diff --git a/404.html b/404.html index ff1e962..677a31d 100644 --- a/404.html +++ b/404.html @@ -1,7 +1,7 @@ -Eric X. Liu's Personal Page

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\ No newline at end of file +[0841892] \ No newline at end of file diff --git a/about/index.html b/about/index.html index bfe8493..ab8f971 100644 --- a/about/index.html +++ b/about/index.html @@ -5,12 +5,12 @@ My work focuses on Infrastructure Performance and Customer Engineering, specific I am a Staff Software Engineer and Tech Lead Manager (TLM) at Google, based in Sunnyvale, CA. My work focuses on Infrastructure Performance and Customer Engineering, specifically for GPUs and TPUs. I lead teams that bridge the gap between cutting-edge AI hardware and the latest ML models (like Gemini), ensuring optimal performance and reliability at Google Cloud scale. I thrive in the ambiguous space where hardware constraints meet software ambition—whether it’s debugging race conditions across thousands of chips or designing API surfaces for next-gen models.">

About

Eric Liu

Hi, I’m Eric Liu.

I am a Staff Software Engineer and Tech Lead Manager (TLM) at Google, based in Sunnyvale, CA.

My work focuses on Infrastructure Performance and Customer Engineering, specifically for GPUs and TPUs. I lead teams that bridge the gap between cutting-edge AI hardware and the latest ML models (like Gemini), ensuring optimal performance and reliability at Google Cloud scale. I thrive in the ambiguous space where hardware constraints meet software ambition—whether it’s debugging race conditions across thousands of chips or designing API surfaces for next-gen models.

Beyond the code, I maintain this “digital garden” where I document my projects and learnings. It serves as my second brain, capturing everything from technical deep dives to random musings. I believe in “learning in public”—so you’ll find unpolished notes on troubleshooting Kubernetes clusters alongside recipes I’m refining. It’s not just a blog; it’s a living repository of my curiosity.

Personal Interests Link to heading

I’m a tinkerer at heart, whether digital or physical:

  • Homelab: Kubernetes, Proxmox, and self-hosted services. I love over-engineering my home network.
  • DIY & Jeep: Maintaining and modifying my Jeep, and general DIY projects.
  • Cooking: experimenting with new recipes and techniques.

Welcome to my corner of the internet.

\ No newline at end of file +[0841892] \ No newline at end of file diff --git a/authors/index.html b/authors/index.html index 46ee05b..f684f58 100644 --- a/authors/index.html +++ b/authors/index.html @@ -1,7 +1,7 @@ -Authors · Eric X. Liu's Personal Page

Authors

    \ No newline at end of file +[0841892] \ No newline at end of file diff --git a/categories/index.html b/categories/index.html index 061830c..b301df9 100644 --- a/categories/index.html +++ b/categories/index.html @@ -1,7 +1,7 @@ -Categories · Eric X. Liu's Personal Page

    Categories

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.gu{color:#79c0ff}body.colorscheme-auto .chroma .gt{color:#ff7b72}body.colorscheme-auto .chroma .gl{text-decoration:underline}body.colorscheme-auto .chroma .w{color:#6e7681}} \ No newline at end of file diff --git a/index.html b/index.html index 8e7de81..4c659c6 100644 --- a/index.html +++ b/index.html @@ -1,8 +1,8 @@ -Eric X. Liu's Personal Page
      avatar

      Eric X. Liu

      • +
      \ No newline at end of file +[0841892] \ No newline at end of file diff --git a/posts/benchmarking-llms-on-jetson-orin-nano/index.html b/posts/benchmarking-llms-on-jetson-orin-nano/index.html index 99f3420..c8851b3 100644 --- a/posts/benchmarking-llms-on-jetson-orin-nano/index.html +++ b/posts/benchmarking-llms-on-jetson-orin-nano/index.html @@ -8,9 +8,9 @@ NVIDIA’s Jetson Orin Nano promises impressive specs: 1024 CUDA cores, 32 Tensor Cores, and 40 TOPS of INT8 compute performance packed into a compact, power-efficient edge device. On paper, it looks like a capable platform for running Large Language Models locally. But there’s a catch—one that reveals a fundamental tension in modern edge AI hardware design. After running 66 inference tests across seven different language models ranging from 0.5B to 5.4B parameters, I discovered something counterintuitive: the device’s computational muscle sits largely idle during single-stream LLM inference. The bottleneck isn’t computation—it’s memory bandwidth. This isn’t just a quirk of one device; it’s a fundamental characteristic of single-user, autoregressive token generation on edge hardware—a reality that shapes how we should approach local LLM deployment.">
      \ No newline at end of file +[0841892] \ No newline at end of file diff --git a/posts/blog-draft/index.html b/posts/blog-draft/index.html index ecaad4e..f0faebf 100644 --- a/posts/blog-draft/index.html +++ b/posts/blog-draft/index.html @@ -11,9 +11,9 @@ To move beyond being a chatbot, an agent needs to be able to affect its world. D Here are the practical lessons learned, organized by the layers of the agentic stack: Environment, Runtime, and Capabilities. Layer 1: The Environment – Breaking the Sandbox Link to heading To move beyond being a chatbot, an agent needs to be able to affect its world. Deep integration starts with networking.">

      Deployment Lessons and My Take on Self-Hosting OpenClaw

      4-minute read

      Deploying autonomous agents like OpenClaw on a self-hosted Kubernetes cluster offers significantly more control and integration potential than cloud-hosted alternatives. However, moving from a standard SaaS model to running your own intelligence infrastructure introduces several deployment challenges.

      Here are the practical lessons learned, organized by the layers of the agentic stack: Environment, Runtime, and Capabilities.

      Layer 1: The Environment – Breaking the Sandbox @@ -47,4 +47,4 @@ Layer 1: The Environment – Breaking the Sandbox Link to heading To move beyond 2016 - 2026 Eric X. Liu -[8aa06e9]

      \ No newline at end of file +[0841892] \ No newline at end of file diff --git a/posts/breville-barista-pro-maintenance/index.html b/posts/breville-barista-pro-maintenance/index.html index 7197a13..844c45c 100644 --- a/posts/breville-barista-pro-maintenance/index.html +++ b/posts/breville-barista-pro-maintenance/index.html @@ -8,9 +8,9 @@ The Breville Barista Pro has two distinct, automated maintenance procedures: the Cleaning (Flush) Cycle and the Descale Cycle. It is important to understand that these are not interchangeable, as they address different types of buildup within the machine.">

      Breville Barista Pro Maintenance

      5-minute read

      Proper maintenance is critical for the longevity and performance of a Breville Barista Pro espresso machine. Consistent cleaning not only ensures the machine functions correctly but also directly impacts the quality of the espresso produced. This guide provides a detailed, technical breakdown of the essential maintenance routines, from automated cycles to daily upkeep.

      Understanding the Two Primary Maintenance Cycles @@ -25,4 +25,4 @@ Understanding the Two Primary Maintenance Cycles Link to heading The Breville Ba 2016 - 2026 Eric X. Liu -[8aa06e9]

      \ No newline at end of file +[0841892] \ No newline at end of file diff --git a/posts/debugging-authentik-performance/index.html b/posts/debugging-authentik-performance/index.html index 977be82..c5ff84f 100644 --- a/posts/debugging-authentik-performance/index.html +++ b/posts/debugging-authentik-performance/index.html @@ -1,9 +1,9 @@ Why Your "Resilient" Homelab is Slower Than a Raspberry Pi · Eric X. Liu's Personal Page

      Why Your "Resilient" Homelab is Slower Than a Raspberry Pi

      5-minute read

      In the world of self-hosting, there are many metrics for success: 99.9% uptime, sub-second latency, or a perfect GitOps pipeline. But for those of us running “production” at home, there is only one metric that truly matters: The Wife Acceptance Factor (WAF).

      My detailed Grafana dashboards said everything was fine. But my wife said the SSO login was “slow sometimes.” She was right. Debugging it took me down a rabbit hole of connection pooling, misplaced assumptions, and the harsh reality of running databases on distributed storage.

      Here is a breakdown of the symptoms, the red herrings, and the root cause that was hiding in plain sight.

      The Environment @@ -44,4 +44,4 @@ My detailed Grafana dashboards said everything was fine. But my wife said the SS 2016 - 2026 Eric X. Liu -[8aa06e9]

      \ No newline at end of file +[0841892] \ No newline at end of file diff --git a/posts/espresso-theory-application-a-guide-for-the-breville-barista-pro/index.html b/posts/espresso-theory-application-a-guide-for-the-breville-barista-pro/index.html index 1ff9538..c7b3fda 100644 --- a/posts/espresso-theory-application-a-guide-for-the-breville-barista-pro/index.html +++ b/posts/espresso-theory-application-a-guide-for-the-breville-barista-pro/index.html @@ -1,9 +1,9 @@ Mastering Your Breville Barista Pro: The Ultimate Guide to Dialing In Espresso · Eric X. Liu's Personal Page

      Mastering Your Breville Barista Pro: The Ultimate Guide to Dialing In Espresso

      6-minute read

      Are you ready to transform your home espresso game from good to genuinely great? The Breville Barista Pro is a fantastic machine, but unlocking its full potential requires understanding a few key principles. This guide will walk you through the systematic process of dialing in your espresso, ensuring every shot is delicious and repeatable.

      Our overarching philosophy is simple: isolate and change only one variable at a time. While numbers are crucial, your palate is the ultimate judge. Dose, ratio, and time are interconnected, but your grind size is your most powerful lever.

      Let’s dive in!

      Part 1: The Foundation — Dose (The Weight of Dry Coffee) @@ -20,4 +20,4 @@ Our overarching philosophy is simple: isolate and change only one variable at a 2016 - 2026 Eric X. Liu -[8aa06e9]

      \ No newline at end of file +[0841892] \ No newline at end of file diff --git a/posts/flashing-jetson-orin-nano-in-virtualized-environments/index.html b/posts/flashing-jetson-orin-nano-in-virtualized-environments/index.html index b645d7a..789ea1f 100644 --- a/posts/flashing-jetson-orin-nano-in-virtualized-environments/index.html +++ b/posts/flashing-jetson-orin-nano-in-virtualized-environments/index.html @@ -12,9 +12,9 @@ Link to heading -Flashing NVIDIA Jetson devices remotely presents unique challenges when the host machine is virtualized. This article documents the technical challenges, failures, and eventual success of flashing a Jetson Orin Nano Super developer kit using NVIDIA SDK Manager in various virtualized environments, specifically focusing on QEMU/KVM virtual machines and LXC containers on Proxmox VE.">
      \ No newline at end of file +[0841892] \ No newline at end of file diff --git a/posts/how-rvq-teaches-llms-to-see-and-hear/index.html b/posts/how-rvq-teaches-llms-to-see-and-hear/index.html index 2e2d29c..1e835e1 100644 --- a/posts/how-rvq-teaches-llms-to-see-and-hear/index.html +++ b/posts/how-rvq-teaches-llms-to-see-and-hear/index.html @@ -1,9 +1,9 @@ Beyond Words: How RVQ Teaches LLMs to See and Hear · Eric X. Liu's Personal Page

      Beyond Words: How RVQ Teaches LLMs to See and Hear

      6-minute read

      Large Language Models (LLMs) are masters of text, but the world is not made of text alone. It’s a symphony of sights, sounds, and experiences. The ultimate goal for AI is to understand this rich, multi-modal world as we do. But how do you teach a model that thinks in words to understand a picture of a sunset or the melody of a song?

      The answer lies in creating a universal language—a bridge between the continuous, messy world of pixels and audio waves and the discrete, structured world of language tokens. One of the most elegant and powerful tools for building this bridge is Residual Vector Quantization (RVQ).

      This article dives deep into RVQ, exploring how it turns raw data into meaningful semantic IDs and how these IDs, in turn, unlock multi-modal understanding in LLMs.

      What is Residual Vector Quantization? The Art of Smart Compression @@ -18,4 +18,4 @@ The answer lies in creating a universal language—a bridge between the continuo 2016 - 2026 Eric X. Liu -[8aa06e9]

      \ No newline at end of file +[0841892] \ No newline at end of file diff --git a/posts/index.html b/posts/index.html index b5a85f0..aeda848 100644 --- a/posts/index.html +++ b/posts/index.html @@ -1,6 +1,6 @@ -Posts · Eric X. Liu's Personal Page

      Posts

      \ No newline at end of file +[0841892] \ No newline at end of file diff --git a/posts/jellyfin-sso-with-authentik/index.html b/posts/jellyfin-sso-with-authentik/index.html index 3ae5be3..b624e05 100644 --- a/posts/jellyfin-sso-with-authentik/index.html +++ b/posts/jellyfin-sso-with-authentik/index.html @@ -8,9 +8,9 @@ The configuration is best handled via API (curl) rather than the UI, as it ensures all fields are correctly typed and persistent.">

      Setting Up Jellyfin SSO with Authentik: Surviving the Beta

      3-minute read

      I recently integrated Jellyfin with Authentik for Single Sign-On (SSO). While the plugin works, it is still very much in an early development phase. The logging is often sparse or cryptic, and the feedback loop can be frustrating. Here is a guide focused on the obscure errors you might encounter and the simple fixes that aren’t immediately obvious.

      The Setup @@ -71,4 +71,4 @@ Do not rely on header forwarding magic. Force the scheme in the plugin configura 2016 - 2026 Eric X. Liu -[8aa06e9]

      \ No newline at end of file +[0841892] \ No newline at end of file diff --git a/posts/mixture-of-experts-moe-models-challenges-solutions-in-practice/index.html b/posts/mixture-of-experts-moe-models-challenges-solutions-in-practice/index.html index 49ae09f..b4742da 100644 --- a/posts/mixture-of-experts-moe-models-challenges-solutions-in-practice/index.html +++ b/posts/mixture-of-experts-moe-models-challenges-solutions-in-practice/index.html @@ -9,9 +9,9 @@ The Problem: Many routing mechanisms, especially “Top-K routing,” involve a discrete, hard selection process. A common function is KeepTopK(v, k), which selects the top k scoring elements from a vector v and sets others to $-\infty$ or $0$.">

      Mixture-of-Experts (MoE) Models Challenges & Solutions in Practice

      7-minute read

      Mixture-of-Experts (MoEs) are neural network architectures that allow different parts of the model (called “experts”) to specialize in different types of inputs. A “gating network” or “router” learns to dispatch each input (or “token”) to a subset of these experts. While powerful for scaling models, MoEs introduce several practical challenges.

      1. Challenge: Non-Differentiability of Routing Functions @@ -44,4 +44,4 @@ The Top-K routing mechanism, as illustrated in the provided ima 2016 - 2026 Eric X. Liu -[8aa06e9]

      \ No newline at end of file +[0841892] \ No newline at end of file diff --git a/posts/open-webui-openai-websearch/index.html b/posts/open-webui-openai-websearch/index.html index fd025fb..973642a 100644 --- a/posts/open-webui-openai-websearch/index.html +++ b/posts/open-webui-openai-websearch/index.html @@ -1,9 +1,9 @@ How I Got Open WebUI Talking to OpenAI Web Search · Eric X. Liu's Personal Page

      How I Got Open WebUI Talking to OpenAI Web Search

      6-minute read

      OpenAI promised native web search in GPT‑5, but LiteLLM proxy deployments (and by extension Open WebUI) still choke on it—issue #13042 tracks the fallout. I needed grounded answers inside Open WebUI anyway, so I built a workaround: route GPT‑5 traffic through the Responses API and mask every web_search_call before the UI ever sees it.

      This post documents the final setup, the hotfix script that keeps LiteLLM honest, and the tests that prove Open WebUI now streams cited answers without trying to execute the tool itself.

      Why Open WebUI Broke @@ -86,4 +86,4 @@ This post documents the final setup, the hotfix script that keeps LiteLLM honest 2016 - 2026 Eric X. Liu -[8aa06e9]

      \ No newline at end of file +[0841892] \ No newline at end of file diff --git a/posts/openwrt-mwan3-wireguard-endpoint-exclusion/index.html b/posts/openwrt-mwan3-wireguard-endpoint-exclusion/index.html index bee65c8..3a7ba96 100644 --- a/posts/openwrt-mwan3-wireguard-endpoint-exclusion/index.html +++ b/posts/openwrt-mwan3-wireguard-endpoint-exclusion/index.html @@ -5,9 +5,9 @@ Link to heading -When using WireGuard together with MWAN3 on OpenWrt, the tunnel can fail to establish or flap when the peer’s IP is routed into the tunnel itself. This is a classic routing bootstrap problem: WireGuard wants to route 0.0.0.0/0 into the tunnel, but the UDP packets to the peer’s public endpoint also get captured, so they never reach the Internet to bring the tunnel up.">
      \ No newline at end of file +[0841892] \ No newline at end of file diff --git a/posts/page/2/index.html b/posts/page/2/index.html index 92b51cf..f4e88bf 100644 --- a/posts/page/2/index.html +++ b/posts/page/2/index.html @@ -1,6 +1,6 @@ -Posts · Eric X. Liu's Personal Page

      Posts

      \ No newline at end of file +[0841892] \ No newline at end of file diff --git a/posts/page/3/index.html b/posts/page/3/index.html index 158331c..4bf66dc 100644 --- a/posts/page/3/index.html +++ b/posts/page/3/index.html @@ -1,6 +1,6 @@ -Posts · Eric X. Liu's Personal Page

      Posts

      \ No newline at end of file +[0841892] \ No newline at end of file diff --git a/posts/ppo-for-language-models/index.html b/posts/ppo-for-language-models/index.html index ef5f933..f05b319 100644 --- a/posts/ppo-for-language-models/index.html +++ b/posts/ppo-for-language-models/index.html @@ -2,9 +2,9 @@ You may have seen diagrams like the one below, which outlines the RLHF training process. It can look intimidating, with a web of interconnected models, losses, and data flows. ">

      A Deep Dive into PPO for Language Models

      7-minute read

      Large Language Models (LLMs) have demonstrated astonishing capabilities, but out-of-the-box, they are simply powerful text predictors. They don’t inherently understand what makes a response helpful, harmless, or aligned with human values. The technique that has proven most effective at bridging this gap is Reinforcement Learning from Human Feedback (RLHF), and at its heart lies a powerful algorithm: Proximal Policy Optimization (PPO).

      You may have seen diagrams like the one below, which outlines the RLHF training process. It can look intimidating, with a web of interconnected models, losses, and data flows. @@ -25,4 +25,4 @@ where δ_t = r_t + γV(s_{t+1}) - V(s_t)

      \ No newline at end of file +[0841892] \ No newline at end of file diff --git a/posts/quantization-in-llms/index.html b/posts/quantization-in-llms/index.html index 2b13034..b0df88c 100644 --- a/posts/quantization-in-llms/index.html +++ b/posts/quantization-in-llms/index.html @@ -1,10 +1,10 @@ -Quantization in LLMs · Eric X. Liu's Personal Page

      Quantization in LLMs

      9-minute read

      The burgeoning scale of Large Language Models (LLMs) has necessitated a paradigm shift in their deployment, moving beyond full-precision floating-point arithmetic towards lower-precision representations. Quantization, the process of mapping a wide range of continuous values to a smaller, discrete set, has emerged as a critical technique to reduce model size, accelerate inference, and lower energy consumption. This article provides a technical overview of quantization theories, their application in modern LLMs, and highlights the ongoing innovations in this domain.

      The Fundamentals of Quantization

      At its core, quantization seeks to represent model weights and activations using fewer bits. Three primary approaches form the theoretical foundation:

      1. K-Means-based Quantization (Non-uniform): This method clusters floating-point weights into a predefined number of groups. Each weight is then replaced by the centroid of its assigned cluster. While effective for storage compression by storing a small “codebook” of centroids and integer indices, its direct computational benefits during inference are limited unless specialized hardware for lookup tables is employed.

      2. Linear (Affine) Quantization: The most prevalent form, linear quantization maps a floating-point range to a fixed integer range using a simple linear transformation: r = S * (q - Z). Here, r is the real value, q is the quantized integer, S is the scale factor, and Z is the zero-point (offset). This approach directly enables integer arithmetic, which is significantly faster and more energy-efficient on modern hardware.

      3. Binary and Ternary Quantization (Extreme Low-Bit): These push quantization to its limits by constraining weights and/or activations to only two (e.g., +1, -1) or three (e.g., +1, 0, -1) values. While offering maximal compression and enabling bitwise operations instead of multiplications, they often incur substantial accuracy degradation for complex LLMs. For instance, BinaryConnect enabled training deep neural networks with binary weights, showing near state-of-the-art results on image classification tasks. XNOR-Net further extended this by binarizing both weights and inputs, achieving significant speedups and memory savings. Ternary Weight Networks (TWNs) and Trained Ternary Quantization (TTQ) improve upon binary methods by introducing a zero value or learnable scaling factors, respectively, mitigating some accuracy loss.

      Quantization Strategies: Bridging Accuracy and Efficiency

      The practical application of quantization involves distinct strategies:

      1. Post-Training Quantization (PTQ): This approach applies quantization to an already trained, full-precision model without any further training or fine-tuning.

        • Quantization Granularity: The precision of quantization can vary across a model.
          • Per-Tensor Quantization applies a single scale and zero-point to an entire tensor.
          • Per-Channel Quantization assigns unique scale and zero-point parameters to each output channel of a layer, crucial for handling diverse value distributions.
          • Group Quantization provides an intermediate granularity, where scales and zero-points are applied to smaller groups of weights within a channel or layer. This balances fine-grained control with hardware efficiency.
        • Dynamic Range Clipping (Calibration): A critical aspect of PTQ is determining the optimal range (r_min, r_max) for quantization, especially for activations, which often exhibit outliers. Methods include:
          • Min-Max: Simply using the observed minimum and maximum values.
          • Exponential Moving Averages (EMA): Tracking ranges using a smoothed average during a calibration run.
          • Kullback-Leibler (KL) Divergence Minimization: Selecting clipping thresholds that minimize the information loss between the original and quantized distributions.
          • Mean Square Error (MSE) Minimization: Optimizing scale and zero-point parameters to minimize the reconstruction error. Adaptive rounding techniques, such as AdaRound, further refine this by optimizing rounding decisions for individual weights.

      2. Quantization-Aware Training (QAT): This method integrates the quantization process directly into the training or fine-tuning loop. By simulating the effects of low-precision arithmetic during training, the model learns to be robust to quantization noise. The Straight-Through Estimator (STE) is commonly used to approximate gradients for the non-differentiable quantization operations, enabling backpropagation. QAT generally yields higher accuracy than PTQ, particularly for aggressive low-bit quantization.

      Emerging Techniques for Modern LLMs

      The scale and complexity of LLMs necessitate advanced quantization strategies:

      1. One-Shot Post-Training Quantization (e.g., GPTQ, AWQ): These techniques aim to achieve near-QAT accuracy with PTQ’s convenience, requiring only a small, unlabelled calibration dataset and no full retraining. GPTQ quantizes weights layer-by-layer by minimizing output MSE, leveraging Hessian-aware information. AWQ identifies and scales “important” weights based on activation magnitudes before quantization. These methods have been instrumental in enabling 4-bit LLM inference on consumer-grade hardware.

      2. Sparsity-Quantization Hybrid (e.g., SpQR): These approaches combine model pruning (removing redundant connections) with quantization to achieve even greater compression. SpQR prunes weights and then quantizes the remaining non-zero weights, often with special handling for critical outlier weights.

      3. Quantization for Efficient Fine-tuning (e.g., QLoRA): QLoRA quantizes the base LLM weights (e.g., to 4-bit) and freezes them, then fine-tunes only small, low-rank adapter modules in full precision. This drastically reduces the memory requirements for fine-tuning large models on limited hardware.

      4. Hardware-Optimized Quantization Formats: Beyond bit-width, specialized floating-point formats and efficient kernels are being developed. MXFP4 (Microscaling FP4), NVIDIA’s FP8 (E4M3/E5M2), and GGUF’s K-quants are examples of block-wise floating-point formats and hierarchical quantization schemes optimized for high performance on modern accelerators like NVIDIA’s Blackwell GPUs. These formats offer superior dynamic range compared to fixed-point integers at very low bit-widths.

      Multi-Level Scaling in Group Quantization: A Deeper Dive

      Modern group quantization approaches often employ multi-level scaling to achieve an optimal balance between precision and compression. Consider a generalized formula for reconstructing a real value r from a quantized value q:

      r = (q - z) * s_l0 * s_l1 * ...

      where z is the zero-point (often 0 for symmetric quantization), and s_l0, s_l1 are scale factors at different hierarchical levels. The “Effective Bit Width” reflects the average number of bits per weight after accounting for both the quantized value and its associated scales.

      Let’s dissect a representative table of such schemes:

      Quantization ApproachData Type (q)L0 Group SizeL0 Scale Data TypeL1 Group SizeL1 Scale Data TypeEffective Bit Width
      Per-Channel QuantINT4Per ChannelFP16--4
      VSQINT416UINT4Per ChannelFP164 + 4/16 = 4.25
      MX4S1M22E1M016E8M03 + 1/2 + 8/16 = 4
      MX6S1M42E1M016E8M05 + 1/2 + 8/16 = 6
      MX9S1M72E1M016E8M08 + 1/2 + 8/16 = 9

      • Data Types Explanation:

        • INT4: Standard 4-bit integer.
        • UINT4: 4-bit unsigned integer.
        • FP16: 16-bit floating-point number.
        • S1M2: A custom 3-bit floating-point-like format (1 sign bit, 2 mantissa bits), with its exponent effectively derived from shared scales.
        • S1M4: A custom 5-bit format (1 sign bit, 4 mantissa bits).
        • S1M7: A custom 8-bit format (1 sign bit, 7 mantissa bits).
        • E1M0: A custom 1-bit exponent-only floating-point scale (1 exponent bit, 0 mantissa bits).
        • E8M0: A custom 8-bit exponent-only floating-point scale (8 exponent bits, 0 mantissa bits).

      • Row-by-Row Analysis:

        1. Per-Channel Quant: This represents a baseline. Each individual value (q) is stored as a 4-bit integer. A single 16-bit FP16 scale (s_l0) is applied per channel. Since a channel contains many weights, the overhead of the 16-bit scale is amortized, making the effective bit width approximately 4 bits per weight.
        2. VSQ (Per-Vector Scaled Quantization): This scheme introduces a two-level scaling hierarchy. The core quantized value (q) is a 4-bit integer. A finer-grained 4-bit unsigned integer scale (s_l0 in UINT4) is applied to groups of 16 quantized values. A coarser 16-bit FP16 scale (s_l1) is applied per channel. The effective bit width is calculated as: (4 bits for q) + (4 bits for s_l0 / 16 elements) = 4 + 0.25 = 4.25 bits/weight. The FP16 s_l1 scale overhead per channel is negligible, hence not included in the fraction.
        3. MX4 (Mixed-Precision with Microexponents, 4-bit effective): This is a key example of specialized floating-point quantization. The base quantized value (q) uses a compact 3-bit S1M2 format. A 1-bit E1M0 scale (s_l0) is applied to very small groups of 2 q values. A coarser 8-bit E8M0 scale (s_l1) is applied to groups of 16 q values. The effective bit width is: (3 bits for q) + (1 bit for s_l0 / 2 elements) + (8 bits for s_l1 / 16 elements) = 3 + 0.5 + 0.5 = 4 bits/weight. This allows for a wider dynamic range, typical of floating-point numbers, while maintaining a very low average bit-width.
        4. MX6: Similar to MX4, but uses a 5-bit S1M4 format for q. The effective bit width becomes: 5 + 0.5 + 0.5 = 6 bits/weight, offering higher precision at the cost of slight increase in size.
        5. MX9: Uses an 8-bit S1M7 format for q. The effective bit width is: 8 + 0.5 + 0.5 = 9 bits/weight, providing near-INT8 precision while retaining the floating-point-like dynamic range benefits.

      These multi-level, mixed-precision, floating-point quantization schemes represent a significant advancement, enabling LLMs to run efficiently on diverse hardware while maintaining high accuracy, especially for managing the ubiquitous outlier values in LLM activations and weights.

      Current Trends and Future Outlook

      The field of LLM quantization is characterized by rapid innovation.

      • Linear (Affine) Quantization remains the foundational principle, with most advancements focusing on refining its application.
      • Per-channel and especially Group/Block-wise Quantization are indispensable for LLMs due to their heterogeneous weight distributions.
      • Post-Training Quantization (PTQ), particularly advanced one-shot methods like GPTQ and AWQ, are highly relevant for efficient deployment of LLMs without the extensive resources required for QAT.
      • Quantization-Aware Training (QAT) is the benchmark for achieving peak accuracy at very low bit-widths, particularly when PTQ falls short.
      • Mixed-Precision Quantization is crucial for balancing accuracy and efficiency across the massive, varying layers of LLMs.
      • Hardware-optimized quantization formats (like MXFP4, FP8) represent a significant step towards co-designing models and silicon for maximum performance.

      Conversely, methods like pure K-means quantization (where computation requires fetching float centroids) and general-purpose pure binary/ternary quantization are less commonly adopted as primary strategies for high-accuracy LLM inference, primarily due to the greater accuracy challenges and lack of widespread hardware acceleration for these specific paradigms compared to optimized integer or block-floating-point operations. The trajectory indicates a continuous push for lower effective bit-widths, driven by clever scaling strategies, specialized data formats, and a hardware-aware approach to model optimization.

      References

      Courbariaux, M., Bengio, Y., & David, J. P. (2015). BinaryConnect: Training Deep Neural Networks with Binary Weights during Propagations. NeurIPS Proceedings.

      Dai, S., Venkatesan, R., Ren, H., Zimmer, B., Dally, W. J., & Khailany, B. (2021). VS-Quant: Per-vector Scaled Quantization for Accurate Low-Precision Neural Network Inference. arXiv preprint arXiv:2102.04503.

      Rastegari, M., Ordonez, V., Redmon, J., & Farhadi, A. (2016). XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks. European Conference on Computer Vision (ECCV).

      Zhu, C., Han, S., Mao, H., & Dally, W. J. (2017). Trained Ternary Quantization. International Conference on Learning Representations (ICLR).

      Migacz, S. (2017). 8-bit Inference with TensorRT. NVIDIA GTC Presentation.

      Krishnamoorthi, R. (2018). Quantizing Deep Convolutional Networks for Efficient Inference: A Whitepaper. arXiv preprint arXiv:1806.08342.

      Li, F., Liu, B., Wang, X., Zhang, B., & Yan, J. (2016). Ternary Weight Networks. arXiv preprint arXiv:1605.04711.

      Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A., Adam, H., & Kalenichenko, D. (2018). Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

      Nagel, M., van Baalen, T., Blankevoort, T., & Louizos, C. (2019). Data-Free Quantization Through Weight Equalization and Bias Correction. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV).

      Han, S., Mao, H

      \ No newline at end of file +[0841892] \ No newline at end of file diff --git a/posts/reverse-engineering-antigravity-ide/index.html b/posts/reverse-engineering-antigravity-ide/index.html index d57cc8f..41067ef 100644 --- a/posts/reverse-engineering-antigravity-ide/index.html +++ b/posts/reverse-engineering-antigravity-ide/index.html @@ -1,9 +1,9 @@ How I Built a Blog Agent that Writes About Itself · Eric X. Liu's Personal Page

      How I Built a Blog Agent that Writes About Itself

      4-minute read

      I’ve been spending a lot of time “vibe coding” in the Antigravity IDE lately. It’s an incredible flow state—intense, iterative, and fast. But it has a major flaw: the context is ephemeral. Once the session is over, that rich history of decisions, wrong turns, and “aha!” moments is locked away in an opaque, internal format.

      I wanted to capture that value. I wanted a system that could take my chaotic coding sessions and distill them into structured, technical blog posts (like the one you’re reading right now).

      But getting the data out turned into a much deeper rabbit hole than I expected.

      The Challenge: Check the Database? @@ -24,4 +24,4 @@ I wanted to capture that value. I wanted a system that could take my chaotic cod 2016 - 2026 Eric X. Liu -[8aa06e9]

      \ No newline at end of file +[0841892] \ No newline at end of file diff --git a/posts/rooting-pixel-2-xl-for-reverse-engineering/index.html b/posts/rooting-pixel-2-xl-for-reverse-engineering/index.html index 3f70482..07a40cd 100644 --- a/posts/rooting-pixel-2-xl-for-reverse-engineering/index.html +++ b/posts/rooting-pixel-2-xl-for-reverse-engineering/index.html @@ -1,9 +1,9 @@ Why I Downgraded Magisk to Root My Pixel 2 XL · Eric X. Liu's Personal Page

      Why I Downgraded Magisk to Root My Pixel 2 XL

      4-minute read

      For the past few weeks, I’ve been stuck in a stalemate with my EcoFlow Bluetooth Protocol Reverse Engineering Project. I have the hci snoop logs, I have the decompiled APK, and I have a strong suspicion about where the authentication logic is hiding. But suspicion isn’t proof.

      Static analysis has its limits. I found the “smoking gun” function—a native method responsible for encrypting the login payload—but understanding how it constructs that payload within a strict 13-byte limit purely from assembly (ARM64) was proving to be a headache.

      I needed to move from static analysis to dynamic analysis. I needed to hook the function at runtime, inspect the memory, and see the data before it gets encrypted. To do that, I needed a rooted Android device.

      The only candidate in my drawer? An 8-year-old Google Pixel 2 XL (“taimen”) that hadn’t been turned on since 2017.

      The Objective @@ -35,4 +35,4 @@ I used Magisk v30.6 (the latest as of writing). The patch proce 2016 - 2026 Eric X. Liu -[8aa06e9]

      \ No newline at end of file +[0841892] \ No newline at end of file diff --git a/posts/secure-boot-dkms-and-mok-on-proxmox-debian/index.html b/posts/secure-boot-dkms-and-mok-on-proxmox-debian/index.html index e47855a..c03a0d7 100644 --- a/posts/secure-boot-dkms-and-mok-on-proxmox-debian/index.html +++ b/posts/secure-boot-dkms-and-mok-on-proxmox-debian/index.html @@ -5,9 +5,9 @@ modprobe nvidia → “Key was rejected by service” That message is the tell: Secure Boot is enabled and the kernel refuses to load modules not signed by a trusted key.">

      Fixing GPU Operator Pods Stuck in Init: Secure Boot, DKMS, and MOK on Proxmox + Debian

      3-minute read

      I hit an issue where all GPU Operator pods on one node were stuck in Init after migrating from Legacy BIOS to UEFI. The common error was NVIDIA components waiting for “toolkit-ready,” while the toolkit init container looped with:

      • nvidia-smi failed to communicate with the NVIDIA driver
      • modprobe nvidia → “Key was rejected by service”

      That message is the tell: Secure Boot is enabled and the kernel refuses to load modules not signed by a trusted key.

      Environment @@ -59,4 +59,4 @@ nvidia-smi failed to communicate with the NVIDIA driver modprobe nvidia → “K 2016 - 2026 Eric X. Liu -[8aa06e9]

      \ No newline at end of file +[0841892] \ No newline at end of file diff --git a/posts/supabase-deep-dive/index.html b/posts/supabase-deep-dive/index.html index 0e8c59a..fa44d0c 100644 --- a/posts/supabase-deep-dive/index.html +++ b/posts/supabase-deep-dive/index.html @@ -1,9 +1,9 @@ Supabase Deep Dive: It's Not Magic, It's Just Postgres · Eric X. Liu's Personal Page

      Supabase Deep Dive: It's Not Magic, It's Just Postgres

      8-minute read

      In the world of Backend-as-a-Service (BaaS), platforms are often treated as magic boxes. You push data in, you get data out, and you hope the magic inside scales. While this simplicity is powerful, it can obscure the underlying mechanics, leaving developers wondering what’s really going on.

      Supabase enters this space with a radically different philosophy: transparency. It provides the convenience of a BaaS, but it’s built on the world’s most trusted relational database: PostgreSQL. The “magic” isn’t a proprietary black box; it’s a carefully assembled suite of open-source tools that enhance Postgres, not hide it.

      This deep dive will deconstruct that suite. We will move beyond the basics to explore the architectural patterns, security models, and development workflows that allow you to build robust, scalable applications. We will cover:

      • The Supabase Blueprint: A procedural guide to designing your application.
      • The Pillars of Supabase: A detailed look at Auth, Storage, Functions, and Realtime.
      • Transactional Realtime: How Supabase guarantees data consistency in a live environment.
      • Best Practices: The practical knowledge you need before writing a single line of code.

      The Guiding Philosophy: Your Database as the Source of Truth @@ -90,4 +90,4 @@ Supabase enters this space with a radically different philosophy: transparency. 2016 - 2026 Eric X. Liu -[8aa06e9]

      \ No newline at end of file +[0841892] \ No newline at end of file diff --git a/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/index.html b/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/index.html index cbdaa82..c197a8e 100644 --- a/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/index.html +++ b/posts/t5-the-transformer-that-zigged-when-others-zagged-an-architectural-deep-dive/index.html @@ -1,9 +1,9 @@ An Architectural Deep Dive of T5 · Eric X. Liu's Personal Page

      An Architectural Deep Dive of T5

      6-minute read

      In the rapidly evolving landscape of Large Language Models, a few key architectures define the dominant paradigms. Today, the “decoder-only” model, popularized by the GPT series and its successors like LLaMA and Mistral, reigns supreme. These models are scaled to incredible sizes and excel at in-context learning.

      But to truly understand the field, we must look at the pivotal models that explored different paths. Google’s T5, or Text-to-Text Transfer Transformer, stands out as one of the most influential. It didn’t just introduce a new model; it proposed a new philosophy. This article dives deep into the architecture of T5, how it fundamentally differs from modern LLMs, and the lasting legacy of its unique design choices.

      The Core Philosophy: Everything is a Text-to-Text Problem @@ -30,4 +30,4 @@ But to truly understand the field, we must look at the pivotal models that explo 2016 - 2026 Eric X. Liu -[8aa06e9]

      \ No newline at end of file +[0841892] \ No newline at end of file diff --git a/posts/technical-deep-dive-llm-categorization/index.html b/posts/technical-deep-dive-llm-categorization/index.html index eed2682..5b527c0 100644 --- a/posts/technical-deep-dive-llm-categorization/index.html +++ b/posts/technical-deep-dive-llm-categorization/index.html @@ -1,9 +1,9 @@ From Gemini-3-Flash to T5-Gemma-2: A Journey in Distilling a Family Finance LLM · Eric X. Liu's Personal Page

      From Gemini-3-Flash to T5-Gemma-2: A Journey in Distilling a Family Finance LLM

      7-minute read

      Running a family finance system is surprisingly complex. What starts as a simple spreadsheet often evolves into a web of rules, exceptions, and “wait, was this dinner or vacation dinner?” questions.

      For years, I relied on a rule-based system to categorize our credit card transactions. It worked… mostly. But maintaining if "UBER" in description and amount > 50 style rules is a never-ending battle against the entropy of merchant names and changing habits.

      Recently, I decided to modernize this stack using Large Language Models (LLMs). This post details the technical journey from using an off-the-shelf commercial model to distilling that knowledge into a small, efficient local model (google/t5gemma-2-270m) that runs on my own hardware while maintaining high accuracy.

      Phase 1: The Proof of Concept with Commercial LLMs @@ -73,4 +73,4 @@ It turned out to be a syntax error in my arguments passed to the Trainer[8aa06e9]

      \ No newline at end of file +[0841892] \ No newline at end of file diff --git a/posts/the-convergence-of-fast-weights-linear-attention-and-state-space-models/index.html b/posts/the-convergence-of-fast-weights-linear-attention-and-state-space-models/index.html index a6b6081..234c853 100644 --- a/posts/the-convergence-of-fast-weights-linear-attention-and-state-space-models/index.html +++ b/posts/the-convergence-of-fast-weights-linear-attention-and-state-space-models/index.html @@ -1,9 +1,9 @@ The Convergence of Fast Weights, Linear Attention, and State Space Models · Eric X. Liu's Personal Page

      The Convergence of Fast Weights, Linear Attention, and State Space Models

      5-minute read

      Modern Large Language Models (LLMs) are dominated by the Transformer architecture. However, as context windows grow, the computational cost of the Transformer’s attention mechanism has become a primary bottleneck. Recent discussions in the AI community—most notably by Geoffrey Hinton—have highlighted a theoretical link between biological memory mechanisms (“Fast Weights”) and efficient engineering solutions like Linear Transformers and State Space Models (SSMs).

      This article explores the mathematical equivalence between Hinton’s concept of Fast Weights as Associative Memory and the recurrence mechanisms found in models such as Mamba and RWKV.

      1. The Standard Transformer Bottleneck @@ -26,4 +26,4 @@ This article explores the mathematical equivalence between Hinton’s concept of 2016 - 2026 Eric X. Liu -[8aa06e9]

      \ No newline at end of file +[0841892] \ No newline at end of file diff --git a/posts/transformer-s-core-mechanics/index.html b/posts/transformer-s-core-mechanics/index.html index 4d30c50..bde94ab 100644 --- a/posts/transformer-s-core-mechanics/index.html +++ b/posts/transformer-s-core-mechanics/index.html @@ -8,9 +8,9 @@ In deep learning, a “channel” can be thought of as a feature dimension. While this term is common in Convolutional Neural Networks for images (e.g., Red, Green, Blue channels), in LLMs, the analogous concept is the model’s primary embedding dimension, commonly referred to as d_model.">

      Transformer's Core Mechanics

      7-minute read

      The Transformer architecture is the bedrock of modern Large Language Models (LLMs). While its high-level success is widely known, a deeper understanding requires dissecting its core components. This article provides a detailed, technical breakdown of the fundamental concepts within a Transformer block, from the notion of “channels” to the intricate workings of the attention mechanism and its relationship with other advanced architectures like Mixture of Experts.

      1. The “Channel”: A Foundational View of d_model @@ -36,4 +36,4 @@ In deep learning, a “channel” can be thought of as a feature dimensi 2016 - 2026 Eric X. Liu -[8aa06e9]

      \ No newline at end of file +[0841892] \ No newline at end of file diff --git a/posts/unifi-vlan-migration-to-zone-based-architecture/index.html b/posts/unifi-vlan-migration-to-zone-based-architecture/index.html index fead548..323aa6c 100644 --- a/posts/unifi-vlan-migration-to-zone-based-architecture/index.html +++ b/posts/unifi-vlan-migration-to-zone-based-architecture/index.html @@ -1,9 +1,9 @@ UniFi VLAN Migration to Zone-Based Architecture · Eric X. Liu's Personal Page

      UniFi VLAN Migration to Zone-Based Architecture

      5-minute read

      Embarking on a network migration to a properly segmented VLAN architecture is a rite of passage for any serious home lab or small business operator. The goal is clear: improve security and organization by separating traffic. However, the path from a flat network to a segmented one is often paved with subtle but critical configuration details that can lead to hours of frustrating troubleshooting.

      This article documents that journey. It details the pitfalls encountered, the core networking concepts that were essential to understand, and the best practices that ultimately led to a stable, secure, and logical network design built on a zone-based firewall model.

      Lesson 1: Demystifying the Native VLAN @@ -28,4 +28,4 @@ This article documents that journey. It details the pitfalls encountered, the co 2016 - 2026 Eric X. Liu -[8aa06e9]

      \ No newline at end of file +[0841892] \ No newline at end of file diff --git a/posts/useful/index.html b/posts/useful/index.html index 2fd0813..10e451b 100644 --- a/posts/useful/index.html +++ b/posts/useful/index.html @@ -1,12 +1,12 @@ Some useful files · Eric X. Liu's Personal Page

      Some useful files

      One-minute read
      \ No newline at end of file +[0841892] \ No newline at end of file diff --git a/posts/vattention/index.html b/posts/vattention/index.html index 0acad0b..0e84468 100644 --- a/posts/vattention/index.html +++ b/posts/vattention/index.html @@ -8,9 +8,9 @@ Prior to PagedAttention, systems allocated contiguous memory for the maximum possible context length, leading to severe fragmentation and wasted memory. PagedAttention addressed this by chunking the KV cache into non-contiguous blocks, managed by a software-defined “page table” (the Block Table) [1].">

      vAttention

      4-minute read

      Large Language Model (LLM) inference is memory-bound, primarily due to the Key-Value (KV) cache—a store of intermediate state that grows linearly with sequence length. Efficient management of this memory is critical for throughput. While PagedAttention (popularized by vLLM) became the industry standard by solving memory fragmentation via software, recent research suggests that leveraging the GPU’s native hardware Memory Management Unit (MMU) offers a more performant and portable solution.

      The Status Quo: PagedAttention and Software Tables @@ -31,4 +31,4 @@ The GPU TLB hierarchy is sensitive to page sizes.

      • 4KB Pages:< 2016 - 2026 Eric X. Liu -[8aa06e9]

      \ No newline at end of file +[0841892] \ No newline at end of file diff --git a/posts/vibe-coding-from-the-jeep/index.html b/posts/vibe-coding-from-the-jeep/index.html index c79230d..0204ca9 100644 --- a/posts/vibe-coding-from-the-jeep/index.html +++ b/posts/vibe-coding-from-the-jeep/index.html @@ -1,9 +1,9 @@ Hacking a Chinese Car Stereo to fulfill my Knight Rider dreams · Eric X. Liu's Personal Page

      Hacking a Chinese Car Stereo to fulfill my Knight Rider dreams

      4-minute read

      “Vibe coding” has become my latest obsession. It’s that flow state where the tools disappear, and you’re just manipulating logic at the speed of thought. Usually, this happens in a high-end IDE like Antigravity. But lately, I’ve been trying to answer a childhood dream.

      Growing up in China before the internet age, my window to the outside world was CCTV-6. Along with Baywatch, one of the first American TV shows I ever watched was Knight Rider. I don’t remember the exact plot lines, but the core concept stuck with me forever: KITT. A car that could talk, think, and do things for you.

      Decades later, I’m sitting in my Jeep, wondering: Can I build my own KITT? Can I take the vibe on the road?

      I already updated the head unit in my Jeep to an aftermarket unit. It features a K706 (UIS7862S) chipset with an 8-core CPU and 8GB of RAM, essentially making it a reasonably powerful Android tablet hardwired into the dashboard.

      The Objective @@ -32,4 +32,4 @@ Growing up in China before the internet age, my window to the outside world was 2016 - 2026 Eric X. Liu -[8aa06e9]

      \ No newline at end of file +[0841892] \ No newline at end of file diff --git a/series/index.html b/series/index.html index 890e098..e765018 100644 --- a/series/index.html +++ b/series/index.html @@ -1,7 +1,7 @@ -Series · Eric X. Liu's Personal Page

      Series

        \ No newline at end of file +[0841892] \ No newline at end of file diff --git a/tags/index.html b/tags/index.html index 9eddcc5..e9a7f34 100644 --- a/tags/index.html +++ b/tags/index.html @@ -1,7 +1,7 @@ -Tags · Eric X. Liu's Personal Page

        Tags

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