Squeeze llm. This can be solved by fine-tuning.

Some models apply normalization or subsequent process to the last hidden state when it’s returned. py", line 522, in pydantic. import time import torch import torch. If someone were to tell you that eventually future versions of GPUs would be used as high-performance tools for HPC Jun 13, 2023 · To address this, we introduce SqueezeLLM, a post-training quantization framework that not only enables lossless compression to ultra-low precisions of up to 3-bit, but also achieves higher quantization performance under the same memory constraint. These models contain an extensive number of parameters and are trained on vast text datasets. DataFrame. I’m moving, among other things, so it’s been a long week. May 8, 2019 · In this story, Squeeze-and-Excitation Network (SENet), by University of Oxford, is reviewed. Our framework incorporates two novel ideas: (i) sensitivity-based non-uniform quantization, which searches for the optimal bit precision assignment based on second-order information; and (ii) the Dense-and-Sparse decomposition that stores outliers and sensitive weight values in an efficient sparse format. You should look at tensor's shape attribute to see it easily. Reload to refresh your session. 2), which retains both sensitive values and outlier values as full-precision sparse format. @article{lee2024llm2llm, title={LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement}, author={Lee, Nicholas and Wattanawong, Thanakul and Kim, Sehoon and Mangalam, Karttikeya and Shen, Sheng and Anumanchipali, Gopala and Mahoney, Michael W and Keutzer, Kurt and Gholami, Amir}, journel={arXiv}, year={2024}, } MemGPT - Create LLM agents with long-term memory and custom tools 📚🦙 Pretrained-Language-Model - Pretrained language model and its related optimization techniques developed by Huawei Noah's Ark Lab. 2 has the following changes compared to Mistral-7B-v0. SENets introduced a key architectural unit — Squeeze-and-Excitation Block (SE Block) which was crucial to the gains in performance. SqueezeLLM: 200/200 [24:14<00:00, 7. LLM optimization: You need to optimize the LLM when 1) the model is producing inconsistent results with incorrect formatting, 2) the tone or style of speech is not correct, or 3) the reasoning is not being followed consistently. This can be solved by fine-tuning. A pre-trained LLM is trained more generally and wouldn't be able to provide the best answers for domain specific questions and understand the medical terms and acronyms. parse_obj. 14 requests/s, 47. Research will likely be mentioned throughout July 11 County of Door Broadband Committee assembly There are greater than 9,570 seasonal models in Door County in Saved searches Use saved searches to filter your results more quickly SqueezeLLM: Dense-and-Sparse Quantization. Jun 13, 2023 · To address this, we introduce SqueezeLLM, a post-training quantization framework that not only enables lossless compression to ultra-low precisions of up to 3-bit, but also achieves higher quantization performance under the same memory constraint. In your last case it would be: import torch. during the summer months. I had very low temperature value along with other parameters such as top_k and top_p which made the next token distribution too steep and as the beam search's logic, you will need to have multiple tokens available, and in the low temperature case I couldn't have (because we know how temperature works May 20, 2023 · Task-agnostic compression: The compressed LLM should retain its original ability as a multi-task solver. Next on our list of low-powered LLMs is the Hercules-Mini 1. Otherwise the object is unchanged. model_parse import ( parse_model, get_layers, get_embedding, get_norm, ) def get_model (model): import torch def skip (*args, **kwargs): pass torch. Jul 10, 2024 · Research Appears to be like at Financial Good thing about Improved Web Entry for Seasonal Residents – Cyber Tech. We find you the best rates on insurance for your auto and home and more. Reducing only the precision of the weights (and not the activations) is sufficient to obtain significant latency reductions. Jun 16, 2023 · SqueezeLLM is a post-training quantization framework that incorporates a new method called Dense-and-Sparse Quantization to enable efficient LLM serving. The JLL report also lists the critical changes needed across the globe to address increased power usage. Using the demo as a starting point, it should be easy to stand up a voice-driven LLM app on any cloud provider. Paper tables with annotated results for SqueezeLLM: Dense-and-Sparse Quantization DeepSpeed Inference helps you serve transformer-based models more efficiently when: (a) The model fits on a GPU, and (b) The model’s kernels are supported by the DeepSpeed library. It is the process of translating natural language (text input) to an Professor Kurt Keutzer's research group at Berkeley AI Research, focusing on Efficient Model Design Jun 20, 2023 · Recent advancements in Large Language Models (LLMs) have demonstrated their remarkable problem-solving capabilities in various fields. Let's say you run a diabetes support community and want to set up an online helpline to answer questions. import argparse import asyncio import json import os import time import shutil import numpy as np from configs. May 22, 2023 · Large language models (LLMs) have revolutionized the field of AI, demonstrating unprecedented capacity across various tasks. With “Squeeze-and-Excitation” (SE) block that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels, SENet is constructed. Here for instance outputs. Each channel is "squeezed" into a single numeric value using average pooling. 254 lines (225 loc) · 8. 2 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-7B-v0. The paper demonstrates the effectiveness of May 9, 2024 · May 9, 2024. Contribute to SyphonArch/SqueezeLLM-for-Any-Precision development by creating an account on GitHub. llama. After installation is completed, open the Start menu, search for Anaconda Prompt, run it as administrator, and create a virtual environment using the following commands. hotpotqa. conda activate llm. Enter each command separately: conda create -n llm python=3. However, deploying these models for inference has been a significant challenge due to their unprecedented resource requirements. The Squeeze-and-Excitation Block is an architectural unit designed to improve the representational power of a network by enabling it to perform dynamic channel-wise feature recalibration. You switched accounts on another tab or window. M. (noun) A large language model that can create text, images and code that mimic Apr 18, 2024 · 3. The custom dataset class takes care of tokenizing the text, padding Jun 7, 2024 · The paper presents a novel technique called "SqueezeLLM" for compressing large language models (LLMs) using a combination of dense and sparse quantization. 6. 4mo Edited. For example, we ask LLM to generate the format specification, which intends to clarify the input format pattern by LLM itself. squeeze(1) RuntimeError: probability tensor contains either inf , nan or element < 0 The text was updated successfully, but these errors were encountered: You signed in with another tab or window. This is your go-to solution if latency is your main concern. 1), where quantization bins are allocated closer to sensitive values, and (ii) the Dense-and-Sparse decomposition (Sec. The fresh tones and elegant and modern design of the watering can set, including spray bottle and squeeze bottle, can meet the different watering needs of your various plants, Choose this watering can indoor plants set, and you can enjoy your peaceful and pleasant gardening life calmly. Remember when a GPU was a small fan-less video card with names like Voodoo, Matrox, Nvidia, or ATI? This simple addition gave your PC a new world of responsive 2D and 3D graphics. Using tokenizers, or tokenization, is the first and fundamental step in the NLP pipeline. 96 tokens/s. SqueezeAI is part of Berkeley AI Research Lab at UC Berkeley focused on AI Systems research. Task: Brush teeth Step 1: Go to bathroom GPT -21. DataFrames with a single column or a single row are squeezed to a Series. The eye-watering cost of LLM inference Jan 15, 2024 · The transition to an LL. nn. If you set --KV_class3 to other number, SqueezeAttention will compute the KV Budget of remaining layers to ensure that the total KV Budget of all layers before and after change is equal. Jun 19, 2023 · 🌡 Have you tried increasing the temperature? Well try increasing the temperature value. (NLP-OSS @ EMNLP 2023) Oct 5, 2023 · Squeeze every bit of latency you can out of your data flow (because users don’t like to wait) The demo is built on top of our daily-python SDK . 53 KB. I had to do a few custom components and patterns, let me know if they are missing or if you cannot use the free version, I'll try to do some export to other PCB formats if needed You can order the PCB directly from PCBway here There is an excel file with is the BOM extract from diptrace and another one that points to Fidget Worm Toy,Worm Big Fidget Toys Adults and Kids, Funny Stretchy Sensory Stress Toys, Fidget Sensory Squeeze Toys, Relieves Stress and Anxiety Finger Toys for Kids with Autism ADHD-Rainbow 3. LLMs can include hundreds of billions of parameters and are trained on enormous text corpora. (You can make it longer) By identifying and eliminating redundant transformer blocks for efficient LLMs, we achieve outstanding accuracy, latency, and throughput results in the LLM models. nn as nn from squeezellm. Generative Large Language Models (LLMs) have demonstrated remarkable results for a wide range of tasks. 8B. SqueezeAILab. History. arXiv preprint arXiv:2306. multinomial(probs, num_samples=1). It’ s a great article and I Dec 24, 2023 · Year in a word: LLM. So this week noted cryptic skeptic Molly White has a new essay out titled “AI isn't useless. Jun 8, 2023 · Prompting Techniques That Squeeze the Best Out of Your LLM. Running eval. (Right) Weight distributions after 3-bit quantization using uniform and sensitivity-based non-uniform quantization. Less training corpus: In this work, we use only 50k publicly available samples (alpaca) to post-train the LLM. text-generation-inference. The process is: The block has a convolutional block as an input. This method is most useful when you don’t know if your object is a Series or Jun 13, 2023 · Figure 3: (Left) The weight distribution of one output channel in LLaMA-7B. 7 out of 5 stars 7 . I am testing using vllm benchmark with 200 requests about 1300tk with 90tk return and a 4090 (in WSL). ValueError: dictionary update sequence element #0 has length 1; 2 is required. This layer tries to use a content aware mechanism to assign channel-wise weights adaptively. TLDR: Deploying LLMs is difficult due to their large memory size. " Features. I compared the inference throughput between using just CPU, versus using GPU with CPU offloading from ZeRO-Inference, using a synthetic dataset. Mistral-7B-v0. It first squeezes the feature maps into a single value using global average pooling, which are then fed into two Conv1D layers, which act like fully This axis maximizes response accuracy. Its objective is to specify the tables and For instance, the Squeeze variant of the Vicuna models can be served within 6 GB of memory and reach 2% higher MMLU than the baseline model in FP16 with an even 2x larger memory footprint. This can be addressed with reduced precision quantization. [1] Text is converted to numerical representations called tokens, and each token is converted into a vector via looking up from a word embedding table. Our approach begins by tapping into the potential of LLMs to accurately perceive and Dec 30, 2023 · In addition, vLLM curates performant CUDA-kernels (e. 3x speedup. In the latter case, the quantized values are more clustered around the sensitive values. Jun 13, 2023 · SpQR: A sparse-quantized representation for near-lossless LLM weight compression. The table compares the FP16 baseline, non-grouped and grouped GPTQ with activation ordering, and SqueezeLLM with different sparsity levels. From the simplest to the most advanced, instruct your GPT for the best generation. However, the inference process for LLMs comes with significant computational costs. But is it worth it?”. For more details please check out our paper. 100 followers. As usual, we will have a ticket pre-sale available for our mailing list members which will take place at 10 Jun 13, 2023 · Figure 1: (Left) SqueezeLLM incorporates two key approaches: (i) sensitivity-based non-uniform quantization (Sec. - "SqueezeLLM: Dense-and-Sparse Quantization" In generative LLM inference, loading weight matrices into memory is the primary bottleneck, while the cost of dequantization and computation in the FP16 domain is relatively insignificant. - "SqueezeLLM: Dense-and-Sparse Quantization" Jan 23, 2023 · Dataset Fetch and Pre-Processing. Our framework incorporates two novel ideas: (i) sensitivity-based non-uniform quantization, which Feb 21, 2024 · This guide will walk you through the process step by step, from setting up your environment to fine-tuning the model for your specific task. There is a natural path from the simplest most crude to the most advanced fine-tuning of the model. A transformer is a deep learning architecture developed by Google and based on the multi-head attention mechanism, proposed in a 2017 paper "Attention Is All You Need". Squeeze are a British rock band active from 1974 to 1982, from 1985 to 1999, and from 2007 to the present date. Studies reveal that memory bandwidth, rather than CPU, is the primary bottleneck for generative tasks in LLM inference. Memory bandwidth, not CPU power, is the primary performance limitation for LLM inference. tensor = torch. 4. modelutils import * from squeezellm. Clocking in at a mere 1. Whether you’re a seasoned machine learning practitioner or a newcomer to the field, this beginner-friendly tutorial will help you harness the power of Gemma for your projects. Updates: Vicuna-7B and 13B, and LLaMA-30B are all supported with both 3-bit and 4-bit. The simple workflow makes quantizing any pretrained LLM straightforward. py and check the result. Jun 18, 2023 · Recent developments in Large Language Models (LLMs) have demonstrated their impressive problem-solving ability across several fields. loss is the loss computed by the model, and outputs. Abstract: This axis maximizes response accuracy. This has forced existing deployment frameworks to use multi-GPU Table 3: Latency (s) and peak memory usage (GB) of 3-bit LLaMA when generating 128 tokens on an A6000 GPU. 03078, 2023. Squeeze makes it easier to save money on your monthly bills. How the US is putting the squeeze on the Sinaloa Cartel LLM, LLM, MSS, BS Criminal Defense Attorney - Doctor of Forensic Psychology former National Director of Trend Analysis for US CBP (DHS) Apr 4, 2020 · The Squeeze-and-Excitation (SE) block is intended to improve the quality of a convolutional neural network’s representations. llm_scoring_module_key = llm_scoring_module_key # Useful filter to avoid computing score of each candidate when using additional heads directly if llm_scoring_module_key == "score" : Jun 12, 2023 · We begin by loading the pre-trained LLM model and tokenizer. SqueezeAndExcite2D class. July 13, 2023. MLC. Our experts help you to determine your needs, and then ensure you get the most value. You signed out in another tab or window. May 4, 2023 · next_tokens = torch. Feb 19, 2024 · GGUF is the new version of GGML. This axis maximizes consistency of behavior. DeepSpeed MII is a library that quickly sets up a GRPC endpoint for the inference model, with the I'm using diptrace for schematics and routing which is free up to 300 pins. Quantization, storing model parameters with reduced accuracy, offers a promising solution. For given input, you want the model to correctly generate output . program can be challenging, but using your summer effectively can set a strong foundation for academic and personal success. , gpt-4, claude-3, etc) — check with the Bleeding-edge engines. The above exception was the direct cause of the following exception: We know that saving money drives a lot of decisions, but we also know that there is more to it than that. Self-Rewarding Language Models Weizhe Yuan 1, 2Richard Yuanzhe Pang Kyunghyun Cho Xian Li 1Sainbayar Sukhbaatar Jing Xu 1Jason Weston,2 1 Meta 2 NYU Abstract run_llm_compiler. Performance is atrocious. Here, we provide a parameter where SqueezeAttention can significantly improve score. SE Blocks can also be easily added Squeeze discography. kani - kani (カニ) is a highly hackable microframework for chat-based language models with tool use/function calling. Results were obtained using a roofline-based performance model for an A5000 GPU. quant import * from squeezellm. 825 GiB diverse, open source language modelling data set. 2 Explain-Squeeze Schema Linking For a large database, it is impractical to prompt all the table descriptions into the LLM and generate a response to the query directly due to the limited tokens. Implements Squeeze and Excite block as in Squeeze-and-Excitation Networks . attentions is None. A global community for prospective LLM students, and a directory of over 700 law schools and Apple is reportedly investing big in AI for 2024 and beyond, and has found a way to squeeze LLM (large language models) chatbots like ChatGPT onto a device instead of having to rely on the cloud Nov 24, 2023 · We’re excited to announce that in 2024 as Squeeze celebrate their 50th anniversary, the band will head out on an extensive UK tour to celebrate in October and November! Tickets will go on general sale next Friday, 1 December at 10:00AM. 5B Task: Brush teeth Step 1: Walk to bathroom Step 2: Walk to sink Step 3: Find They call me Big body Squeeze😬😬 🤟🏾🐶🌴👉🏾🕉️ #island #southside #800 #squeeze #squeezo #islandboy #explorepage #815 #ibsqeeezo #LLM #Badlemon #lemonsqueeze #rockford Clip Lost my self chasing you 🌐🛬🏝️ #island #southside #800 #squeeze #squeezo #islandboy #explorepage #815 #ibsqeeezo #LLM #Badlemon # Mar 30, 2024 · Here is a set of guidelines to help you squeeze out the best performance from your models: Benchmark with the best available LLM/Chat Model (e. Thus, by quantizing just the weights to lower precision, while leaving the activations in full precision, we can attain significant speedup, in addition to SqueezeLLM is a post-training quantization framework that incorporates a new method called Dense-and-Sparse Quantization to enable efficient LLM serving. Note the throughput results are highly parallelized, and the throughput on a single request would be different. flashattention, gptq+awq+squeeze llm quantized kernels), builds some of their own CUDA-kernels (e. For any layer of a convolutional neural network, we can build a corresponding SE block that recalibrates the feature maps: To address this, we introduce SqueezeLLM, a post-training quantization framework that not only enables lossless compression to ultra-low precisions of up to 3-bit, but also achieves higher quantization performance under the same memory constraint. Blame. For comparison, we include bitwidth and perpelxity on the C4 benchmark. conda install libuv. But a naive method hurts performance. 10. This indicates that the rate at which parameters Apr 22, 2023 · Let’s now return to the original task that got me down this rabbit hole: getting an LLM to perform well on my limited hardware. 2. tensor([1, 0, 2, 3, 4]) RayLLM - LLMs on Ray. 27s/it] Throughput: 0. July 10, 2024. We design several ways to squeeze knowledge from LLM (see Table 4). configs import CONFIGS as HOTPOTQA_CONFIGS from configs. LLMs can incl We would like to show you a description here but the site won’t allow us. 3. GGML is the C++ replica of LLM library and it supports multiple LLM like LLaMA series & Falcon etc. A dense layer followed by a ReLU adds non-linearity and output Based on the insight that the memory, rather than the compute, is the primary bottleneck in LLM inference with generative tasks, we intro-duce SqueezeLLM, a post-training quantization framework with a novel sensitivity-based non-uniform quantization and Dense-and-Sparse decomposition. With Modal, you no longer have to choose between ease of use and the latest developments in language model research—you can have both! All state-of-the-art LLM serving frameworks work out of the box, including: TensorRT. Next, we define a custom dataset class to handle our data. When applied to LLaMA-7B with 3-bit quantization, our method Jun 14, 2023 · Introducing SqueezeLLM, a framework for efficient quantization of large language models. By intelligently analyzing and quantizing only the most critical weights, it achieves 4–8x compression and up to 3x faster inference with minimal accuracy impact. SqueezeLLM is a method for compressing Large Language Models ( LLM) to contain their memory and compute requirements at inference time. 100,000+ question dataset for QA. Apple has made a recent breakthrough, Oct 31, 2023 · 3. self. Squeeze 1 dimensional axis objects into scalars. Most of Feb 1, 2024 · The Datacenter Squeeze is a Global Problem. Introducing SqueezeLLM, a post-training quantization framework that incorporates a new method called Dense-and-Sparse Quantization to enable efficient LLM serving. Let’s see how the libraries we just talked about helped. UC Berkeley’s SqueezeLLM combines Dense-and-Sparse decomposition with non-uniform quantization to achieve ultra-low-bit precision quantization. rotary embeddings, silu_and_mul activation function), and builds some more performant implementations of the models (such as vectorized sampling, and single qkv_proj / up_gate_proj) which Oct 5, 2023 · LLM Quantization Techniques: Understanding SqueezeLLM Compression. Dec 28, 2023 · This challenge is like finding a way to squeeze an elephant, the LLM, into a Mini Cooper, an iPhone. [1] Recent developments in Large Language Models (LLMs) have demonstrated their impressive problem-solving ability across several fields. TLDR: * Deploying Oct 21, 2023 · Activation-Aware Quantization squeezes every last bit of performance out of large language models. Optimizing the Key-Value (KV) cache of the Large Language Model (LLM) has been considered critical to saving the cost of inference. Here’s a comprehensive guide on how to prepare wisely for your LL. A large-scale reading comprehension dataset with more than 28,000 passages and nearly 100,000 questions. Apr 7, 2024 · This work proposes SqueezeAttention to precisely optimize the allocation of KV-cache budget among layers on-the-fly and then incorporate three representative token sparsification algorithms to compress the KV-cache for each layer with its very own budget. A review of convolutional neural networks (CNNs) is available here. Simply sign up to the Artificial intelligence myFT Digest -- delivered directly to your inbox. Perhaps The LLM Juice Isn’t Worth The Electrical Squeeze (rwblog S6E23) This will be an unusually content-lite post. Although seemingly quiet in the LLM space. 32k context window (vs 8k context in v0. 3/4 bit weight quantization for LLMs executability over the LLM baseline. Jun 13, 2023 · Generative Large Language Models (LLMs) have demonstrated remarkable results for a wide range of tasks. Stanford Question Answering Dataset is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the Jul 13, 2023 · The Great GPU Squeeze is Upon Us. squeeze-robot-hand-orange. Founded by Glenn Tilbrook (guitar, vocals), Chris Difford (guitar, vocals), Jools Holland (keyboards) and Paul Gunn (drums), the group have released 15 studio albums, 14 compilation albums, 4 live albums, 1 Apr 25, 2024 · Hercules-Mini-1. Cannot retrieve latest commit at this time. BaseModel. The Mistral-7B-Instruct-v0. 1) Rope-theta = 1e6; No Sliding-Window Attention; For full details of this model please read our paper and release blog post. HAWQ-V2: Hessian Aware trace-Weighted Quantization of neural networks. LLMs have impressive capabilities, but their high inference cost will hinder their large-scale adoption. vLLM. Series or DataFrames with a single element are squeezed to a scalar. Contribute to ray-project/ray-llm development by creating an account on GitHub. 8 billion parameters, this little powerhouse punches above its weight. g. 1. Hercules-Mini is a versatile LLM that can handle math, coding, roleplay, and even general assistant tasks. We would like to show you a description here but the site won’t allow us. We can use the models supported by this library on Apple Sep 16, 2023 · TL;DR: SqueezeLLM introduces a post-training quantization for LLMs that ensures loss-less ultra-low precision, leveraging sensitivity-based non-uniform quantization and Dense-and-Sparse decomposition to achieve ~4-5x compression rate and up to 2. The conducted human evaluation reveals a trade-off between executability and correctness but shows a promising sign towards extracting actionable knowledge from language models. Efficient compression: 3 minutes for pruning and 3 hours for post-training. squeeze(axis=None) [source] #. The proposed method aims to significantly reduce the memory footprint and inference latency of LLMs without sacrificing their performance. This has forced existing deployment frameworks to use multi-GPU inference pipelines, which are often complex and costly, or to use smaller and less performant Jul 6, 2023 · Understanding LLM Fine-Tuning. The top-20 most sensitive values are marked in red. main. Dec 12, 2023 · Deploying app 'ray-llm' failed with exception: Traceback (most recent call last): File "pydantic/main. LLMCompiler: An LLM Compiler for Parallel Function Calling LLMCompiler is a framework that enables an efficient and effective orchestration of parallel function calling with LLMs, including both open-source and close-source models, by automatically identifying which tasks can be performed in parallel and which ones are interdependent. In this paper, we propose an efficient LLM inference pipeline that harnesses the power of LLMs. 15B pages and over than 380TiB size dataset, public, free to use. Jan 17, 2022 · Goowin Watering Can for Indoor Plants . tools import tools as hotpotqa_tools from Figure 2: Normalized runtime for LLaMA-7B when reducing the bit precision for the weights with sequence lengths of 128 (left) and 2048 (right). Studies show that in LLM inference, memory bandwidth, not CPU, is the key performance limitation for generative tasks. AI generated image. init Simply put, unsqueeze() "adds" a superficial 1 dimension to tensor (at the specified dimension), while squeeze removes all superficial 1 dimensions from tensor. LLM LLM LLM. py. - "SqueezeLLM: Dense-and-Sparse Quantization" Sep 15, 2020 · Squeeze-and-Excitation Networks ( SENet) were the winners of the Imagenet Classification Challenge in 2017, surpassing the 2016 winners by a relative improvement of around 25%. Therefore, schema linking is a plugin that serves as a preprocessing step before SQL generation. 🔥 Extreme LLM Compression: Hype or Reality? 🔍 I&#39;ve been digging into the world of &quot;extreme&quot; LLM compression – techniques that squeeze massive language… Jun 13, 2023 · SqueezeLLM: Dense-and-Sparse Quantization. RAM: 1 – 3 GB. By leveraging sensitivity-based non-uniform quantization and dense-and-sparse decomposition, SqueezeLLM achieves high quantization performance and improved inference speed. You can access each attribute as you would usually do, and if that attribute has not been returned by the model, you will get None. In Europe, one-third of the grid infrastructure is over 40 years old, requiring an estimated €584 billion of investment by 2030 to meet the European Union’s green goals. Code. jd pd gp fe ry sm jc lu ol ce