vLLM: The “Magic” Accelerator for Large Models

Imagine you are the owner of an extremely busy restaurant. Your head chef (the currently hottest “Large Language Model”, LLM for short) possesses amazing culinary skills and can conjure up delicious dishes (generate answers) based on various customer requests (text inputs). However, this restaurant faces a big problem: customers are ordering faster and faster, while the chef, despite their exquisite skills, can only cook one dish at a time. The kitchen efficiency is low, causing customers to wait for a very long time, and the waste of ingredients (computing resources) and kitchen space (memory) is very serious.

With the rapid development of Artificial Intelligence, Large Language Models (LLMs) have become an indispensable part of our lives. They can write poetry, code, translate, and even chat, just like that omnipotent chef. However, the inference process of these huge models (the “cooking” process) is a huge challenge. They have extremely high demands on computing resources, are slow, and expensive. To solve these problems, a star-level “kitchen management system” came into being, which is the protagonist we are introducing today—vLLM.

What is vLLM?

vLLM stands for “Virtual Large Language Model”. It is not a specific language model, but an open-source high-performance inference engine designed specifically to accelerate the inference of large language models. You can understand it as an extremely intelligent kitchen management system. Its task is to ensure that the chef (LLM) works in the fastest and most efficient way when processing massive orders, maximizing the use of every corner of the kitchen (GPU), while minimizing the waste of ingredients (memory).

The Dilemma of Large Model Inference: Why Do We Need vLLM?

Why is the inference of large models so difficult? Let’s continue to use the restaurant analogy:

1. Huge Calculation Volume, Every Dish is Super Complex: Every answer from an LLM requires massive calculations, just like every dish made by the chef is a meticulously crafted Michelin meal, consuming time and effort.
2. Heavy “Memory” Burden (KV Cache): When the chef cooks each dish, to ensure consistent taste, they will pile all the complex ingredients and cooking tips used before (the “Attention Key” and “Attention Value” in the large model, referred to as KV Cache) on the workbench. These “memories” will accumulate as the complexity of the dish increases, occupying a large amount of valuable kitchen workbench space (GPU memory). In the traditional way, even if there are many dishes, the memory area for each dish is fixed, resulting in a lot of idle but occupied space, causing serious memory fragmentation and waste.
3. Low Efficiency, Long Wait Time for Customers (Low Throughput): Traditional restaurants usually adopt the “finish one dish before doing the next” method. If dozens or hundreds of customers order at the same time, the chef must complete them sequentially. This causes many customers to wait for a long time, meaning the model’s “throughput” is very low.

These dilemmas collectively lead to speed bottlenecks, high latency, and high operating costs for large model inference.

The Magic of vLLM: Two Core Technologies

The greatness of vLLM lies in its introduction of two revolutionary technologies, which fundamentally solve the above problems: PagedAttention and Continuous Batching. It is precisely with these two innovations that vLLM can increase LLM throughput by up to 24 times, while significantly reducing latency and hardware costs.

1. PagedAttention: The Intelligent “Memory” Management Master

To solve the heavy “memory” burden problem, vLLM proposed the PagedAttention mechanism. This is like equipping the chef with an extremely intelligent ingredient management system:

• Waste in Traditional Methods: Previously, every time the chef started a new dish, a fixed-size area of the workbench would be designated to place the ingredients and tips for this dish. But the actual complexity of dishes and the amount of ingredients required vary. Sometimes the dish is very simple, and most of this area is empty; sometimes it’s a pile, but regardless of whether it is used or not, this area is “reserved” and cannot be used by other dishes. This led to a huge waste of kitchen space.

• Innovation of PagedAttention: The inspiration for the PagedAttention mechanism comes from the virtual memory management technology in operating systems. It no longer reserves a fixed-size space for each dish but divides the “memory” (KV Cache) of each dish into many small “memory blocks” (Pages). When the chef needs a certain “memory block”, the system will dynamically allocate a physical space from a public “memory pool” to it. These physical spaces do not need to be continuous, just like books in a library may be placed separately, but the catalog (Block Table) will accurately record the location of each page.

  Even better, if multiple dishes have common, repeated “memories” (for example, all customers ordered the same appetizer, or the initial steps of making a certain dish are the same), PagedAttention allows them to share these “memory blocks”. Only when they start to produce different “memories” (the dish produces unique changes) will the system copy and allocate independent memory blocks for the new part (Copy-on-Write mechanism).

  Effect: By this means, PagedAttention greatly reduces the memory waste of KV Cache. The GPU memory utilization rate approaches 100%. The memory waste of traditional LLM inference engines may be higher than 96%, while vLLM can reduce it to less than 4%. This means the kitchen workbench is no longer piled with useless ingredients, and the chef has more space to process more orders simultaneously.

2. Continuous Batching: Pipeline Order Processing Expert

To solve the problem of low efficiency, vLLM introduced Continuous Batching technology. This is like the restaurant introducing an intelligent, pipeline-style order processing system:

• Shortcomings of Traditional Batching: The previous batching mode was “static batching”, like the restaurant accumulating a batch of orders (say, 10 pizzas), and the chef making these 10 pizzas together. Only when all pizzas are baked and served will the next batch of orders be processed. If one pizza requires extra toppings and takes a long time, all subsequent customers have to wait.

• Innovation of Continuous Batching: Continuous Batching is like continuously flowing order processing. The system will dynamically combine the ongoing (unfinished) and new (just ordered) customer orders and send them into the chef’s “production pipeline” at the fastest speed. Once an order is completed or new GPU resources become free, the system will immediately supplement new or waiting orders, instead of waiting for a batch to be fully completed. It will continuously send available requests into the LLM in batches, and as long as the GPU is free, it will never let it stop and wait.

  Effect: Continuous Batching greatly improves the utilization of GPU, allowing the large model to process requests uninterruptedly, just like an intelligent traffic command system, keeping the road clear at all times. This enables vLLM to achieve throughput several times or even tens of times higher than traditional solutions, while significantly reducing the response latency of user requests.

Changes Brought by vLLM

The emergence of vLLM has brought revolutionary impacts to the large model field:

• Performance Leap: According to some benchmarks, vLLM’s throughput is 24 times higher than Hugging Face Transformers (a commonly used LLM open-source library). Its latest version has increased throughput by 2.7 times and reduced latency by 5 times. This means that with the same time and resources, more requests can be processed, and the response speed is faster.
• Significant Cost Reduction: More efficient resource utilization means fewer GPUs are needed to process LLMs. Cases show that after using vLLM, the number of GPUs required to process the same traffic was reduced by 50%. For enterprises and developers, this is undoubtedly a huge benefit.
• Broader Compatibility and Openness: vLLM is not only compatible with NVIDIA GPUs but is also actively expanding support for various hardware such as AMD GPUs, Intel GPUs, AWS Neuron, Google TPUs, etc. It supports multiple popular model architectures including LLaMA and GPT-2, and can be easily integrated with frameworks like Langchain. As an open-source project, vLLM allows community innovation and development.
• Simple and Easy to Use: vLLM provides a server interface compatible with OpenAI API, allowing developers to seamlessly integrate it into existing applications and deploy without modifying model codes.

Latest Progress and Outlook

The vLLM project continues to be active and develops rapidly. In January 2025, vLLM released the V1 Alpha version, which is a major architectural upgrade that brought a 1.7x speed increase and added support for multi-modality. In addition, vLLM is constantly optimizing its quantization support (such as bitsandbytes, QQQ, FP8 KV cache) and supporting a wider range of model architectures.

It can be said that vLLM is becoming the industry standard and driving force in the field of large model inference.

Summary

vLLM is like the unsung hero in the large model restaurant—an efficient and intelligent kitchen management system. It cleverly manages “memory space” through PagedAttention to eliminate waste; and processes orders in a pipeline style through Continuous Batching to maximize the value of every computing resource. It is precisely these two “magics” that allow large language models to serve us faster, cheaper, and more efficiently, bringing advanced AI technology to a wider range of application scenarios. In the future, with technologies like vLLM, we can expect large models to unleash greater potential in various fields and truly enter thousands of households.

Normalizing Flow

In the vast world of Artificial Intelligence (AI), we often face a core challenge: how to understand and generate complex and varied data. Whether it is images, sounds, text, or scientific experiment data, they may seem chaotic, but there are unique laws hidden behind them. At this time, a technology called “Normalizing Flow” emerged. It acts like a magician, skillfully unraveling the “mysteries” of these data.

What are “Normalizing Flows”? — A Creative Transformation

Imagine you have a piece of ordinary plasticine in your hand, and its shape might be a simple sphere. Now, you want to use this plasticine to sculpt a complex and exquisite sculpture, such as a spaceship. What would you do? You would go through a series of operations such as kneading, rubbing, stretching, and pressing to change the shape of the plasticine step by step, and finally get the complex shape you want. More importantly, if your technique is exquisite enough, you can even reverse these steps and turn the spaceship back into the initial simple sphere.

“Normalizing Flow” does something similar in the field of AI. It is a special generative model whose core idea can be summarized as: skillfully “shaping” a simple, easy-to-understand probability distribution (such as the bell curve we are most familiar with, i.e., Gaussian distribution) into a complex, realistic data distribution through a series of reversible transformations. Conversely, it can also “reverse engineer” complex data from the real world into a simple distribution.

• “Flow”: Refers to this series of continuous, reversible mathematical transformation processes. Just like water flowing through pipes of different shapes, although the form is always changing, the total amount of water (in probability distribution, corresponding to the total probability, which is 1) always remains unchanged. Each transformation is a stage of the “flow”, progressing layer by layer until the final form.
• “Normalizing”: Means that this process can transform any complex data distribution “back” (or convert) to a standard, simple distribution that is easy for us to analyze, usually a standard normal distribution.

How the “Magic” Happens? — Reversible Layered Transformations

The “magic” of “Normalizing Flow” lies in the “deformation” method it uses. These deformations are carefully designed:

1. Starting from Simple: It always starts from a simple probability distribution that we know well and is mathematically easy to handle (such as the normal distribution). This is our “original plasticine ball”.
2. Reversible Transformation Chain: It completes this “shaping” through a series of continuous, reversible, and mathematically differentiable functions. Each function is like a unique shaping tool that makes a local adjustment to the data. Since these operations are all reversible, we can not only go from simple to complex (generate data) but also from complex to simple (analyze data).
3. Precise Calculation of “Volume Change”: In each transformation, the “density” (i.e., probability) of the data changes. To precisely track this change, we need a mathematical tool called the “Jacobian determinant” to calculate the degree of expansion or contraction of the “volume” of the data space during the transformation process. The ingenuity of Normalizing Flow lies in the fact that it designs these transformations so that this complex Jacobian determinant becomes very easy to calculate.
4. Empowered by Neural Networks: These complex transformation functions are usually learned and implemented by neural networks in deep learning. The powerful fitting ability of neural networks allows “Normalizing Flow” to learn extremely complex data distributions.

What are the Strengths of “Normalizing Flows”? — Achieving Both Effect and Precision

Compared with other generative models in the field of AI, Normalizing Flow has unique advantages:

• Exact Probability Calculation: This is one of the most significant features of Normalizing Flow. It can exactly calculate the probability of any generated data point. This is crucial for many applications, such as anomaly detection (low probability data points may be outliers) or measuring generation quality.
• High-Quality Sample Generation: By learning complex real data distributions, Normalizing Flow can generate very realistic and diverse data samples, whether they are images, audio, or other types of data.
• Stable Training Process: Unlike some generative models (such as Generative Adversarial Networks, GANs) that often face problems such as unstable training and mode collapse, the training process of Normalizing Flow is usually more stable and easier to verify convergence to an ideal state.
• Review: Since its design requires all transformations to be reversible, this means that we can not only generate complex data from a simple distribution but also map complex data back to a simple distribution, thereby better understanding the data itself.

Application Scenarios of “Normalizing Flows” — From Images to Scientific Exploration

With its unique advantages, Normalizing Flow has shown great potential in multiple fields:

• High-Fidelity Content Generation: Capable of generating high-quality realistic images, videos, and audio. For example, recent research results “TarFlow” have demonstrated that the image generation quality of Normalizing Flow can rival the currently most popular Diffusion Models, and has achieved new SOTA results in likelihood estimation. (This is a prospective mention of future research results)
• Anomaly Detection and Outlier Identification: Due to its ability to accurately calculate the probability of data points, Normalizing Flow can effectively identify abnormal data with extremely low probability in normal data distribution, which is widely used in industrial inspection, network security, and other fields.
• Scientific Simulation and Discovery: In frontier scientific fields such as physics, chemistry, and cosmology, Normalizing Flow is used to model complex particle distributions, predict molecular structures, and analyze cosmological data. For example, it is used for conformational sampling and free energy calculation in molecular dynamics simulations, and can even provide powerful tools in cosmological data analysis.
• Data Compression and Denoising: Efficient data compression can be achieved by mapping complex data to low-dimensional simple distributions; conversely, it can also be used for data denoising.
• Tabular Data Generation and Privacy Protection: Under the premise of protecting data privacy, utilizing Normalizing Flow to generate realistic synthetic tabular data can be used for scenarios such as data augmentation and model testing.

Latest Developments and Outlook — Potential on the Horizon

In recent years, researchers have continuously explored and improved Normalizing Flow. New methods such as “Flow Matching” appeared in 2023, which trains Continuous Normalizing Flows (CNFs) in a simulation-free manner. It not only achieved the best performance at the time in benchmarks like ImageNet but also showed great potential in training efficiency and sampling speed, even providing a more stable and robust alternative for training diffusion models.

Although once overshadowed by GANs and VAEs in the generative field, Normalizing Flow is regaining attention due to its theoretical elegance and interpretability, as well as its constantly improving generation capabilities. Models like TarFlow prove that Normalizing Flow has huge potential in large-scale generation tasks.

Conclusion: Understanding the Dance of Data

“Normalizing Flow” is not a simple generation tool; it is more like a window that allows us to glimpse the invisible and complex probability distribution behind the data. By visualizing this “invisible dance” and controlling it precisely, AI scientists can understand data more deeply, create data, and ultimately unravel more “mysteries” of the real world. With the continuously advancing technology, we can expect Normalizing Flow to play an increasingly critical role in future AI development, becoming an indispensable tool for interpreting and creating the digital world.

do-calculus

Unveiling the Magic of AI Causal Inference: do-calculus

In the vast starry sky of Artificial Intelligence (AI), we often marvel at its ability to predict the future. Whether recommending products, diagnosing diseases, or identifying images, AI can perform exceptionally well. However, these capabilities are mostly based on the discovery of “correlation”—that is, the trend of co-variation between things. But we all know that “correlation does not imply causation”. For example, while ice cream sales rise in summer, drowning accidents also increase, but we cannot say that eating ice cream causes drowning. This is because there is a common cause behind both: hot weather.

This “correlation trap” is particularly dangerous in the AI field. If AI makes decisions solely based on correlation, it may lead to incorrect or even harmful interventions. For example, discovering a correlation between a certain drug and disease recovery, but it might actually be because patients taking the drug had milder conditions to begin with. How can we enable AI to understand “why” like humans do, and answer the question “what would happen if I do this”? This is the core of Causal Inference, and do-calculus is one of the key tools to achieve this goal.

“Observing” and “Intervening”: Breaking the Maze of Correlation

The core idea of do-calculus lies in strictly distinguishing between the two behaviors of “observing” and “intervening”. We can understand this with a simple life scenario:

1. Observe: Imagine you are a detective, just passively recording facts. You observe that people who drink coffee in the morning usually look more awake. On the surface, there seems to be a correlation between drinking coffee and wakefulness. However, you cannot determine whether coffee causes wakefulness, or if awake people are more inclined to choose coffee, or if other factors (such as early rising habits, stress, etc.) affect both coffee drinking and wakefulness levels simultaneously. This is like seeing “when it rains, the ground is wet” from data, which is an observed conditional probability $P(\text{Wet Ground} | \text{Rain})$.

2. Intervene: Now you are no longer a detective, but a scientist who can actively conduct experiments. You find a group of people and randomly divide them into two groups: one group is forced to drink coffee, and the other is not, and then observe their wakefulness levels. Through this “mandatory” means, you eliminate other interfering factors, thus being able to judge more accurately whether coffee really causes wakefulness. This is the meaning represented by the “do-operator” in do-calculus, denoted as $P(\text{Wet Ground} | \text{do}(\text{Watering}))$, meaning “If we force water to appear on the ground, will the ground be wet?” The do-operator is like a “key” that opens the door from correlation to causation.

In short, the goal of do-calculus is to identify the effect of this “intervention” from observational data using mathematical methods.

Confounding Factors: The “Fog” of Causal Inference

Why is observed correlation alone insufficient to judge causation? Besides the “ice cream and drowning” example mentioned above, another classic example is: smoking and yellow fingers. Yellow fingers and lung cancer in a person might both be related to smoking. If you only observe the correlation between yellow fingers and lung cancer without considering smoking as a common cause, you might reach a wrong causal conclusion. This common cause is called a “confounding variable” in causal inference.

Proposed by AI pioneer Judea Pearl in 1995, do-calculus was designed to address the challenge of such confounding factors. It provides a formal framework that combines Causal Graphs (a graph representing causal relationships between variables) and a set of mathematical rules to help us isolate true causal effects from observational data.

The “Magic Formula” of do-calculus: Three Golden Rules

do-calculus is not a complex set of calculation methods, but a deduction system composed of three core rules. These three rules give us a kind of “magic” that allows us to deduce the true effect of an intervention by adjusting and transforming probability expressions without actual intervention (such as when randomized controlled trials cannot be performed).

The intuitive meanings of these three rules are:

1. Ignorance of Irrelevant Observations (Addition/Deletion of Observation): In certain causal structures, once we have intervened on a variable, observing certain other variables provides no additional information about the causal effect of interest, so these observation terms can be removed from the probability expression. This is like in the kitchen, if you have already added salt to the pot, observing whether the salt shaker is full or empty has nothing to do with the taste of the dish.

2. Action/Observation Exchange: In other specific causal structures, we can equivalently replace the “intervention” action on a variable with the “observation” action of that variable without changing the derived causal effect, and vice versa. This is like sometimes “deliberately arranging for someone to attend a meeting” and “observing that someone happened to attend a meeting” can be interchangeable under specific circumstances, with consistent impact on the judgment of the final meeting result.

3. Ignorance of Irrelevant Interventions (Addition/Deletion of Action): When a variable has no causal effect on the outcome variable we are interested in, even if we “intervene” on this variable, its effect can be ignored. For example, if you intervene to turn on a light bulb, but the light bulb has no causal link to the sweetness of your coffee, this intervention can be ignored.

By flexibly applying these three rules, do-calculus allows complex causal queries containing “do-operators” (such as “how will Y change if we force X?”) to be transformed into probability expressions containing only ordinary observational data. In this way, even if we haven’t done randomized controlled trials, we can calculate causal effects like “what would happen to B if I did A” from existing historical data.

Value of do-calculus in the AI Era

In today’s data-driven AI era, the importance of do-calculus is increasing day by day.

• Realizing Causal AI: Traditional machine learning models excel at pattern recognition, but do-calculus allows AI to go beyond appearances and understand the causal mechanisms behind data. This enables AI not only to predict “what will happen” but also to understand “why it happens” and “what should I do to make it happen or not happen”.
• Optimizing Business Decisions: In the business field, do-calculus can help companies assess the true causal impact of different marketing strategies and product pricing on sales and user retention, rather than just correlations. For example, Microsoft has used causal inference to optimize advertising effectiveness.
• Promoting Scientific Research and Policy Making: In fields like medicine and social sciences, inferring causal relationships from large amounts of observational data through do-calculus allows evaluation of drug efficacy and public policy effects, which is particularly critical in scenarios with limited resources where randomized controlled trials are difficult to implement.
• Enhancing AI Explainability and Fairness: Understanding the causal chain behind AI decisions helps improve model explainability and transparency, identify and eliminate potential biases, and ensure fairness in AI decisions.
• Application of Emerging Tool Libraries: To facilitate developers and researchers in applying do-calculus, open-source tool libraries like CausalNex and DoWhy have emerged. They encapsulate complex causal inference theories into easy-to-call interfaces, promoting the practical implementation of Causal AI.

Conclusion

The leap from “correlation” to “causation” is a key step for artificial intelligence to move from “intelligence” to “wisdom”. As the cornerstone of causal inference, do-calculus provides AI with a sharp weapon to gain insight into the deep mechanisms of the world. It allows us not only to be satisfied with prediction but also to understand, explain, and intervene, thereby making wiser and more responsible decisions. With the continuous development of do-calculus theory and application tools, future AI will no longer be just a powerful calculator, but a wise partner capable of truly understanding the world and mastering causal relationships.

Zephyr

In the vast starry sky of Artificial Intelligence (AI), various innovative technologies shine like stars. Today, we are going to introduce a high-profile concept — “Zephyr”. However, in the field of AI, “Zephyr” has two main meanings. To avoid confusion, we mainly focus on a series of Large Language Models (LLMs) developed and open-sourced by Hugging Face, which are the focus of broader discussion in the AI field. Another “Zephyr AI” is an AI company focusing on precision medicine and data analysis.

Zephyr: The “Smart Little Assistant” in the AI World

Imagine you have a very smart and capable personal assistant. He is not only knowledgeable but also good at communication, always able to accurately understand your intentions and give appropriate answers. In the world of artificial intelligence, the Zephyr large language model developed by Hugging Face plays such a role.

1. Its “Birth”: From “Good Student” to “Top Student”

The Zephyr model did not appear out of thin air. It was “finely crafted” on an already very excellent “base model”. This base model is Mistral 7B. You can imagine Mistral 7B as a talented and widely read “good student” who has mastered a lot of knowledge but may not be sophisticated enough in actual communication and specific instruction execution.

The birth of Zephyr is like this “good student” accepting a special “elite training program”. This program mainly includes two “training methods”:

• “Guidance from Famous Teachers” (Distilled Supervised Fine-Tuning, dSFT):
  This is like letting this “good student” learn from an experienced “famous teacher”. The famous teacher will give him a large number of “demonstration assignments” (high-quality instruction-answer pairs), telling him how to respond accurately and effectively to various problems. Through imitating and learning from these “examples”, the student (Mistral 7B) can quickly improve the ability to understand instructions and generate appropriate answers.

• “Moral Education and Code of Conduct” (Direct Preference Optimization, DPO & Constitutional AI):
  Being smart alone is not enough; an excellent assistant also needs to have good “morals”. DPO and Constitutional AI are like a series of “codes of conduct” and “feedback mechanisms”. After the student completes the task, the teacher (AI feedback or human preference data) will tell him which answers are preferred by everyone, safer, and more harmless. Through constant “reflection” and “adjustment”, Zephyr learns how to become a “Helpful, Harmless, Honest” AI, which is the goal pursued by the Hugging Face H4 team. This allows it not only to output useful information but also to avoid producing inappropriate or harmful content.

2. The Secret of “Small but Powerful”: Great Wisdom in a Small Body

In the world of AI models, the size of a model is usually measured by “parameters”. The more parameters, usually the more powerful the model. Many well-known large language models (LLMs), such as GPT-3, possess hundreds of billions of parameters. While the Zephyr model, especially Zephyr 7B, has only 7 billion parameters.

This is like a “Kung Fu master” who is not burly. Although his “size” is not as big as those “big guys”, due to proper training and exquisite moves, he can show strength comparable to or even surpassing those “big guys” in many actual “contests” (such as multi-turn dialogue, instruction following tasks, etc.). Although his “brain” is not the largest, the efficiency of information processing is extremely high, and the “comprehension” of user intent is also very strong. This allows it to run more efficiently and consume fewer computing resources while maintaining high performance.

3. Openness and Freedom: A “Smart Butler” Available to Everyone

One of the biggest highlights of the Zephyr model is its “open source” nature. This is like a public, free “smart butler” software blueprint and user manual. Any developer or company can download this “blueprint” (model code and weights) for free, modify and optimize it according to their own needs, and then deploy it on their own devices or servers.

This means:

• Cost-effective: No need to pay expensive API call fees, which can reduce the development and operation costs of AI applications.
• Highly Customizable: Developers can further fine-tune it according to the needs of specific industries or scenarios, making it speak specific “jargon” and solve professional problems.
• Stronger Privacy: Since it can be deployed locally, sensitive data does not need to be uploaded to third-party servers, helping to protect user privacy.

4. Where it fits: AI Assistants Everywhere

With its excellent conversational capabilities and instruction-following abilities, the Zephyr model has shown great potential in various application scenarios:

• Intelligent Customer Service and Virtual Assistants: Can build more natural and fluid customer service chatbots to respond quickly to user inquiries and provide help.
• Content Creation Assistance: Assist in writing articles, generating creative text, and improving content production efficiency.
• Educational Tools: As an intelligent tutor, provide personalized learning guidance and Q&A for students.
• Localized Applications: Since the model is small and open-source, it can run on personal computers or edge devices to develop “offline available” AI applications.

Summary and Outlook

The Zephyr model is a model of “small body, big energy” in the AI field. It proves that through clever training methods, even models with relatively small parameters can achieve amazing results in practical applications, even surpassing some larger models. Its open-source nature provides huge convenience for developers, accelerating the popularization and innovation of AI technology. With the continuous advancement of technology, we can expect efficient and customizable AI models like Zephyr to become increasingly important “smart little assistants” in our daily lives and work.

Wasserstein Distance

In the field of AI, “distance” and “similarity” are key concepts for understanding data and model behavior. Among the many methods for measuring differences between distributions, Wasserstein Distance (also known as Earth Mover’s Distance, EMD) stands out, providing us with a more intuitive and stable metric. It plays an important role in artificial intelligence, especially in fields like Generative Adversarial Networks (GAN).

1. What is Wasserstein Distance? — Starting with “Moving Earth”

Imagine you have two piles of sand: one is the data you actually observed (real data distribution), and the other is the data generated by your AI model (generated data distribution). The shape, location, and size of these two piles of sand may vary. Now, your task is to reshape the first pile of sand (model-generated sand) into the second pile of sand (real sand). You need to hire a bulldozer to do this job.

Wasserstein Distance measures the minimum “work” required to complete this “earth moving” task. The “work” here is usually defined as: how much sand you moved, multiplied by the average distance this sand was moved. If the two piles of sand are exactly the same, then no sand needs to be moved, and the work is 0. If they are completely unrelated or have very different shapes, then more “work” needs to be done.

This vivid metaphor is the origin of the name Earth Mover’s Distance, which is a concept of Optimal Transport first proposed by Gaspard Monge in 1781. It was not until later that research by Leonid Vaseršteǐn and others applied it to the comparison of probability distributions and finally named it after him.

2. Why is Wasserstein Distance So Special? — Differences from Other “Distances”

In computer science and machine learning, we have other methods to measure the difference between two probability distributions, the most common of which are KL Divergence (Kullback-Leibler Divergence) and JS Divergence (Jensen-Shannon Divergence). So, compared to them, what are the advantages of Wasserstein Distance?

1. Insensitive to Overlap, Providing Meaningful Gradient Information:

   • Imagine two piles of sand. If there is absolutely no overlap between them (for example, one pile is entirely on the left and the other is entirely on the right), then KL divergence or JS divergence might give an infinite or constant value. This makes it impossible for us to judge which pile of sand is “closer” to the other, and we don’t know how to adjust the model to “move” the sand to reduce the distance. In machine learning algorithms, this can lead to vanishing gradients, preventing the model from learning effectively.
   • Wasserstein Distance is different. Even if the two piles of sand have absolutely no overlap, it can give a meaningful numerical value based on the distance the sand needs to be moved. For example, the work required for two piles of sand 10 meters apart is obviously smaller than that for piles 100 meters apart. This value provides a smooth gradient information that can be effectively optimized, allowing the model to clearly know “which direction to work towards” to make the generated sand more like the real sand.
   • You can understand it as: KL/JS divergence might only care “whether” the two piles of sand are different, but Wasserstein Distance can better measure “where” they are different and “to what extent” they are different.

2. Considers “Path” and “Cost”:

   • KL divergence and JS divergence focus more on the probability difference at each point of the two distributions.
   • Wasserstein Distance focuses on how to optimally convert the “mass” (e.g., sand) in one distribution to another. It measures not only the total amount of difference but also the “cost” or “work” required to eliminate this difference, which is related to the “distance” moved and the “mass”.

3. Geometric Intuition:

   • Wasserstein Distance aligns highly with physical intuition, i.e., the metaphor of “earth moving”. This makes its intrinsic meaning easier to understand even for non-professionals.

3. Applications of Wasserstein Distance in AI

The attention Wasserstein Distance has received in the AI field is largely due to its application in Generative Adversarial Networks (GANs).

1. Stability Improvement of Generative Adversarial Networks (GANs):
Traditional GANs often encounter problems like mode collapse and unstable training. This is partly because their loss function (usually based on JS divergence) suffers from vanishing gradients when the overlap between the two distributions is very low.
Wasserstein GAN (WGAN), proposed in 2017, was designed to solve this problem. WGAN replaces the original loss function with Wasserstein Distance, enabling the Discriminator (Critic) to provide more meaningful gradient signals to the Generator, even when the overlap between the real data distribution and the generated data distribution is small. This makes WGAN training more stable, generating samples of higher quality and better diversity. It can better measure the distance (or difference) between the generated image distribution and the real image distribution.

2. Image Processing and Computer Vision:
Wasserstein Distance is used in image processing to measure the difference between two images. Compared to traditional pixel-level comparisons, it can better account for image structural information and spatial relationships. For example, in image retrieval, it can be used to find the image most similar to a query image, even if the image has deformation or noise. In addition, it also plays a role in tasks such as image generation and style transfer.

3. Data Drift Detection:
After a machine learning model is deployed, the distribution of input data may change over time, which is called “Data Drift”, potentially leading to model performance degradation. Wasserstein Distance can be used to effectively measure the difference between the new data distribution and the training data distribution, thereby detecting data drift. Compared to KL divergence, Wasserstein Distance is more robust when detecting structural changes in complex data distributions or large datasets.

4. Other Applications:
In addition to the above fields, Wasserstein Distance has also been applied in natural language processing, computational biology (such as comparing persistent diagrams of cell count datasets), and geophysical inverse problems. It has even been used in integrated information theory to calculate the differences between concepts and conceptual structures.

4. Looking to the Future

Although Wasserstein Distance has the disadvantage of relatively high computational cost (especially on high-dimensional data), its unique advantages in machine learning, especially in generative models and data analysis, make it an indispensable tool. With the advancement of computing resources and the development of new algorithms, it is believed that the application of Wasserstein Distance will become more extensive and in-depth, bringing more innovation and breakthroughs to the AI field.

Wasserstein Distance Demo

Warmup Steps: The Rehearsal Before the Sprint for AI Models

In the field of AI, there is a seemingly simple but crucial concept called “Warmup Steps”, often translated as “预热步数” or “热身阶段” in Chinese. It plays a stabilizing and accelerating role in the training of deep learning models, especially for large and complex models, and its importance cannot be underestimated.

What are “Warmup Steps” in AI?

Imagine you are preparing for a running race. You would not sprint at full speed immediately after the starting gun fires, right? Doing so would likely lead to pulled muscles or even exhaustion early in the race. Smart runners will first perform a series of stretches, jogging, and other “warm-up” activities to let their bodies gradually adapt to the intensity of the exercise, then gradually accelerate, and finally reach their peak competitive state.

In the training of AI models, “Warmup Steps” play this role of “warming up”. In the early stages of deep learning model training, we usually set a key parameter called “Learning Rate”. The learning rate determines the size of the step the model takes during each learning (parameter update). If the learning rate is too large, the model is like an impatient runner who takes “steps too big” from the start, making it easy to “fall” (leading to unstable training, or even failure to converge, i.e., model collapse, technically called “gradient explosion” or loss becoming NaN), let alone finding the optimal solution.

The strategy of “Warmup Steps” is: for a short period of time at the very beginning of model training (i.e., a series of “steps” or iterations), instead of directly using the preset “normal” learning rate, start with a very small (even close to zero) learning rate, and then generally increase it linearly or non-linearly until it reaches the preset “normal” learning rate. Afterwards, the model will continue training according to the regular learning rate scheduling strategy (such as gradually decreasing the learning rate).

Vivid Metaphors in Daily Life

Metaphor 1: From a Novice Driver to an Old Driver

When you first learn to drive, you will definitely be cautious, start smoothly, accelerate slowly, and turn carefully. This is like the model in the “Warmup Steps” stage, cautiously exploring the data with a small learning rate effectively avoiding “flooring the gas pedal” and causing loss of control. As you become familiar with the vehicle and the road, you can gradually increase the speed and drive more smoothly. This is also true for the model; it needs a smooth transition period to “get familiar” with the data and understand the “distribution” characteristics of the data, rather than rushing headlong from the start.

Metaphor 2: New Employee Onboarding

When a new employee joins the company, you would not expect them to take on the most core and complex projects on the first day. The company usually arranges onboarding training to familiarize them with the company culture and business processes, providing necessary guidance to help them gradually adapt to the work environment. This process of “familiarization and adaptation” is the new employee’s “Warmup Steps”. When a model is in the early stages of training, its “brain” (parameter weights) is randomly initialized, knowing nothing about the task. Through “Warmup Steps”, it can start learning in a gentler way, gradually adjusting its internal “mechanisms” (such as attention mechanisms), thereby better integrating into the “work” and completing learning tasks efficiently.

Why are “Warmup Steps” So Important?

The role of “Warmup Steps” is mainly reflected in the following aspects:

1. Improve Training Stability: At the beginning of training, the parameters of the model are random, resulting in a very superficial “understanding” of the training data. If a large learning rate is used at this time, the model may perform overly aggressive parameter updates, causing the training process to oscillate violently or even diverge, failing to learn correctly. The warmup mechanism can effectively avoid this implementation of “dying before victory”, keeping the model stable in the early stages.
2. Avoid Early Overfitting: In the early stages of training, the model is prone to “early overfitting” to small batches of training data (mini-batch). By gradually increasing the learning rate, this phenomenon can be effectively alleviated, helping the model maintain the stability of the data distribution.
3. Improve Convergence Speed and Final Performance: Although it sounds like slow first and fast later, in fact, the warmup steps can help the model find a good initial state faster, thereby accelerating the subsequent convergence process and finally achieving better performance. Just like a runner, the early warmup allows them to run faster and longer in the subsequent race.
4. Especially Suitable for Large Models: For large deep learning models such as transformers, as well as the fine-tuning of currently popular Large Language Models (LLMs), Warmup Steps have almost become standard. It ensures smooth adjustment of the learning rate and significantly reduces errors that may occur during training.

Summary

“Warmup Steps” is an elegant and practical technique in deep learning training. By gradually increasing the learning rate in the early stages of training, it simulates the process of “warming up” and “adapting” of humans or other complex systems. This not only makes the model training more stable, avoiding the risk of early collapse, but also helps the model better explore and understand the data, ultimately improving training efficiency and model performance. Next time you see an AI model successfully complete a complex task, don’t forget that it may have started to fully display its skills after going through a period of patient “warming up”.

YOLO

Like “Golden Eyes”, How Does AI Recognize Everything at a Glance? — An Introduction to the YOLO Model

Imagine you walk into a room, sweep your eyes around, and immediately know where the sofa, the coffee table, and the pen are. This is the “Golden Eyes” and powerful cognitive ability of humans. In the field of artificial intelligence, there is a model that can do similar things and is extremely fast. It is the famous YOLO (You Only Look Once).

AI’s “Treasure Hunt”: What is Object Detection?

Before diving into YOLO, let’s understand a concept—“Object Detection”. It is like an AI “treasure hunt”. The task is not only to find specific objects (such as a “cat” in a picture) in an image or a video but also to circle it with a precise box and tell you what object it is.

Before YOLO appeared, AI object detection was usually a tedious “multi-step” process. You can imagine it as a detective:

1. Step 1 (Region Proposal): The detective scans the entire room roughly, guesses where clues might be hidden, and circles these suspicious areas one by one.
2. Step 2 (Classification): Then, the detective carefully checks and identifies each circled area to determine what exactly is inside.
   Although this process is rigorous, it is very time-consuming because the AI needs to “look” many times and go through multiple steps to get the result.

YOLO’s “Unique Skill”: Just One Look!

The birth of the YOLO model overturned the traditional “detective-style” detection process. Its core idea is just as its name suggests—“You Only Look Once”. It no longer goes step by step like a detective, but integrates all steps together and gets everything done at once.

You can imagine YOLO as a superman with the ability to “read ten lines at a glance” or even “understand everything at a glance”: the moment you look at a bookshelf, the location and category information of all red books are directly generated in your brain, instead of finding the books first and then identifying the colors.

How does YOLO achieve this? It mainly relies on the following key steps:

1. Divide and Conquer: Grid Division
   YOLO divides the input image evenly into many small grids (such as a 7x7 or 13x13 grid). This is like dividing the floor of a room into small square areas.

2. Predicting “Clues”: Bounding Boxes and Confidence
   For each small grid, YOLO will “make its own decision” to predict:

   • Does this grid contain the center of an object?
   • If so, what is the specific position and size of this object (represented by a “bounding box”)?
   • How confident is YOLO in this prediction (this is confidence, a value between 0 and 1, closer to 1 means more confidence)?
   • What category is this object most likely to be (such as a cat, a dog, or a car)? And what is the probability of belonging to that category?
     This is like every small square area telling you: “I might have a target here, it looks roughly like this, it is this color, and I am almost certain!”

3. Layer-by-Layer Selection: Non-Maximum Suppression (NMS)
   Since an object may span several grids, it may be repeatedly predicted by multiple grids. To avoid the same object being framed multiple times, YOLO uses a method called “Non-Maximum Suppression (NMS)”. It selects the bounding box with the highest confidence as the final prediction result and eliminates other bounding boxes with high overlap and lower confidence.
   This is like many small squares pointing to the same book, and NMS will pick out the square that “points most accurately and has the most confidence” as the final judgment. However, it is worth mentioning that later versions of YOLO, especially YOLOv10, have begun to try to reduce or even eliminate the dependence on NMS through new training strategies, thereby further improving efficiency and end-to-end performance.

Why is YOLO So Fast?

The biggest secret behind YOLO’s ability to “see everything at a glance” lies in the fact that it integrates all steps of object detection—region proposal, feature extraction, classification, and bounding box regression—into a single neural network. This allows image data to pass through this network “once” to get the final detection result, greatly reducing the amount of calculation and processing time.

To put it simply, before you needed to find a detective (Step 1), and after the detective finished the investigation, find an appraiser (Step 2). Now, you go directly to an “all-round AI”, who gives you the result at a glance, naturally much faster.

YOLO’s “Strengths” and “Weaknesses”

Strengths:

• Amazing Speed: The YOLO model is famous for its extremely high processing speed, capable of completing object detection at the millisecond level, making it very suitable for real-time applications.
• Strong Real-time Capability: This makes it an ideal choice for fields such as autonomous driving (real-time identification of pedestrians and vehicles), security monitoring (real-time detection of abnormal movements), industrial quality inspection (rapid detection of product defects), robot navigation, and sports event analysis.
• Low Background Error: Compared with some traditional methods that easily mistake background for objects, YOLO’s global perspective allows it to have a better understanding of background information, thereby reducing background false detections.
• Continuous Optimization: The YOLO series continues to iterate, making breakthroughs in accuracy and performance.

Weaknesses:

• Challenges in Small Object and Dense Object Detection: In early versions, due to the limitation of grid division, each grid could only predict a few objects. Therefore, for objects that are particularly small or closely stacked together in the image, YOLO sometimes performed worse than some more complex two-stage detectors.
• Bounding Box Localization Accuracy: Early YOLO was sometimes not “fine” enough in the positioning of bounding boxes. Although it could find the object, the box might not be that compact and precise.
  Of course, with the continuous development of the YOLO series, these shortcomings are gradually being overcome.

The Evolving “Golden Eyes”: The Evolution of the YOLO Family

Since the advent of YOLOv1 in 2016, the YOLO family has been like a team constantly striving to evolve. From v1, v2, v3… all the way to the latest version, each iteration has brought new breakthroughs in speed and accuracy.

• YOLOv9: Released in early 2024, YOLOv9 introduced breakthrough technologies such as Programmable Gradient Information (PGI) and Generalized Efficient Layer Aggregation Network (GELAN). It focuses on solving the challenge of information loss inherent in deep neural networks, ensuring that key information is retained throughout the detection process, thereby significantly improving the model’s learning ability, efficiency, and accuracy, especially performing well when dealing with lightweight models and complex scenes.

• YOLOv10: Launched by researchers from Tsinghua University around May 2024, YOLOv10 has pushed real-time object detection to a new height. Its biggest innovation lies in the successful elimination of the need for Non-Maximum Suppression (NMS) in the inference stage by adopting a consistent dual assignments training strategy and efficiency-accuracy driven model design. This means that while maintaining or even improving high accuracy, it greatly reduces computational overhead and inference latency, achieving more pure “end-to-end” object detection, further optimizing the trade-off between speed and accuracy.

The YOLO series models are like the “Swiss Army Knife” in the field of AI vision, powerful and efficient. From autonomous driving on the street to intelligent inspection in factories, from agricultural monitoring in the fields to computer-aided diagnosis in hospitals, YOLO and its family will continue to demonstrate the powerful ability of their “Golden Eyes” in more fields, allowing AI to better understand and see the world.

Title: ViT
Tags: [“Deep Learning”, “CV”]

Vision Transformer (ViT): How Does AI’s “Farsighted Eye” See Pictures?

Imagine how you and I identify whether a picture is a cat, a dog, or a car. Our brains quickly scan the entire picture, capture key features, and combine them to form a holistic perception. In the field of Artificial Intelligence, especially Computer Vision, enabling machines to do this has always been a goal pursued by scientists.

For a long time in the past, Convolutional Neural Networks (CNNs) were the overlords of image processing. It acts like a “nearsighted” detective, zooming in on local areas layer by layer, first identifying the most basic features such as edges and textures, and then gradually combining these small features into larger features (e.g., eyes, noses), and finally forming a recognition of the entire object. CNN performs well in many tasks, but it has a limitation: due to its design focus on local feature extraction, it may struggle to understand complex relationships between distant elements in an image, just like a person who only looks down at a book and ignores the full view of the surroundings.

However, in 2020, researchers at Google brought a “vision revolution” — Vision Transformer, or ViT for short. It boldly “transplanted” the Transformer model, originally used for processing text, into the field of image understanding, giving AI a “farsighted eye” for processing images, capable of seeing the whole picture at a glance and gaining insight into the connections between all elements in the picture.

What is Transformer? Evolution from Language to Vision

Before diving into ViT, let’s briefly understand its “predecessor” — the Transformer model. Transformer was originally designed to process natural language (such as the words we speak or write). Its core innovation is “Self-Attention”.

You can imagine a sentence as a string of pearl necklaces. When we understand this sentence, the meaning of each word (a pearl) is not isolated; it is influenced by other words in the sentence. For example, the word “apple” refers to a brand in “Apple phone” and a fruit in “eat an apple”. Transformer’s self-attention mechanism allows the model to “pay attention” to all other words in the sentence when processing each word and adjust the understanding of the current word according to their importance. It can capture very long-distance dependencies, which is especially powerful when processing long texts.

ViT’s disruptiveness lies in its simple yet bold idea: since Transformer is so excellent at understanding the order and relationship of text, why can’t we treat images as a kind of “sequence” as well?

How ViT “Sees” Pictures: A Four-Step “Puzzle Master”

To enable the vision-superior Transformer to process images, ViT underwent some ingenious modifications. We use a “puzzle master” analogy to analyze the workflow of ViT:

1. Disassembling the Picture: Cutting the Image into “Puzzle Pieces”
   Imagine you have a magnificent landscape painting in front of you. The first thing ViT does is evenly cut this painting into many small squares, just like a puzzle. These small squares are called “Image Patches” in ViT. The size of each small square is fixed, such as 16x16 pixels. In this way, a large picture is converted into a series of ordered small picture blocks. This step is like cutting each page of a book into strips of the same size for subsequent processing.

2. Encoding “Puzzle Pieces”: Giving Each Piece a “Digital Identity”
   Just cutting is not enough; the machine cannot directly understand these image blocks. Therefore, ViT will generate a unique “digital identity” for each small block, known in the industry as “Linear Embedding”. This “digital identity” is a string of number vectors that concentrates visual information such as color, texture, and shape of the image block. This is like taking an “ID photo” for each puzzle piece and then converting it into a digital code that the machine can understand.

3. Adding “Position Information”: Remembering the “Seat” of Each Piece
   Now we have a pile of digitally encoded puzzle pieces, but their order is scrambled. The model doesn’t know which piece should be in the top left corner and which in the bottom right corner. To solve this problem, ViT will add a “Positional Embedding” to each encoded image block. This is like writing its original coordinates (e.g., row 3, column 5) on the back of each puzzle piece, so that the Transformer knows which position in the picture each piece comes from during processing.

4. Transformer Encoder: “Global Analysis” of the Strongest Brain
   After the preparation work is completed, these image block sequences with position information can be sent to the core part of the Transformer — the Encoder. The “self-attention mechanism” stacked layer by layer inside the encoder begins to work:

   • Global Association of “You in Me, Me in You”: When the encoder processes a specific image block (e.g., the leaf part of a tree in the painting), it does not view this leaf in isolation. Through the self-attention mechanism, the encoding of this leaf will examine the encodings of all other image blocks (such as the trunk, distant mountains, grass on the ground) and assign different “attention weights” based on their importance to understanding the “leaf”. For example, it will find that the “trunk” is most closely related to the “leaf”, while the “distant mountains” are weakly related. This mechanism allows the model to establish complex relationships between all elements in the image and capture global contextual information. This is like a team meeting where everyone listens carefully to others’ views when speaking and combines them to form a more comprehensive view.

   • Deep Learning and Feature Integration: After processing through multiple layers of self-attention mechanisms and Feed-Forward Networks, the digital identity of each image block will become richer and more meaningful. They are no longer isolated pixels but “high-level features” that integrate the contextual information of the entire picture.

Finally, ViT extracts a special class identifier (usually an extra “Class Token”) from all processed image blocks and sends it to a simple classifier (usually a fully connected layer) to output the final category prediction result of the image, such as “this is a cat” or “this is a car”.

Advantages and Challenges of ViT:

Advantages:

• Global Vision, Long-Range Dependencies: The core advantage of ViT lies in the self-attention mechanism enabling it to capture long-range dependencies between different regions in an image, which is very beneficial for understanding complex scenes and object contexts.
• Higher Generalization Ability: With massive training data, ViT demonstrates stronger generalization ability than CNNs, capable of learning more powerful and general visual representations.
• Potential for Fusion with Other Modalities: Since Transformer itself is a general architecture for processing sequence data, this makes it easier for ViT to fuse with data from other modalities such as text and audio in the future, building more powerful multi-modal AI models.

Challenges:

• Data Hungry: ViT requires massive amounts of training data to unleash its potential. Without sufficient data, it often performs worse than CNNs. Typically, ViT is pre-trained on large-scale datasets (such as JFT-300M, ImageNet-21K) and then fine-tuned on specific tasks.
• High Computational Cost: The computational complexity of the self-attention mechanism is high, especially when processing high-resolution images, its computational resource and memory consumption far exceed CNN models with equivalent parameters.

Latest Progress and Applications of ViT:

Since ViT was proposed, it has quickly become a research hotspot in the field of computer vision and has spawned a large number of variants and improved models, such as Swin Transformer and MAE, which solve some computational efficiency and data dependency problems while maintaining the core idea of ViT.

Currently, ViT and its variants are widely used in:

• Image Classification, Object Detection, Semantic Segmentation: In these basic visual tasks, ViT has surpassed many traditional CNN models, achieving SOTA (State-Of-The-Art) performance.
• Medical Image Analysis: Assisting doctors in diagnosing diseases, such as identifying lesion areas in X-rays or CT scans.
• Autonomous Driving: Helping vehicles understand complex road environments and identify pedestrians, vehicles, and traffic signs.
• Multi-Modal Learning: Combined with large language models to achieve Image Captioning and Text-to-Image Generation, such as generative AI models like Midjourney and DALL-E.
• Video Understanding: Processing video frame sequences to achieve behavior recognition, event detection, and other tasks.

In short, the emergence of ViT is a milestone in the field of AI computer vision. It proves that the Transformer architecture is not limited to text but can also shine in image processing. It is like equipping AI with a pair of “farsighted eyes” capable of perceiving the whole situation, taking a solid and important step for artificial intelligence in understanding and perceiving our colorful visual world. In the future, with the improvement of model efficiency and the emergence of more general data, ViT and its family will demonstrate their powerful potential in more fields.

References:
Vision Transformers in Autonomous Driving. [Online]. Available: https://github.com/topics/vision-transformers-for-autonomous-driving.
How DALL-E, MidJourney, Stable Diffusion & Other AI Image Generators Work. [Online]. Available: https://www.mage.ai/blog/how-ai-image-generators-work/.
Vision Transformers are scaling up for video and 3D. [Online]. Available: https://huggingface.co/papers/2301.07727.
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. [Online]. Available: https://arxiv.org/abs/2010.11929.

Title: Vicuna
Tags: [“Deep Learning”, “NLP”, “LLM”]

In the field of Artificial Intelligence, the development of Large Language Models (LLMs) is changing rapidly, and one notable concept is Vicuna. For non-professionals, this name might be a bit unfamiliar, but it plays a significant role in the AI world. We can imagine Vicuna as a “smart apprentice” who has mastered the skills of natural conversation with humans in an efficient and economical way, even rivaling top “masters.”

1. What is Vicuna? — How a Smart “Apprentice” is Cultivated

In the “big family” of Artificial Intelligence, Large Language Models (LLMs) are like “super brains” capable of understanding and generating human language. They have learned to phrase sentences, reason logically, and even create content by reading massive amounts of text data. The familiar ChatGPT is outstanding among such “super brains.”

Vicuna can be seen as a “rising star” in this family. It did not start learning from scratch but stood on the shoulders of giants — it was “further educated” based on the LLaMA model open-sourced by Meta. If we view LLaMA as a “scholar” with extensive knowledge but not very good at chatting, then Vicuna is a “social expert” proficient in dialogue, forged on the basis of this scholar through special “training” methods.

This “further education” process is technically called “Instruction Fine-tuning.” Imagine the LLaMA model as a gifted student who has read ten thousand books and has rich knowledge but may not be articulate. The creators of Vicuna (researchers from institutions like Stanford, UC Berkeley, MBZUAI, etc.) collected a large amount of real conversation records between humans and ChatGPT (about 70,000 dialogues from ShareGPT). These conversation records are like “chat tutorials” or “examples of master dialogues.” By learning these examples, Vicuna imitated ChatGPT’s conversation style and response patterns.

It is worth mentioning that the cost of this “apprentice training program” is very low; it is claimed that training the Vicuna 13B model cost only about $300. This is like finding an extremely efficient learning method to cultivate an outstanding AI assistant at a very small cost.$

2. Vicuna’s “Secret of Learning” and Powerful Capabilities

The reason Vicuna stands out is due to its unique “secret of learning”:

1. “Master Mimic”: Learning from Top-tier Dialogues
   By learning from high-quality dialogue data between users and ChatGPT, Vicuna effectively observed directly how a top “dialogue master” communicates with people. This “immersion” training method allowed Vicuna to quickly master the ability to generate fluent, detailed, and structured answers.

2. “Small but Mighty”: Lower Cost, Similar Performance
   Compared to giant models with hundreds of billions of parameters, Vicuna (e.g., the 13 billion parameter version) appears much “smaller.” But surprisingly, even with a smaller size, Vicuna achieved about 90% of ChatGPT’s quality in dialogue assessments by GPT-4. This means that in many common chat scenarios, it can provide an experience very close to ChatGPT, but with significantly reduced operating costs.

   This is like a top chef (ChatGPT), who can make the most delicious dishes but requires expensive ingredients and complex equipment. Vicuna is like a talented young chef who carefully studied the master’s recipes and can make dishes that are 90% as delicious using more common ingredients and simpler tools, costing less and being easier to popularize.

3. “Auto-Judge”: GPT-4 as the Referee
   To objectively evaluate Vicuna’s conversational ability, researchers adopted a clever method: they invited another powerful AI model — GPT-4 — to act as the “judge.” GPT-4 scores and explains in detail Vicuna’s and other models’ answers based on multiple dimensions such as helpfulness, relevance, accuracy, and level of detail. This way of evaluating AI by top AI ensures the authority and objectivity of Vicuna’s capability assessment.

3. Significance and Applications of Vicuna

The emergence of Vicuna has epoch-making significance for the entire AI field:

• “Democratization” of AI: In the past, only a few large technology companies had the ability to train and deploy top AI models. As an open-source model, Vicuna’s low training cost and excellent performance have greatly lowered the threshold for individual developers, small teams, and research institutes to enter this field. This is like high-end custom clothing finding a way into ordinary households at a more affordable price due to more efficient production methods. This promotes the democratization and popularization of artificial intelligence technology.

• Innovation “Accelerator”: Vicuna’s high capability, free availability, and flexible research license provide convenience for researchers and developers to rapidly prototype conversational AI applications. Many applications and research projects based on Vicuna have emerged, such as models like LLaVA, which were developed further based on Vicuna.

• Multi-functional Assistant: Vicuna can be widely applied in various scenarios, including:

  • Intelligent Customer Service: Providing 24/7 answering services and automating the handling of common questions.
  • Content Creation: Assisting in writing articles and generating creative text.
  • Information Retrieval and Q&A: Quickly extracting and answering user questions from a large amount of information.
  • Educational Support: Providing personalized learning support and answering doubts.

4. Limitations and Future Outlook

Although Vicuna performs well, it is not perfect. Like many current large language models, Vicuna may still encounter difficulties when dealing with tasks requiring complex reasoning or mathematical calculations, and may also have limitations in ensuring factual accuracy. In addition, recent research (October 2025) also points out that large language models, including Vicuna, still appear inauthentic when imitating the subtleties of human natural conversation (such as tone, social cues, and cohesion), potentially over-imitating, misusing fillers, or displaying unnatural openings and closings. This indicates that AI still has a long way to go in truly understanding and simulating human emotions and social interactions.

However, Vicuna’s success, as an important milestone for the open-source community in the field of large language models, demonstrates that small models can also burst with great energy through efficient fine-tuning and data distillation. It inspires more researchers to devote themselves to the research and development of open-source AI, jointly promoting the rapid development and popularization of artificial intelligence technology. In the future, with the continuous advancement of technology, we have reason to believe that Vicuna and its derivative models will play an increasingly important role in non-commercial and research fields.

WGAN: The Secret Weapon for Making AI Art More “Realistic”

Imagine you are an art authenticator, and your peer is an emerging painter. This painter is always trying to create extremely realistic, almost indistinguishable replicas of famous paintings. Over time, your ability to authenticate becomes stronger, and the painter’s imitation skills also become more superb, eventually continuously reaching a state where you can hardly distinguish the true from the false. This is the core idea of one of the most exciting technologies in the current field of artificial intelligence: Generative Adversarial Networks (GANs).

Today, we are going to dive into a star member of the GANs family: WGAN (Wasserstein Generative Adversarial Network). It is like building a more stable bridge between the “painter” and the “authenticator” mentioned above, allowing them to learn from each other better and finally create even more amazing works.

1. What are GANs? — The “Cat and Mouse Game” in AI

Before WGAN, we must first understand its predecessor: GANs. GANs consist of two parts:

1. Generator (G): Imagine it as an imitating painter. Its task is to generate new data (such as a picture) based on random input (such as a string of numbers). At first, it paints very poorly, like a doodling apprentice.
2. Discriminator (D): Imagine it as an art authenticator. Its task is to judge whether the received data is real (from the real dataset) or forged (from the generator). It will strive to learn how to distinguish between true and false.

There is a continuous “adversarial game” between the two:

• Generator G constantly tries to generate more realistic fake data to fool Discriminator D.
• Discriminator D constantly improves its discrimination ability, striving not to be fooled by Generator G.

Through this “cat and mouse game”, Generator G constantly improves under the “sharp” eyes of Discriminator D, and is finally able to generate fake data that is very similar to real data. For example, generating faces, animals, and even anime characters, the realism is breathtaking.

However, traditional GANs also have some troublesome problems, just like the authenticator and the imitating painter will “get stuck” at certain times:

• Unstable Training: The model often oscillates during the training process and cannot converge, just as a painter sometimes falls into a creative bottleneck, and an authenticator may suddenly fail.
• Mode Collapse: In order to reliably fool the discriminator, the generator may only generate a few specific samples that the discriminator considers real, resulting in very poor diversity of generated samples. For example, the painter only wants to draw a “safe” cat, ignoring other felines such as tigers and lions.

2. WGAN Emerges: Saying Goodbye to the Pain Points of the “Cat and Mouse Game”

The appearance of WGAN is exactly to solve these pain points of traditional GANs. By introducing a brand-new mathematical concept—Wasserstein Distance (also known as Earth Mover’s Distance, EMD), it modified the “game rules” of GANs.

Core Idea Shift:
If the traditional GANs discriminator judges “true or false” (binary classification), then the discriminator in WGAN (more accurately called the Critic) no longer simply judges 0 or 1, but evaluates “how fake” or “how real” the generated sample is, giving a continuous score. It is no longer just a “yes/no” referee, but more like a “scorer”.

This change brings huge benefits:

1. More Stable Training, Easier Convergence: It’s like having a smoother communication channel between the painter and the critic. They can better understand each other’s intentions and thus improve steadily.
2. Effectively Alleviates Mode Collapse: The critic can evaluate the “quality” of generated samples more carefully, and will not be easily deceived by a small number of high-quality samples, thereby encouraging the generator to explore more diverse creations.
3. The Learning Process Has Practical Meaning: The score given by the critic can directly reflect the quality of the generated image. This score can serve as a meaningful indicator during the training process, letting you know how much the “painter’s” level has improved.

3. The Core of WGAN: From JS Divergence to Wasserstein Distance (EMD)

To better understand why WGAN is superior, we have to mention its improved mathematical foundation.

In traditional GANs, the discriminator measures the difference between the real data distribution and the generated data distribution, usually using Jensen-Shannon (JS) divergence. JS divergence is an indicator that measures the similarity of two probability distributions.

Drawbacks of JS Divergence:
Imagine you have two piles of sand, representing the real data distribution and the generated data distribution respectively. If the two piles of sand do not overlap at all (which is common in high-dimensional spaces), JS divergence will directly tell you that they are “completely different” and give a large fixed value. This is like telling the painter: “Your painting is completely different from the real one, but where exactly the difference is, I don’t know, because they are not in the same league at all.” This leads to vanishing gradients, the generator gets no useful feedback, and learning efficiency is low.

Introducing Wasserstein Distance (EMD):
WGAN switches to Wasserstein Distance. Its concept is very intuitive: it measures the minimum cost required to move one pile of sand (generated data distribution) into another pile of sand (real data distribution). This cost is the sum of the amount of sand moved multiplied by the moving distance.

Sand Pile Analogy:
Whether two piles of sand completely overlap, partially overlap, or do not overlap at all, you can always calculate the minimum cost required to move one pile to another. This means that the WGAN critic can always provide meaningful gradient information to the generator, even if the two are far apart, it knows “where the difference is” and “which direction to work towards”. This makes the training process smoother and more stable.

4. WGAN Implementation Details and WGAN-GP Improvement

WGAN made several key modifications in implementation:

1. Remove the Sigmoid Activation Function in the Output Layer of the Discriminator: Because the critic no longer performs binary classification, but directly outputs a score.
2. The Critic is Not Trained to Optimality: Compared to the generator, the critic is trained more times, but it does not need to be trained to the extreme like traditional GANs, because the gradient of the Wasserstein distance will always exist.
3. Weight Clipping: This is a mechanism introduced in the original WGAN to force the critic to satisfy a mathematical condition (Lipschitz continuity) to ensure the effective calculation of the Wasserstein distance. However, the disadvantage of weight clipping is that the clipping range needs to be manually adjusted. Improper clipping may lead to insufficient model capacity or gradient explosion/vanishing.

To solve the problems caused by weight clipping, researchers proposed WGAN-GP (WGAN with Gradient Penalty) [1]. WGAN-GP uses Gradient Penalty to replace weight clipping. It directly limits the gradient norm of the critic by adding a term to its loss function, thereby better satisfying the Lipschitz continuity condition while avoiding the disadvantages of weight clipping. WGAN-GP has become a widely used WGAN variant due to its more stable training and better generation effects.

5. WGAN Application Prospects and Future Development

WGAN and its improved version WGAN-GP have achieved significant success in various generation tasks, including:

• Image Generation: Generating realistic faces, animals, landscapes, etc., and even creating art works that conform to specific styles [2].
• Image-to-Image Translation: For example, converting sketches to real photos, or converting day scenes to night scenes.
• Data Augmentation: In fields where data is scarce, such as medical imaging and autonomous driving, WGAN can generate new training data to help models learn better.
• High-Resolution Image Synthesis: Combined with other technologies, WGAN can generate amazing high-resolution images.

With the deepening of research, GANs and WGAN are still developing. Researchers are exploring more stable training methods, more efficient model architectures, and how to better control generated content, so that AI can not only “paint alike”, but also “paint creatively” and “paint meaningfully”.

Conclusion

WGAN is an important milestone in the history of Generative Adversarial Networks. By introducing Wasserstein distance, it effectively solves the difficult problems of unstable training and mode collapse in traditional GANs. It has taken a solid step for AI to master the “painting” skill, making machine-generated images more realistic and diverse, and also opening up infinite possibilities for future creative applications. From “cat and mouse game” to “moving sand”, WGAN leads us to a more creative era of artificial intelligence with a more elegant mathematical way.

References:
[1] Improved Training of Wasserstein GANs. arXiv.
[2] “WGAN and Real-world Applications - Analytics Vidhya”.