CPU to the Rescue: LLMs for Everyone
Wed, 24 Apr 2024 13:25:19 -0000
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Optimizing Large Language Models
The past year has shown remarkable advances in large language models (LLMs) and what they can achieve. What started as tools for text generation have grown into multimodal models that can translate languages, hold conversations, generate music and images, and more. That said, training and running inference servers of these massive, multi-billion parameter models require immense computational resources and lots of high-end GPUs.
The surge in popularity of LLMs has fueled intense interest in porting these frameworks to mainstream CPUs. Open-source projects like llama.cpp and the Intel® Extension for Transformers aim to prune and optimize models for efficient execution on CPU architectures. These efforts encompass plain C/C++ implementations, hardware-specific optimizations for AVX, AVX2, and AVX512 instruction sets, and mixed precision model representations. Quantization and compression techniques are exploited to shrink models from 16-bit down to 8-bit or even 2-bit sizes. The goal is to obtain smaller, leaner models tailored for inferencing on widely available CPUs from the data center to your laptop.
While GPUs may still be preferred for training, CPUs in data centers and on devices can be used for efficient deployment to inference with these optimized models. CPUs can leverage recent advancements in architecture and provide broader access to large language model capabilities. The past year's advances in model optimization and CPU inferencing show promise in bringing natural language technologies powered by large models to more users.
Hardware
To evaluate these new CPU inferencing tools, we leveraged Dell Omnia cluster provisioning software to deploy Rocky Linux on a Dell PowerEdge C6620 server. Omnia allows rapid deployment of several operating system choices across a cluster of PowerEdge servers featuring Intel® Xeon® processors. By using Omnia for automated OS installation and configuration, we could quickly stand up a test cluster to experiment with the inference capabilities of the CPU-optimized models on our Intel® hardware.
Table 1. Dell PowerEdge C6620 specifications
Hardware | Details |
Server | Dell PowerEdge C6620 |
Processor Model | Intel® Xeon® Gold 6414U (Sapphire Rapids) |
Processors per Node | 2 |
Processor Core Count | 32 |
Processor Frequency | 2GHz |
Host Memory | 256 GB, 8 x 32GB |
Table 2. Involved software specifications
Software | Details |
Omnia | |
Rocky Linux 8.8 | |
Intel® Extensions for Transformers | |
Llama 2 with Intel® Neural Speed
Intel® has open sourced several tools under permissive licenses on GitHub to facilitate development with the Intel® Extensions for Transformers. One key offering is Neural Speed, which aims to enable efficient inferencing of large language models on Intel® hardware. Neural Speed leverages Intel® Neural Compressor, a toolkit for deep learning model optimization, to apply low-bit quantization and sparsity techniques that compress and accelerate the performance of leading LLMs. This allows Neural Speed to deliver state-of-the-art efficient inferencing for major language models. Neural Speed provides an inference stack that can maximize the performance of the latest Transformer-based language models on Intel® platforms ranging from edge to cloud. By open sourcing these technologies with permissive licensing, Intel® enables developers to easily adopt and innovate with optimized LLM inferencing across Intel® hardware.
To get started, clone the Intel® Neural Speed repo and install packages:
git clone https://github.com/intel/neural-speed.git pip install -r requirements.txt pip install .
Neural Speed can support 3 different model types:
- GGUF models generated by llama.cpp
- GGUF models from HuggingFace
- Pytorch models from HuggingFace – quantized by Neural Speed
We began our experiments by working directly with Meta's Llama-2-7B-chat model in Pytorch from Hugging Face. This 7 billion parameter conversational model served as an ideal test case for evaluating end-to-end model conversion, quantization, and inferencing using the Neural Speed toolkit. To streamline testing, Neural Speed provides handy scripts to handle the full pipeline, beginning with taking the original Pytorch model and porting it to a GGUF model, then applying quantization policies to compress the model down to lower precisions like int8 or int4, and finally running inference to assess the performance. In this case, we did not compress the model and retained 32-bit values.
The following command will run a one-click conversion, quantization, and inference:
python scripts/run.py \ /home/models/ Llama-2-7b-chat-hf \ --weight_dtype f32 \ -p "always answer with Haiku. What is the greatest thing about sailing?
Sailing's greatest joy, Freedom on the ocean blue, Serenity found.
Conclusion
In our testing, the converted and quantized Llama-2 model provided not only accurate responses but also excellent response latency, which we will dig deeper into with future blogs. While we demonstrated this workflow on Meta's 7 billion parameter Llama-2 conversational AI, the same process can be applied to port and optimize many other leading large language models to run efficiently on CPUs. Other suitable candidates include chat-centric LLMs like NeuralChat, GPT-J, GPT-NEOX, Dolly-v2, and MPT, as well as general purpose models like Falcon, BLOOM, Mistral, OPT, and Hugging Face's DistilGPT2. Code-focused models like CodeLlama, MagicCoder, and StarCoder could also potentially benefit. Additionally, Chinese models including Baichuan, Baichuan2, and Qwen are prime targets for improved deployment on Intel® CPUs.
The key advantage of this CPU inferencing approach is harnessing all available CPU cores for cost-effective parallel inferencing. By converting and quantizing models to run natively on Intel® CPUs, we can take full advantage of ubiquitous Intel®-powered machines ranging from laptops to servers. For platforms lacking high-end GPUs, optimizing models to leverage existing CPU resources is a compelling way to deliver responsive AI experiences.
Author: John Lockman III, Distinguished Engineer | https://www.linkedin.com/in/johnlockman/
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Unlocking the Power of Large Language Models and Generative AI: A Dell and Run:ai Joint Solution
Tue, 30 Jan 2024 19:47:13 -0000
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In the fast-paced landscape of AI, the last year has undeniably been marked as the era of Large Language Models (LLMs), especially in the Generative AI (GenAI) field. Models like GPT-4 and Falcon have captured our imagination, showcasing the remarkable potential of these LLMs. However, beneath their transformative capabilities lie a substantial challenge: the insatiable hunger for computational resources.
The demand for compute: fueling innovation with computational power
GenAI applications span from media industry to software development, driving innovation across industries. OpenAI's release of GPT-3 was a turning point, demonstrating the capabilities of language models and their potential to revolutionize every sector. On one hand, startups and tech giants have introduced closed-source models, offering APIs for their usage, exemplified by OpenAI and GPT-4. On the other hand, an active open-source community has emerged, releasing powerful models such as Falcon and Llama 2. These models, both closed- and open-source, have spurred a wave of interest, with companies racing to use their potential.
While the promise of LLMs is enormous, they come with a significant challenge—access to high-performance GPUs. Enterprises aiming to deploy these models in their private data centers or cloud environments must contend with the need for substantial GPU power. Security concerns further drive the preference for in-house deployments, making GPU accessibility critical.
The infrastructure required to support LLMs often includes high-end GPUs connected through fast interconnects and storage solutions. These resources are not just expensive and scarce but are also in high demand, leading to bottlenecks in machine learning (ML) development and deployment. Orchestrating these resources efficiently and providing data science and ML teams with easy and scalable access becomes a Herculean task.
Challenges with GPU allocation
In this landscape, GPUs are the backbone of the computational power that fuels these massive language models. Due to the limited availability of on-premises and cloud resources, the open-source community has taken steps to address this challenge. Libraries like bits and bytes (by Tim Dettmers) and ggml (by Georgi Gerganov) have emerged, using various optimization techniques such as quantization to fine-tune and deploy these models on local devices.
However, the challenges are not limited to model development and deployment. These LLMs demand substantial GPU capacity to maintain low latency during inference and high throughput during fine-tuning. In the real world, the need for capacity means having an infrastructure that dynamically allocates GPU resources to handle LLM fine-tuning and inference operations, all while ensuring efficiency and minimal wasted capacity.
As an example, consider loading LLama-7B using half precision (float16). Such a model requires approximately 12GB of GPU memory─a figure that can be even lower with the use of lower precision. In instances where high-end GPUs, like the NVIDIA A100 GPU with 40 GB (or 80 GB) of memory, are dedicated solely to a single model, severe resource waste results, especially when done at scale. The wasted resource does not only translate to financial inefficiencies but also reduced productivity in data science teams, and an increased carbon footprint due to the excessive underutilization of running resources over extended periods.
Some LLMs are so large that they must be distributed across multiple GPUs or multiple GPU servers. Consider Falcon-180B using full precision. Such a model requires approximately 720 GB and the use of more than 16 NVIDIA A100 GPUs with 40 GB each. Fine tuning such models and running them in production requires tremendous computing power and significant scheduling and orchestration challenges. Such workloads require not only a high-end compute infrastructure but also a high-end performant software stack that can distribute these workloads efficiently without bottlenecks.
Apart from training jobs, serving these models also requires efficient autoscaling on hardware. When there is high demand, these applications must be able to scale up to hundreds of replicas rapidly, while in low demand situations, they can be scaled down to zero to save costs.
Optimizing the management of LLMs for all these specific needs necessitates a granular view of GPU use and performance as well as high-level scheduling view of compute-intensive workloads. For instance, it is a waste if a single model like LLama-7B (12 GB) is run on an NVIDIA A100 (40GB) with almost 60 percent spare capacity instead of using this remaining capacity for an inference workload.
Concurrency and scalability are essential, both when dealing with many relatively small, on-premises models, each fine-tuned and tailored to specific use cases as well as when dealing with huge performant models needing careful orchestration. These unique challenges require a resource orchestration tool like Run:ai to work seamlessly on top of Dell hardware. Such a solution empowers organizations to make the most of their GPU infrastructure, ensuring that every ounce of computational power is used efficiently. By addressing these challenges and optimizing GPU resources, organizations can harness the full potential of LLMs and GenAI, propelling innovation across various industries.
Dell Technologies and Run:ai: joint solution
To address these bottlenecks, which hinder the rapid adoption of GenAI in organizations, Run:ai, a compute orchestration solution, teams up with Dell Technology.
The Dell Generative AI Solutions portfolio, a comprehensive suite of Dell products and services (Dell PowerEdge XE9680, PowerEdge 760XA, and PowerEdge XE8640 servers) in collaboration with NVIDIA, enables customers to build GenAI models on-premises quickly and securely, accelerate improved outcomes, and drive new levels of intelligence. Dell Validated Designs for Generative AI now support both model tuning and inferencing, allowing users to deploy GenAI models quickly with pretested and proven Dell infrastructure, software, and services to power transformative business outcomes with GenAI. The Validated designs integrate end-to-end AI solutions including all the critical components (server, networking, storage, and software) for AI systems, while Run:ai introduces two key technological components that unlock the true potential of these AI models: GPU optimization and a sophisticated scheduling system for training and inference workloads. Extending the Dell GenAI approaches with Run:ai orchestration enables customers to optimize GenAI and AI operations to build and train AI models and run inferencing with greater speed and efficiency.
AI-optimized compute: maximizing GPU utilization
Dell Technologies offers a range of acceleration-optimized PowerEdge servers, purpose-built for high-performance workloads like AI and demanding use-cases in generative AI, as part of the extensive server portfolio that supports various NVIDIA GPUs. Dell PowerEdge servers advance accelerated compute to drive enhanced AI workload outcomes with greater insights, inferencing, training, and visualization. However, one of the primary challenges in training and deploying LLMs is GPU use. Together with Dell PowerEdge servers, Run:ai's GPU optimization layer enables features like fractionalizing GPUs and GPU oversubscription. These features ensure that multiple workloads (training and inference), even small models, can efficiently run on the same GPU. By making better use of existing GPU resources, costs are reduced, and bottlenecks are mitigated.
Advanced scheduling: efficient workload management
Run:ai's advanced scheduling system integrates seamlessly into Kubernetes environments on top of PowerEdge servers. It is designed to tackle the complexities that arise when multiple teams and users share a GPU cluster and when running large multi-GPU or multi-node workloads. The scheduler optimizes resource allocation, ensuring efficient utilization of GPUs among various workloads, including training, fine-tuning, and inference.
Autoscaling and GPU optimization for inference workloads
Run:ai's autoscaling functionality enables dynamic adjustments to the number of replicas, allowing for efficient scaling based on demand. In times of increased workload, Run:ai optimally uses the available GPU, scaling up the replicas to meet performance requirements. Conversely, during periods of low demand, the number of replicas can be scaled down to zero, minimizing resource use and leading to cost savings. While there might be a brief cold start delay with the first request, this approach provides a flexible and effective solution to adapt to changing inference demands while optimizing costs.
Beyond autoscaling, deploying models for inference using Run:ai is a straightforward process. Internal users can effortlessly deploy their models and access them through managed URLs or user-friendly web interfaces like Gradio and Streamlit. This streamlined deployment process facilitates sharing and presentation of deployed LLMs, fostering collaboration and delivering a seamless experience for stakeholders.
AI networking
To achieve high throughput in multi-node training and low latency when hosting a model on multiple machines, most GenAI models require robust and highly performant networking capabilities on hardware, which is where Dell's networking capabilities and offerings come into play. The network interconnects the compute nodes among each other to facilitate communications during distributed training and inferencing. The Dell PowerSwitch Z-series are high-performance, open, and scalable data center switches ideal for generative AI, as well as NVIDIA Quantum InfiniBand switches for faster connectivity.
Fast access to your data
Data is a crucial component for each part of the development and deployment steps. Dell PowerScale storage supports the most demanding AI workloads with all-flash NVMe file storage solutions that deliver massive performance and efficiency in a compact form factor. PowerScale is an industry-leading storage platform purpose-built to handle massive amounts of unstructured data, ideal for supporting datatypes required for generative AI.
Streamlined LLM tools
To simplify the experience for researchers and ML engineers, Run:ai offers a suite of tools and frameworks. They remove the complexities of GPU infrastructure with interfaces like command-line interfaces, user interfaces, and APIs on top of Dell hardware. With these tools, training, fine-tuning, and deploying models become straightforward processes, enhancing productivity, and reducing time-to-market. As a data scientist, you can take pretrained models from the Huggingface model hub and start working on them with your favorite IDE and experiment with management tools in minutes, a testament to the efficiency and ease of the Dell and Run:ai solution.
Benefits of the Dell and Run:ai solution for customers
Now that we have explored the challenges posed by LLMs and the joint solution of Dell Technologies and Run:ai to these bottlenecks, let's dive into the benefits that this partnership between Dell Technologies and Run:ai and offers to customers:
1. Accelerated time-to-market
The combination of Run:ai's GPU optimization and scheduling solutions, along with Dell's robust infrastructure, significantly accelerates the time-to-market for AI initiatives. By streamlining the deployment and management of LLMs, organizations can quickly capitalize on their AI investments.
2. Enhanced productivity
Data science and ML engineering teams, often unfamiliar with the complexities of AI infrastructure, can now focus on what they do best: building and fine-tuning models. Run:ai's tools simplify the process, reducing the learning curve and improving productivity.
3. Cost efficiency
Optimizing GPU use not only provides performance but also provides cost-effectiveness. By running multiple workloads on the same GPU, organizations can achieve better cost efficiency, get the most out of their infrastructure, thus making AI initiatives more financially viable.
4. Increased scalability and GPU availability
Run:ai's advanced scheduling system ensures that workloads are efficiently managed, even during peak demand. This scalability is crucial for organizations that need to serve language models in real time to a growing user base. In addition, the scheduling component ensures fair and optimized allocation of GPU resources between multiple users, teams, and tasks, preventing resource bottlenecks and contention and increasing availability of GPUs to allow more users, teams, and AI services to get access and use available GPU resources effectively.
5. Innovation unleashed
The solution empowers enterprise teams to innovate and experiment with LLMs and GenAI without being hindered by infrastructure complexities. Researchers and ML engineers can easily fine-tune and deploy models using abstraction tools, fostering innovation and exploration in AI projects.
Summary
The joint solution offered by Dell Technologies and Run:ai addresses the critical challenges faced by organizations ramping up with GenAI for their business needs and working with LLMs. By enhancing GPU accessibility, optimizing scheduling, streamlining workflows, and saving costs, this solution empowers businesses to fully harness the potential of LLMs in GenAI applications while simplifying the challenges. With AI initiatives becoming increasingly vital in today's world, this partnership offers businesses new ways to automate and simplify their GenAI strategy and drive more business innovation.
For information about how to get started with Dell Technologies and Run:ai on your GenAI journey, see these resources:
- Dell AI Solutions
- GPU Optimization with Run:ai Atlas technical whitepaper
- Accelerate AI by Optimizing Compute Resources
- Run:ai
Authors: Justin King, Ekin Karabulut
Contributor: James Yung
Deploying Llama 7B Model with Advanced Quantization Techniques on Dell Server
Tue, 16 Jan 2024 20:05:01 -0000
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Introduction
Large-language Models (LLMs) have gained great industrial and academic interest in recent years. Different LLMs have been adopted in various applications, such as: content generation, text summarization, sentiment analysis, and healthcare. The LLM evolution diagram in Figure 1 shows the popular pre-trained models since 2017 when the transformer architecture was first introduced [1]. It is not hard to find the trend of larger and more open-source models following the timeline. Open-source models boosted the popularity of LLMs by eliminating the huge training cost associated with the large scale of the infrastructure and long training time required. Another portion of the cost of LLM applications comes from the deployment where an efficient inference platform is required.
This blog focuses on how to deploy LLMs efficiently on Dell platform with different quantization techniques. We first benchmarked the model accuracy under different quantization techniques. Then we demonstrated their performance and memory requirements of running LLMs under different quantization techniques through experiments. Specifically, we chose the open-source model Llama-2-7b-chat-hf for its popularity [2]. The server is chosen to be Dell main-stream server R760xa with NVIDIA L40 GPUs [3] [4]. The deployment framework in the experiments is TensorRT-LLM, which enables different quantization techniques including advanced 4bit quantization as demonstrated in the blog [5].
Figure 1 :LLM evolution
Background
LLM inferencing processes tend to be slow and power hungry, because of the characteristics of LLMs being large in weight size and having auto-regression. How to make the inferencing process more efficient under limited hardware resources is among the most critical problems for LLM deployment. Quantization is an important technique widely used to push for more efficient LLM deployment. It can relieve the large hardware resource requirements by reducing the memory footprint and computation energy, as well as improve the performance with faster memory access time compared to the deployment with the original un-quantized model. For example, in [6], the performance in terms of throughput by tokens per second (tokens/s) for Llama-2-7b model is improved by more than 2x by quantizing from floating point 16-bit format to integer 8-bit. Recent research made more aggressive quantization techniques like 4-bit possible and available in some deployment frameworks like TensorRT-LLM. However, quantization is not free, and it normally comes with accuracy loss. Besides the cost, reliable performance with acceptable accuracy for specific applications is what users would care about. Two key topics covered in this blog are accuracy and performance. We first benchmark the accuracy of the original model and quantized models over different tasks. Then we deployed those models into Dell server and measured their performance. We further measured the GPU memory usage for each scenario.
Test Setup
The model under investigation is Llama-2-7b-chat-hf [2]. This is a finetuned LLMs with human-feedback and optimized for dialogue use cases based on the 7-billion parameter Llama-2 pre-trained model. We load the fp16 model as the baseline from the huggingface by setting torch_dtype to float16.
We investigated two advanced 4-bit quantization techniques to compare with the baseline fp16 model. One is activation-aware weight quantization (AWQ) and the other is GPTQ [7] [8]. TensorRT-LLM integrates the toolkit that allows quantization and deployment for these advanced 4-bit quantized models.
For accuracy evaluation across models with different quantization techniques, we choose the Massive Multitask Language Understanding (MMLU) datasets. The benchmark covers 57 different subjects and ranges across different difficulty levels for both world knowledge and problem-solving ability tests [9]. The granularity and breadth of the subjects in MMLU dataset allow us to evaluate the model accuracy across different applications. To summarize the results more easily, the 57 subjects in the MMLU dataset can be further grouped into 21 categories or even 4 main categories as STEM, humanities, social sciences, and others (business, health, misc.) [10].
Performance is evaluated in terms of tokens/s across different batch sizes on Dell R760xa server with one L40 plugged in the PCIe slots. The R760xa server configuration and high-level specification of L40 are shown in Table 1 and 2 [3] [4]. To make the comparison easier, we fix the input sequence length and output sequence length to be 512 and 200 respectively.
System Name | PowerEdge R760xa |
Status | Available |
System Type | Data Center |
Number of Nodes | 1 |
Host Processor Model | 4th Generation Intel® Xeon® Scalable Processors |
Host Process Name | Intel® Xeon® Gold 6430 |
Host Processors per Node | 2 |
Host Processor Core Count | 32 |
Host Processor Frequency | 2.0 GHz, 3.8 GHz Turbo Boost |
Host Memory Capacity and Type | 512GB, 16 x 32GB DIMM, 4800 MT/s DDR5 |
Host Storage Capacity | 1.8 TB, NVME |
Table 1: R760xa server configuration
GPU Architecture | L40 NVIDIA Ada Lovelace Architecture |
GPU Memory Bandwidth | 48 GB GDDR6 with ECC |
Max Power Consumption | 300W |
Form Factor | 4.4" (H) x 10.5" (L) Dual Slot |
Thermal | Passive |
Table 2: L40 High-level specification
The inference framework that includes different quantization tools is NVIDIA TensorRT-LLM initial release version 0.5. The operating system for the experiments is Ubuntu 22.04 LTS.
Results
We first show the model accuracy results based on the MMLU dataset tests in Figure 2 and Figure 3, and throughput performance results when running those models on PowerEdge R760xa in Figure 4. Lastly, we show the actual peak memory usage for different scenarios. Brief discussions are given for each result. The conclusions are summarized in the next section.
Accuracy
Figure 2:MMLU 4-category accuracy test result
Figure 2 shows the accuracy test results of 4 main MMLU categories for the Llama-2-7b-chat-hf model. Compared to the baseline fp16 model, we can see that the model with 4-bit AWQ has a significant accuracy drop. On the other hand, the model with 4-bit GPTQ has a much smaller accuracy drop, especially for the STEM category, the accuracy drop is smaller than 5%.
Figure 3:MMLU 21-category accuracy test result
Figure 3 further shows the accuracy test results of 21 MMLU sub-categories for the Llama-2-7b-chat-hf model. Similar conclusions can be made that the 4-bit GPTQ quantization gives much better accuracy, except for the law category, the two quantization techniques achieve a close accuracy.
Performance
Figure 4: Throughput test result
Figure 4 shows the throughput numbers when running Llama-2-7b-chat-hf with different batch size and quantization methods on R760xa server. We observe significant throughput boost with the 4-bit quantization, especially when the batch size is small. For example, a 3x tokens/s is achieved when the batch size is 1 when comparing the scenarios with 4-bit AWQ or GPTQ quantization to the 16-bit baseline scenario. Both AWQ and GPTQ quantization give similar performance across different batch sizes.
GPU Memory Usage
Figure 5: Peak GPU memory usage
Figure 5 shows the peak GPU memory usage when running Llama-2-7b-chat-hf with different batch size and quantization methods on R760xa server. From the results, 4-bit quantization techniques greatly reduced the memory required for running the model. Compared to the memory size required for the baseline fp16 model, the quantized models with AWQ or GPTQ only requires half or even less of the memory, depending on the batch size. A slightly larger peak memory usage is also observed for GPTQ quantized model compared to the AWQ quantized model.
Conclusion
- We have shown the impacts for accuracy, performance, and GPU memory usage by applying advanced 4-bit quantization techniques on Dell PowerEdge server when running Llama 7B model.
- We have demonstrated the great benefits of these 4-bit quantization techniques in terms of improving throughput and saving GPU memory.
- We have quantitively compared the quantized models with the baseline model in terms of accuracy among various subjects based on the MMLU dataset.
- Tests showed that with an acceptable accuracy loss, 4-bit GPTQ is an attractive quantization method for the LLM deployment where the hardware resource is limited. On the other hand, large accuracy drops across many MMLU subjects have been observed for the 4-bit AWQ. This indicates the model should be limited to the applications tied to some specific subjects. Otherwise, other techniques like re-training or fine-turning techniques may be required to improve accuracy.
References
[1]. A. Vaswani et. al, “Attention Is All You Need”, https://arxiv.org/abs/1706.03762
[2]. https://huggingface.co/meta-llama/Llama-2-7b-chat-hf
[4]. https://www.nvidia.com/en-us/data-center/l40/
[5]. https://github.com/NVIDIA/TensorRT-LLM
[7]. J. Lin et. al, “AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration”, https://arxiv.org/abs/2306.00978
[8]. E. Frantar et. al, “GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers”, https://arxiv.org/abs/2210.17323
[9]. D. Hendrycks et. all, “Measuring Massive Multitask Language Understanding”, https://arxiv.org/abs/2009.03300
[10]. https://github.com/hendrycks/test/blob/master/categories.py