n * Definition for singly-linked list.\n * public class ListNode {\n * int val;\n * ListNode next;\n * ListNode() {}\n * ListNode(int val) { this.val = val; }\n * ListNode(int val, ListNode next) { this.val = val; this.next = next; }\n * }\n **/\nclass GreatestCommonDivisorsSolution {\n public ListNode insertGreatestCommonDivisors(ListNode head) {\n ListNode curr = head;\n while (curr != null && curr.next != null) {\n int newVal = gcd(curr.val, curr.next.val);\n // insert new node between curr and curr.next with newVal\n curr.next = new ListNode(newVal, curr.next);\n curr = curr.next.next;\n }\n return head;\n }\n\n private int gcd(int a, int b) {\n if (b == 0) return a;\n return gcd(b, a % b);\n }\n}\n\n\n","id":"https://gel.github.io/interviews/3-coding-problems/1-logical-and-maintainable/","title":"Logical and Maintainable"},"https://gel.github.io/interviews/3-coding-problems/2-problem-solving/":{"body":"\nLRU Cache - Medium - LeetCode 146\nLongest Palindromic Substring - Medium - LeetCode 5\n\n\nLRU Cache - Medium - LeetCode 146🔗\nQuestion\n\nDesign a data structure that follows the constraints of a Least Recently Used (LRU) cache.\n\n\nImplement the LRUCache class:\n\n\nLRUCache(int capacity) Initialize the LRU cache with positive size capacity.\nint get(int key) Return the value of the key if the key exists, otherwise return -1.\nvoid put(int key, int value) Update the value of the key if the key exists. Otherwise, add the > key-value pair to the cache. If the number of keys exceeds the capacity from this operation, > evict the least recently used key.\nThe functions get and put must each run in O(1) average time complexity.\n\nExplanation\nLRU cache is a challenging problem, the problem is mostly focused on how to store the keys efficiently for removal by time inserted. Therefore in addition to the k/v hashtable we need an additional data-structure that allows retrieval, deletion and modification. Heap / Tree based solution are all applicable solutions but requires O(Log(N)) for insertion and removal.\nIn this problem it is possible to do a constant time when choosing a doubly linked list which allows to add to head and evicti from tail. It also possible to perform modification in constant (move to HEAD).\nThis is a java implemention of DoublyLinkedList and the LRU Cache. \nSolution\nclass DoublyLinkedList {\n public Node root;\n public Node tail;\n\n public Node addToHead(final int key) {\n if (root == null) {\n this.root = new Node(key, null, null);\n this.tail = this.root;\n } else {\n Node node = new Node(key, this.root, null);\n this.root.previous = node;\n this.root = node;\n }\n return this.root;\n }\n\n public void moveToHead(final Node node) throws IllegalArgumentException {\n if (this.root == null) {\n throw new RuntimeException(\"Invalid head state\");\n }\n\n if (this.root == node) { // node is already head\n return;\n }\n\n if (this.tail == node) { // node is the tail - fix it\n this.tail = this.tail.previous;\n }\n\n if (node.previous != null) { // fix existing previous of node\n node.previous.next = node.next;\n }\n\n if (node.next != null) { // fix existing next of node\n node.next.previous = node.previous;\n }\n\n // set as head\n node.previous = null;\n node.next = this.root;\n this.root.previous = node;\n this.root = node;\n }\n\n public Node evictTail() {\n if (this.root == null) { // list is empty\n return null;\n }\n\n if (this.root == this.tail) {\n Node reference = this.root;\n this.root = null;\n this.tail = null;\n return reference;\n }\n\n Node existingTail = this.tail;\n existingTail.previous.next = null;\n this.tail = existingTail.previous;\n return existingTail;\n }\n}\n\nclass LRUCache {\n final int capacity;\n int size;\n final Map<Integer, CacheEntry> cache;\n final DoublyLinkedList sortedTtlList;\n\n static class CacheEntry {\n public CacheEntry(int value, Node node) {\n this.value = value;\n this.node = node;\n }\n\n public int value;\n public Node node;\n }\n\n public LRUCache(int capacity) {\n this.capacity = capacity;\n this.size = 0;\n this.cache = new HashMap<>();\n this.sortedTtlList = new DoublyLinkedList();\n }\n\n public int get(int key) {\n if (!cache.containsKey(key)) {\n return -1;\n }\n\n CacheEntry entry = cache.get(key);\n sortedTtlList.moveToHead(entry.node);\n return entry.value;\n }\n\n public void put(int key, int value) {\n if (cache.containsKey(key)) {\n CacheEntry entry = cache.get(key);\n sortedTtlList.moveToHead(entry.node);\n entry.value = value;\n } else {\n if (this.size < this.capacity) {\n this.size = this.size + 1;\n } else {\n Node nodeToRemove = sortedTtlList.evictTail();\n cache.remove(nodeToRemove.key);\n }\n final Node newNode = sortedTtlList.addToHead(key);\n cache.put(key, new CacheEntry(value, newNode));\n }\n }\n}\n\n\nLongest Palindromic Substring - Medium - LeetCode 5🔗\nQuestion\n\nGiven a string s, return the longest palindromic substring in s.\n\nInput: s = \"babad\"\nOutput: \"bab\"\nExplanation: \"aba\" is also a valid answer.\n\nExplanation\nThe simplest solution is calling isPalindrome for every substring. Checking palindrome is O(N) and all substrings are O(N^2) - therefore O(N^3).\nThis problem can be converted to a dynamic programming problem where Palindrome(i, j) = Palindrome(i+1, j-1) && s[i] == s[j] (same for odd and even cases). This will require O(N^2) time and O(N^2) memory.\nThere is a simpler way to think about this problem. We can think about a palindrome as a center-based string and then check the palindrome from all of the centers.\nSolution\npublic class LongestPalindrome {\n private static class PalindromeSequence {\n public int startIndex;\n public int length;\n\n PalindromeSequence() {\n this.startIndex = 0;\n this.length = 1;\n }\n }\n\n private void checkAndUpdateLongestPalindrome(String s, int leftIndex, int rightIndex, PalindromeSequence maxPalindrome) {\n while (leftIndex >= 0 && rightIndex < s.length() && s.charAt(leftIndex) == s.charAt(rightIndex)) {\n leftIndex -= 1;\n rightIndex += 1;\n }\n\n int existingLength = rightIndex - leftIndex - 1;\n if (existingLength > maxPalindrome.length) {\n maxPalindrome.startIndex = leftIndex + 1;\n maxPalindrome.length = existingLength;\n }\n }\n\n public String longestPalindrome(String s) {\n PalindromeSequence maxPalindrome = new PalindromeSequence();\n\n for (int i = 0; i < s.length(); i++) {\n int rightIndex = i;\n int leftIndex = i - 1;\n checkAndUpdateLongestPalindrome(s, leftIndex, rightIndex, maxPalindrome);\n\n leftIndex = i - 1;\n rightIndex = i + 1;\n checkAndUpdateLongestPalindrome(s, leftIndex, rightIndex, maxPalindrome);\n }\n\n return s.substring(maxPalindrome.startIndex, maxPalindrome.startIndex + maxPalindrome.length);\n }\n}\n\nDynamic programming alternative for reference\npublic class LongestPalindromeDynamicProgramming {\n public String longestPalindrome(String s) {\n int strLength = s.length();\n int maxLength = 0;\n int maxStartIndex = 0;\n\n boolean[][] dp = new boolean[strLength][strLength];\n for (int i = strLength - 1; i >= 0; i--) {\n for (int j = i; j < strLength; j++) {\n dp[i][j] = s.charAt(i) == s.charAt(j) && (j - i < 2 || dp[i+1][j-1]);\n int existingLength = j - i + 1;\n if (dp[i][j] && existingLength > maxLength) {\n maxStartIndex = i;\n maxLength = j - i + 1;\n }\n }\n }\n return s.substring(maxStartIndex, maxStartIndex + maxLength);\n }\n}\n\n","id":"https://gel.github.io/interviews/3-coding-problems/2-problem-solving/","title":"Problem Solving"},"https://gel.github.io/interviews/4-system-design/":{"body":"Mastering System Design for Technical Interviews\nSystem design questions are pivotal in technical interviews, directly influencing how interviewers evaluate and rank candidates for senior engineering roles. However, many engineers underestimate the difficulty of system design and rely on insufficient resources that often provide misleading guidance. \nIn my experience, both free and paid system design trainings frequently miss the mark on adequately preparing candidates. Therefore, in this blog, I will walk through solutions to common system design interview questions, sharing the approach and techniques I've found most effective.\nRecommended System Design Process🔗\n\n\nListen closely to thoroughly understand all requirements before proposing any solutions. Practice active listening and asking clarifying questions at appropriate times. Document requirements clearly on a whiteboard or paper. \n\n\nOnly after requirements are fully defined, begin architecting the high-level design. Explain considerations like expected users, requests per second, latency limits, storage volumes, etc.\n\n\nMap out key architectural components and core data flows end-to-end (e.g user clicks button, client-side call, load balancer, authentication, backend service A, etc.)\n\n\nDeconstruct each component further specifying detailed APIs, data schemas, databases, caching, etc. as needed.\n\n\nKey Strategies🔗\n\n\nAvoid jumping to solutions prematurely before nailing down requirements. Build trust by demonstrating care and precision. \n\n\nKnow when to ask probing questions to unblock progress versus nodding along.\n\n\nBalance high-level architecture with lower-level implementation details at appropriate junctures.\n\n\nWith a structured, requirements-focused approach and strong communication skills, system design interviews can be mastered through practice over time. I look forward to sharing specific techniques for top technology companies' system design questions in future posts.\n","id":"https://gel.github.io/interviews/4-system-design/","title":"Mastering System Design"}},"docInfo":{"https://gel.github.io/":{"body":23,"title":4},"https://gel.github.io/blog/":{"body":1,"title":0},"https://gel.github.io/blog/dalle-faceswap/":{"body":364,"title":4},"https://gel.github.io/blog/wsl-environment/":{"body":74,"title":3},"https://gel.github.io/books/":{"body":0,"title":0},"https://gel.github.io/books/programming/":{"body":0,"title":0},"https://gel.github.io/books/programming/pragmatic-programmer/":{"body":6,"title":8},"https://gel.github.io/books/programming/the-rust-programming-language/":{"body":16,"title":9},"https://gel.github.io/books/reading/":{"body":0,"title":0},"https://gel.github.io/books/reading/anger-management-for-parents/":{"body":0,"title":11},"https://gel.github.io/books/reading/chasing-daylight/":{"body":0,"title":8},"https://gel.github.io/books/reading/how-to-talk-so-little-kids-will-listen/":{"body":0,"title":13},"https://gel.github.io/books/reading/morning-rituals/":{"body":0,"title":4},"https://gel.github.io/books/reading/never-split-the-difference/":{"body":0,"title":8},"https://gel.github.io/books/reading/oh-crap-potty-training/":{"body":0,"title":13},"https://gel.github.io/books/reading/primal-endurance/":{"body":0,"title":13},"https://gel.github.io/books/reading/rich-dad-before-you-quit/":{"body":135,"title":8},"https://gel.github.io/books/reading/rich-dad-poor-dad/":{"body":0,"title":7},"https://gel.github.io/books/reading/sapiens/":{"body":0,"title":7},"https://gel.github.io/books/reading/secrets-of-millionaire-mind/":{"body":0,"title":10},"https://gel.github.io/books/reading/smarter-faster-better/":{"body":0,"title":10},"https://gel.github.io/books/reading/surrounded-by-idiots/":{"body":0,"title":4},"https://gel.github.io/books/reading/the-7-habits/":{"body":0,"title":13},"https://gel.github.io/books/reading/the-cancer-code/":{"body":0,"title":9},"https://gel.github.io/books/reading/the-complete-guide-to-fasting/":{"body":0,"title":13},"https://gel.github.io/books/reading/the-innovation-stack/":{"body":0,"title":11},"https://gel.github.io/books/reading/the-obesity-code/":{"body":0,"title":8},"https://gel.github.io/books/reading/the-psychology-of-money/":{"body":0,"title":4},"https://gel.github.io/books/reading/the-subtle-art/":{"body":0,"title":11},"https://gel.github.io/books/reading/unlocking-the-keto-code/":{"body":0,"title":15},"https://gel.github.io/generative-ai/1-intro/":{"body":181,"title":3},"https://gel.github.io/generative-ai/1-intro/1-llm-survey/":{"body":156,"title":2},"https://gel.github.io/generative-ai/2-llm-research/":{"body":291,"title":2},"https://gel.github.io/generative-ai/2-llm-research/1-llm-pretraining-finetuning/":{"body":908,"title":4},"https://gel.github.io/generative-ai/2-llm-research/2-llm-agents/":{"body":67,"title":2},"https://gel.github.io/generative-ai/2-llm-research/3-llm-optimization/":{"body":582,"title":2},"https://gel.github.io/generative-ai/2-llm-research/4-llm-prompting/":{"body":580,"title":2},"https://gel.github.io/generative-ai/2-llm-research/5-llm-benchmarks/":{"body":48,"title":3},"https://gel.github.io/generative-ai/2-llm-research/6-llm-multimodal/":{"body":300,"title":3},"https://gel.github.io/generative-ai/2-llm-research/7-llm-models/":{"body":98,"title":2},"https://gel.github.io/generative-ai/3-llm-implementation/":{"body":365,"title":2},"https://gel.github.io/generative-ai/3-llm-implementation/1-datasets/":{"body":80,"title":1},"https://gel.github.io/generative-ai/3-llm-implementation/2-llms/":{"body":114,"title":3},"https://gel.github.io/interviews/1-intro/":{"body":67,"title":2},"https://gel.github.io/interviews/1-intro/1-making-a-career-move/":{"body":113,"title":3},"https://gel.github.io/interviews/1-intro/2-finding-the-best-job/":{"body":144,"title":3},"https://gel.github.io/interviews/1-intro/3-passing-the-interview/":{"body":165,"title":3},"https://gel.github.io/interviews/2-data-structres-and-algorithms/":{"body":150,"title":3},"https://gel.github.io/interviews/2-data-structres-and-algorithms/1-data-structures/":{"body":197,"title":2},"https://gel.github.io/interviews/2-data-structres-and-algorithms/2-algorithms/":{"body":547,"title":1},"https://gel.github.io/interviews/3-coding-problems/":{"body":137,"title":4},"https://gel.github.io/interviews/3-coding-problems/1-logical-and-maintainable/":{"body":920,"title":2},"https://gel.github.io/interviews/3-coding-problems/2-problem-solving/":{"body":570,"title":2},"https://gel.github.io/interviews/4-system-design/":{"body":204,"title":3}},"length":54},"lang":"English"}; \ No newline at end of file diff --git a/sitemap.xml b/sitemap.xml new file mode 100644 index 0000000..b38f0f5 --- /dev/null +++ b/sitemap.xml @@ -0,0 +1,310 @@ + +
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