diff --git a/.gitignore b/.gitignore index bc25b27d2a..90df706b60 100644 --- a/.gitignore +++ b/.gitignore @@ -12,4 +12,4 @@ package-lock.json nohup.out venv/ -contributor_private.csv \ No newline at end of file +z_local_saved/ \ No newline at end of file diff --git a/content/learning-paths/servers-and-cloud-computing/aaaademo-audio/_index.md b/content/learning-paths/servers-and-cloud-computing/aaaademo-audio/_index.md deleted file mode 100644 index ce59e6bfed..0000000000 --- a/content/learning-paths/servers-and-cloud-computing/aaaademo-audio/_index.md +++ /dev/null @@ -1,47 +0,0 @@ ---- -title: aaa Whisper cpp audio demo aaaaa - -minutes_to_complete: 60 - -who_is_this_for: Test - -learning_objectives: - - One - - Two - - Three - -prerequisites: - - Viewing the demo! - -author: Zach Lasiuk - - - -### Tags -skilllevels: Advanced -subjects: Containers and Virtualization -armips: - - Neoverse -operatingsystems: - - Linux -tools_software_languages: - - Kubernetes - - -further_reading: - - resource: - title: Getting started with Llama - link: https://llama.meta.com/get-started - type: documentation - - resource: - title: Hugging Face Documentation - link: https://huggingface.co/docs - type: documentation - - -### FIXED, DO NOT MODIFY -# ================================================================================ -weight: 2 # _index.md always has weight of 1 to order correctly -layout: "learningpathall" # All files under learning paths have this same wrapper -learning_path_main_page: "yes" # This should be surfaced when looking for related content. Only set for _index.md of learning path content. ---- diff --git a/content/learning-paths/servers-and-cloud-computing/aaaademo-audio/_next-steps.md b/content/learning-paths/servers-and-cloud-computing/aaaademo-audio/_next-steps.md deleted file mode 100644 index c3db0de5a2..0000000000 --- a/content/learning-paths/servers-and-cloud-computing/aaaademo-audio/_next-steps.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -# ================================================================================ -# FIXED, DO NOT MODIFY THIS FILE -# ================================================================================ -weight: 21 # Set to always be larger than the content in this path to be at the end of the navigation. -title: "Next Steps" # Always the same, html page title. -layout: "learningpathall" # All files under learning paths have this same wrapper for Hugo processing. ---- diff --git a/content/learning-paths/servers-and-cloud-computing/aaademo-kubectl-arm/_demo.md b/content/learning-paths/servers-and-cloud-computing/aaademo-kubectl-arm/_demo.md deleted file mode 100644 index 6cc4c33393..0000000000 --- a/content/learning-paths/servers-and-cloud-computing/aaademo-kubectl-arm/_demo.md +++ /dev/null @@ -1,36 +0,0 @@ ---- -title: Demo - Run kubectl on Arm CPU -overview: Kubernetes supports many types of apps - web/game servers, data storage, AI training/inference, and so much more. It also supports many architectures, including Arm64. Using this Arm Kubernetes migration tool, you can identify if your cluster already just works on arm64. This demo illustrates how easy it is to run this tool yourself by using pre-loaded Kubernetes cluster config files and a console notebook to run the tool within. - -demo_steps: - - Select a Kubernetes cluster file to analyze. - - Run the Kubernetes migration tool in the demo space. - - Review the output and stats to understand it. - -diagram: kubernetes_diagram.png - -configuration_popup_details: Super long list of configuration information to provide to the user. Should be context and all that to be crystal clear what the setup is. - -configuration_dropdown_options: - - parameters: - param_name: Cluster .yaml file - options: - - name: Hosted LLM chatbot - specs: Hosted LLM chatbot cluster includes Rancher packages, the web server NGINX, Redis, and Mongo-DB as a simple database. ML capabilities come from TensorFlow and Rocket.chat modules. - - name: other file - specs: Other information. - selectable: true - explanation: Helpbox info that isn't used yet. - - -### Specific details to this demo -# ================================================================================ -prismjs: true - -### FIXED, DO NOT MODIFY -# ================================================================================ -demo_template_name: kubectl_demo # allows the 'demo.html' partial to route to the correct Configuration and Demo/Stats sub partials for page render. -weight: 1 # _index.md always has weight of 1 to order correctly -layout: "learningpathall" # All files under learning paths have this same wrapper -learning_path_main_page: "yes" # This should be surfaced when looking for related content. Only set for _index.md of learning path content. ---- diff --git a/content/learning-paths/servers-and-cloud-computing/aaademo-kubectl-arm/_index.md b/content/learning-paths/servers-and-cloud-computing/aaademo-kubectl-arm/_index.md deleted file mode 100644 index e9378d6fb9..0000000000 --- a/content/learning-paths/servers-and-cloud-computing/aaademo-kubectl-arm/_index.md +++ /dev/null @@ -1,49 +0,0 @@ ---- -title: Kubernetes Arm quicktool aaaa - -minutes_to_complete: 60 - -who_is_this_for: This is for developers who want to use the Kubernetes migration tool. - -learning_objectives: - - One - - Two - - Three - -prerequisites: - - Viewing the demo! - -author: Zach Lasiuk - -draft: true -cascade: - draft: true - -### Tags -skilllevels: Advanced -subjects: Containers and Virtualization -armips: - - Neoverse -operatingsystems: - - Linux -tools_software_languages: - - Kubernetes - - -further_reading: - - resource: - title: Getting started with Llama - link: https://llama.meta.com/get-started - type: documentation - - resource: - title: Hugging Face Documentation - link: https://huggingface.co/docs - type: documentation - - -### FIXED, DO NOT MODIFY -# ================================================================================ -weight: 2 # _index.md always has weight of 1 to order correctly -layout: "learningpathall" # All files under learning paths have this same wrapper -learning_path_main_page: "yes" # This should be surfaced when looking for related content. Only set for _index.md of learning path content. ---- diff --git a/content/learning-paths/servers-and-cloud-computing/aaademo-kubectl-arm/_next-steps.md b/content/learning-paths/servers-and-cloud-computing/aaademo-kubectl-arm/_next-steps.md deleted file mode 100644 index c3db0de5a2..0000000000 --- a/content/learning-paths/servers-and-cloud-computing/aaademo-kubectl-arm/_next-steps.md +++ /dev/null @@ -1,8 +0,0 @@ ---- -# ================================================================================ -# FIXED, DO NOT MODIFY THIS FILE -# ================================================================================ -weight: 21 # Set to always be larger than the content in this path to be at the end of the navigation. -title: "Next Steps" # Always the same, html page title. -layout: "learningpathall" # All files under learning paths have this same wrapper for Hugo processing. ---- diff --git a/content/learning-paths/servers-and-cloud-computing/aaademo-kubectl-arm/hosted-llm-chatbot.yaml b/content/learning-paths/servers-and-cloud-computing/aaademo-kubectl-arm/hosted-llm-chatbot.yaml deleted file mode 100644 index 9a68fbb3d0..0000000000 --- a/content/learning-paths/servers-and-cloud-computing/aaademo-kubectl-arm/hosted-llm-chatbot.yaml +++ /dev/null @@ -1,235 +0,0 @@ -apiVersion: v1 -kind: Namespace -metadata: - name: hosted-llm-chatbot - ---- - -apiVersion: apps/v1 -kind: Deployment -metadata: - name: coredns - namespace: hosted-llm-chatbot -spec: - replicas: 2 - selector: - matchLabels: - app: coredns - template: - metadata: - labels: - app: coredns - spec: - containers: - - name: coredns - image: rancher/mirrored-coredns-coredns:1.10.1 - ---- - -apiVersion: apps/v1 -kind: Deployment -metadata: - name: local-path-provisioner - namespace: hosted-llm-chatbot -spec: - replicas: 1 - selector: - matchLabels: - app: local-path-provisioner - template: - metadata: - labels: - app: local-path-provisioner - spec: - containers: - - name: local-path-provisioner - image: rancher/local-path-provisioner:v0.0.24 - ---- - -apiVersion: apps/v1 -kind: Deployment -metadata: - name: klipper-helm - namespace: hosted-llm-chatbot -spec: - replicas: 1 - selector: - matchLabels: - app: klipper-helm - template: - metadata: - labels: - app: klipper-helm - spec: - containers: - - name: klipper-helm - image: rancher/klipper-helm:v0.8.0-build20230510 - ---- - -apiVersion: apps/v1 -kind: Deployment -metadata: - name: klipper-lb - namespace: hosted-llm-chatbot -spec: - replicas: 1 - selector: - matchLabels: - app: klipper-lb - template: - metadata: - labels: - app: klipper-lb - spec: - containers: - - name: klipper-lb - image: rancher/klipper-lb:0.4.4 - ---- - -apiVersion: apps/v1 -kind: Deployment -metadata: - name: traefik - namespace: hosted-llm-chatbot -spec: - replicas: 1 - selector: - matchLabels: - app: traefik - template: - metadata: - labels: - app: traefik - spec: - containers: - - name: traefik - image: rancher/mirrored-library-traefik:2.9.10 - ---- - -apiVersion: apps/v1 -kind: Deployment -metadata: - name: metrics-server - namespace: hosted-llm-chatbot -spec: - replicas: 1 - selector: - matchLabels: - app: metrics-server - template: - metadata: - labels: - app: metrics-server - spec: - containers: - - name: metrics-server - image: rancher/mirrored-metrics-server:v0.6.3 - ---- - -apiVersion: apps/v1 -kind: Deployment -metadata: - name: redis - namespace: hosted-llm-chatbot -spec: - replicas: 1 - selector: - matchLabels: - app: redis - template: - metadata: - labels: - app: redis - spec: - containers: - - name: redis - image: redis:5.0.5 - ---- - -apiVersion: apps/v1 -kind: Deployment -metadata: - name: nginx - namespace: hosted-llm-chatbot -spec: - replicas: 1 - selector: - matchLabels: - app: nginx - template: - metadata: - labels: - app: nginx - spec: - containers: - - name: nginx - image: nginx:1.14.2 - ---- - -apiVersion: apps/v1 -kind: Deployment -metadata: - name: mongo - namespace: hosted-llm-chatbot -spec: - replicas: 1 - selector: - matchLabels: - app: mongo - template: - metadata: - labels: - app: mongo - spec: - containers: - - name: mongo - image: docker.io/mongo:4.0.10 - ---- - -apiVersion: apps/v1 -kind: Deployment -metadata: - name: tensorflow - namespace: hosted-llm-chatbot -spec: - replicas: 1 - selector: - matchLabels: - app: tensorflow - template: - metadata: - labels: - app: tensorflow - spec: - containers: - - name: tensorflow - image: tensorflow/tensorflow:2.16.1 - ---- - -apiVersion: apps/v1 -kind: Deployment -metadata: - name: rocketchat - namespace: hosted-llm-chatbot -spec: - replicas: 1 - selector: - matchLabels: - app: rocketchat - template: - metadata: - labels: - app: rocketchat - spec: - containers: - - name: rocketchat - image: rocket.chat:6.7.0 diff --git a/content/learning-paths/servers-and-cloud-computing/aaademo-kubectl-arm/kubernetes-instructions.md b/content/learning-paths/servers-and-cloud-computing/aaademo-kubectl-arm/kubernetes-instructions.md deleted file mode 100644 index 9a35db9441..0000000000 --- a/content/learning-paths/servers-and-cloud-computing/aaademo-kubectl-arm/kubernetes-instructions.md +++ /dev/null @@ -1,266 +0,0 @@ ---- -title: Tool installation and running -weight: 3 - -### FIXED, DO NOT MODIFY -layout: learningpathall ---- - -## Before you begin -The instructions in this Learning Path are for any Arm server running Ubuntu 22.04 LTS. You need an Arm server instance with at least four cores and 8GB of RAM to run this example. The instructions have been tested on an AWS Graviton3 c7g.2xlarge instance. - -## Overview - -Arm CPUs are widely used in traditional ML and AI use cases. In this Learning Path, you learn how to run generative AI inference-based use cases like a LLM chatbot on Arm-based CPUs. You do this by deploying the [Llama-2-7B-Chat model](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF) on your Arm-based CPU using `llama.cpp`. - -[llama.cpp](https://github.com/ggerganov/llama.cpp) is an open source C/C++ project developed by Georgi Gerganov that enables efficient LLM inference on a variety of hardware - both locally, and in the cloud. - -## About the Llama 2 model and GGUF model format - -The [Llama-2-7B-Chat model](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF) from Meta belongs to the Llama 2 model family and is free to use for research and commercial purposes. Before you use the model, visit the Llama [website](https://llama.meta.com/llama-downloads/) and fill in the form to request access. - - -Llama 2 collection of models perform general natural language processing (NLP) tasks such as text generation. You can access the base foundation Llama 2 model or select the specialized chat Llama 2 version that is already optimized for back-and-forth dialogue. In this Learning Path, you run the specialized chat model. -The Llama 2 family of models range in size from 7 billion to 70 billion parameters. The greater the number of parameters, the more information the model can store. This directly affects how well the model understands language and the model's general capabilities. LLMs that run efficiently on CPUs typically have lower numbers of parameters. For this example, the 7 billion (7b) model is ideal for retaining quality chatbot capability while also running efficiently on your Arm-based CPU. - -Traditionally, the training and inference of LLMs has been done on GPUs using full-precision 32-bit (FP32) or half-precision 16-bit (FP16) data type formats for the model parameter and weights. Recently, a new binary model format called GGUF was introduced by the `llama.cpp` team. This new GGUF model format uses compression and quantization techniques that remove the dependency on using FP32 and FP16 data type formats. For example, GGUF supports quantization where model weights that are generally stored as FP16 data types are scaled down to 4-bit integers. This significantly reduces the need for computational resources and the amount of RAM required. These advancements made in the model format and the data types used make Arm CPUs a great fit for running LLM inferences. - -## Install dependencies - -Install the following packages on your Arm based server instance: - -```bash -sudo apt update -sudo apt install make cmake -y -``` - -You also need to install `gcc` on your machine: - -```bash -sudo apt install gcc g++ -y -sudo apt install build-essential -y -``` - -## Download and build llama.cpp - -You are now ready to start building `llama.cpp`. - -Clone the source repository for llama.cpp: - -```bash -git clone https://github.com/ggerganov/llama.cpp -``` - -By default, `llama.cpp` builds for CPU only on Linux and Windows. You don't need to provide any extra switches to build it for the Arm CPU that you run it on. - -Run `make` to build it: - -```bash -cd llama.cpp -make GGML_NO_LLAMAFILE=1 -j$(nproc) -``` - -Check that `llama.cpp` has built correctly by running the help command: - -```bash -./llama-cli -h -``` - -If `llama.cpp` has built correctly on your machine, you will see the help options being displayed. A snippet of the output is shown below: - -```output -usage: ./llama-cli [options] - -general: - - -h, --help, --usage print usage and exit - --version show version and build info - -v, --verbose print verbose information - --verbosity N set specific verbosity level (default: 0) - --verbose-prompt print a verbose prompt before generation (default: false) - --no-display-prompt don't print prompt at generation (default: false) - -co, --color colorise output to distinguish prompt and user input from generations (default: false) - -s, --seed SEED RNG seed (default: -1, use random seed for < 0) - -t, --threads N number of threads to use during generation (default: 4) - -tb, --threads-batch N number of threads to use during batch and prompt processing (default: same as --threads) - -td, --threads-draft N number of threads to use during generation (default: same as --threads) - -tbd, --threads-batch-draft N number of threads to use during batch and prompt processing (default: same as --threads-draft) - --draft N number of tokens to draft for speculative decoding (default: 5) - -ps, --p-split N speculative decoding split probability (default: 0.1) - -lcs, --lookup-cache-static FNAME - path to static lookup cache to use for lookup decoding (not updated by generation) - -lcd, --lookup-cache-dynamic FNAME - path to dynamic lookup cache to use for lookup decoding (updated by generation) - -c, --ctx-size N size of the prompt context (default: 0, 0 = loaded from model) - -n, --predict N number of tokens to predict (default: -1, -1 = infinity, -2 = until context filled) - -b, --batch-size N logical maximum batch size (default: 2048) -``` - - -## Install Hugging Face Hub - -There are a few different ways you can download the Llama-2-7B Chat model. In this Learning Path, you download the model from Hugging Face. - -{{% notice Note %}} Use of Llama-2-7B-Chat model is governed by the Meta license. Before you proceed to download the model, please visit the Llama [website](https://llama.meta.com/llama-downloads/) and fill in the form. {{% /notice %}} - -[Hugging Face](https://huggingface.co/) is an open source AI community where you can host your own AI models, train them and collaborate with others in the community. You can browse through the thousands of models that are available for a variety of use cases like NLP, audio, and computer vision. - -The `huggingface_hub` library provides APIs and tools that let you easily download and fine-tune pre-trained models. You will use `huggingface-cli` to download the [Llama-2-7B-Chat model](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF). - -Install the required Python packages: - -```bash -sudo apt install python-is-python3 python3-pip python3-venv -y -``` - -Create and activate a Python virtual environment: - -```bash -python -m venv venv -source venv/bin/activate -``` - -Your terminal prompt now has the `(venv)` prefix indicating the virtual environment is active. Use this virtual environment for the remaining commands. - -Install the `huggingface_hub` python library using `pip`: - -```bash -pip install huggingface_hub -``` - -You can now download the model using the huggingface cli: - -```bash -huggingface-cli download TheBloke/Llama-2-7b-Chat-GGUF llama-2-7b-chat.Q4_0.gguf --local-dir . --local-dir-use-symlinks False -``` -Before you proceed and run this model, take a quick look at what `Q4_0` in the model name denotes. - -## Quantization format - -`Q4_0` in the model name refers to the quantization method the model uses. The goal of quantization is to reduce the size of the model (to reduce the memory space required) and faster (to reduce memory bandwidth bottlenecks transferring large amounts of data from memory to a processor). The primary trade-off to keep in mind when reducing a model's size is maintaining quality of performance. Ideally, a model is quantized to meet size and speed requirements while not having a negative impact on performance. - -Llama 2 was originally trained and published using the bfloat16 data type, meaning that each of the 7 billion model parameters takes up 16 bits of memory to store. Putting that into real terms, multiplying 16 bits per parameter by 7 billion parameters, the base foundation llama-2-7b model is just over 13Gb in size. - -This model is `llama-2-7b-chat.Q4_0.gguf`, so what does each component mean in relation to the quantization level? The main thing to note is the number of bits per parameter, which is denoted by 'Q4' in this case or 4-bit integer. As a result, by only using 4 bits per parameter for 7 billion parameters, the model drops to be 3.6Gb in size. - -Here is a quick lookup to the rest of the quantization parts for the Llama-2 model family as it exists today: - -| quantization-method | # of bits per parameter | quantization format (does not apply to quantization method 'IQ') | quantization method specifics | -| ------------------- | ----------------------- | ---------------------------------------------------------------- | ------------------ | -| Q, IQ, F, FP | 2,3,4,5,6,7,8,16,32 | _0, _1, _K | _XXS, _XS, _S, _M, _L | - -Some examples: - -* Q8_0 --> Straightforward quantization method (indicated with _0 or _1), with an 8 bit integer per parameter. -* Q4_K_M --> K-quant method (indicated with _K), with a 4 bit integer per parameter, with the _M quantization mix type used. -* IQ2_XXS --> I-quant method (indicated with _IQ), with the _XXS quantization mix type used. -* F16 --> Using a 16 bit floating point number per parameter (no other quantization method used, only rounding a number if starting from a 32 bit floating point number). - -Each quantization method has a unique approach to quantizing parameters. The deeper technical details of different quantization methodologies are outside the scope of this guide. The main takeaway is that selecting the right model quantization is critical to running an LLM effectively on your hardware, and the most impactful quantization decision is the number of bits per parameter. You will need also need to check you have enough system memory before deploying larger models or models with higher precision/quantization. - -In this guide, you will not use any other quantization methods, because Arm has not made kernel optimizations for other quantization types. - -## Re-quantize the model weights - -To see improvements for Arm optimized kernels, you need to generate a new weights file with rearranged Q4_0 weights. As of [llama.cpp commit 0f1a39f3](https://github.com/ggerganov/llama.cpp/commit/0f1a39f3), Arm has contributed code for three types of GEMV/GEMM kernels corresponding to three processor types: - -* AWS Graviton2, where you only have NEON support (you will see less improvement for these GEMV/GEMM kernels), -* AWS Graviton3, where the GEMV/GEMM kernels exploit both SVE 256 and MATMUL INT8 support, and -* AWS Graviton4, where the GEMV/GEMM kernels exploit NEON/SVE 128 and MATMUL_INT8 support - -To re-quantize optimally for Graviton3, run - -```bash -./llama-quantize --allow-requantize llama-2-7b-chat.Q4_0.gguf llama-2-7b-chat.Q4_0_8_8.gguf Q4_0_8_8 -``` - -This will output a new file, `llama-2-7b-chat.Q4_0_8_8.gguf`, which contains reconfigured weights that allow `llama-cli` to use SVE 256 and MATMUL_INT8 support. - -{{% notice Note %}} -This requantization is optimal only for Graviton3. For Graviton2, requantization should optimally be done in `Q4_0_4_4` format, and for Graviton4, `Q4_0_4_8` is the optimal requantization format. -{{% /notice %}} - -## Compare the pre-quantized Llama-2-7B-Chat LLM model weights to the optimized weights - -First, run the pre-quantized llama-2-7b-chat model exactly as the weights were downloaded from huggingface: - -```bash -./llama-cli -m llama-2-7b-chat.Q4_0.gguf -p "Building a visually appealing website can be done in ten simple steps:" -n 64 -t 2 -``` - -This command will use the downloaded model (`-m` flag), with the specified prompt (`-p` flag), and target a 64 token completion (`-n` flag), using two threads (`-t` flag). - -You will see lots of interesting statistics being printed from llama.cpp about the model and the system, followed by the prompt and completion. The tail of the output from running this model on an AWS Graviton3 c7g.2xlarge instance is shown below: - -```output -llm_load_tensors: ggml ctx size = 0.14 MiB -llm_load_tensors: CPU buffer size = 3647.87 MiB -.................................................................................................. -llama_new_context_with_model: n_ctx = 4096 -llama_new_context_with_model: n_batch = 2048 -llama_new_context_with_model: n_ubatch = 512 -llama_new_context_with_model: flash_attn = 0 -llama_new_context_with_model: freq_base = 10000.0 -llama_new_context_with_model: freq_scale = 1 -llama_kv_cache_init: CPU KV buffer size = 2048.00 MiB -llama_new_context_with_model: KV self size = 2048.00 MiB, K (f16): 1024.00 MiB, V (f16): 1024.00 MiB -llama_new_context_with_model: CPU output buffer size = 0.12 MiB -llama_new_context_with_model: CPU compute buffer size = 296.01 MiB -llama_new_context_with_model: graph nodes = 1030 -llama_new_context_with_model: graph splits = 1 - -system_info: n_threads = 2 / 8 | AVX = 0 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 0 | NEON = 1 | SVE = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 0 | SSSE3 = 0 | VSX = 0 | MATMUL_INT8 = 1 | LLAMAFILE = 0 | -sampling: - repeat_last_n = 64, repeat_penalty = 1.000, frequency_penalty = 0.000, presence_penalty = 0.000 - top_k = 40, tfs_z = 1.000, top_p = 0.950, min_p = 0.050, typical_p = 1.000, temp = 0.800 - mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000 -sampling order: -CFG -> Penalties -> top_k -> tfs_z -> typical_p -> top_p -> min_p -> temperature -generate: n_ctx = 4096, n_batch = 2048, n_predict = 64, n_keep = 1 - - - Building a visually appealing website can be done in ten simple steps: - Einzeln, a UX/UI Designer at Designhill, provides a list of ten simple steps to create a visually appealing website. These steps include using high-quality images, choosing a consistent color scheme, and incorporating negative space. Additionally, Using a clean and simple layout, creating a clear hierarchy -llama_print_timings: load time = 1120.85 ms -llama_print_timings: sample time = 2.11 ms / 64 runs ( 0.03 ms per token, 30303.03 tokens per second) -llama_print_timings: prompt eval time = 1998.79 ms / 16 tokens ( 124.92 ms per token, 8.00 tokens per second) -llama_print_timings: eval time = 15991.48 ms / 63 runs ( 253.83 ms per token, 3.94 tokens per second) -llama_print_timings: total time = 17996.97 ms / 79 tokens -``` - -The `system_info` printed from llama.cpp highlights important architectural features present on your hardware that improve the performance of the model execution. In the output shown above from running on an AWS Graviton3 instance, you will see: - - * NEON = 1 This flag indicates support for Arm's Neon technology which is an implementation of the Advanced SIMD instructions - * ARM_FMA = 1 This flag indicates support for Arm Floating-point Multiply and Accumulate instructions - * MATMUL_INT8 = 1 This flag indicates support for Arm int8 matrix multiplication instructions - * SVE = 1 This flag indicates support for the Arm Scalable Vector Extension - - -The end of the output shows several model timings: - -* load time refers to the time taken to load the model. -* prompt eval time refers to the time taken to process the prompt before generating the new text. In this example, it shows that it evaluated 16 tokens in 1998.79 ms. -* eval time refers to the time taken to generate the output. Generally anything above 10 tokens per second is faster than what humans can read. - -You can compare these timings to the optimized model weights by running: - -```bash -./llama-cli -m llama-2-7b-chat.Q4_0_8_8.gguf -p "Building a visually appealing website can be done in ten simple steps:" -n 64 -t 2 -``` - -This is the same command as before, but with the model file swapped out for the re-quantized file. - -The timings on this one look like: - -``` -llama_print_timings: load time = 984.78 ms -llama_print_timings: sample time = 2.12 ms / 64 runs ( 0.03 ms per token, 30245.75 tokens per second) -llama_print_timings: prompt eval time = 463.98 ms / 16 tokens ( 29.00 ms per token, 34.48 tokens per second) -llama_print_timings: eval time = 6890.95 ms / 63 runs ( 109.38 ms per token, 9.14 tokens per second) -llama_print_timings: total time = 7362.13 ms / 79 tokens -``` - -As you can see, load time improves, but the biggest improvement can be seen in eval times. The number of tokens per second for prompt eval quadruples, while the speed of inference more than doubles. - -You have successfully run a LLM chatbot with Arm optimizations, all running on your Arm AArch64 CPU on your server. You can continue experimenting and trying out the model with different prompts. - diff --git a/content/learning-paths/servers-and-cloud-computing/aaademo-kubectl-arm/kubernetes_diagram.png b/content/learning-paths/servers-and-cloud-computing/aaademo-kubectl-arm/kubernetes_diagram.png deleted file mode 100644 index aa465ab34a..0000000000 Binary files a/content/learning-paths/servers-and-cloud-computing/aaademo-kubectl-arm/kubernetes_diagram.png and /dev/null differ diff --git a/content/learning-paths/servers-and-cloud-computing/aaaademo-audio/_demo.md b/content/learning-paths/servers-and-cloud-computing/whisper/_demo.md similarity index 66% rename from content/learning-paths/servers-and-cloud-computing/aaaademo-audio/_demo.md rename to content/learning-paths/servers-and-cloud-computing/whisper/_demo.md index 6fe60611a0..8744151484 100644 --- a/content/learning-paths/servers-and-cloud-computing/aaaademo-audio/_demo.md +++ b/content/learning-paths/servers-and-cloud-computing/whisper/_demo.md @@ -1,17 +1,13 @@ --- title: Demo - Audio transcription on Arm overview: | - This Arm Kleidi learning path shows how to use a single AWS Graviton instance -- powered by an Arm Neoverse CPU -- to build a simple “Token as a Service” server, used below to provide a chat-bot to serve a small number of concurrent users. - - This architecture would be suitable for businesses looking to deploy the latest Generative AI technologies using their existing CPU compute capacity and deployment pipelines. The demo uses the open source llama.cpp framework, which Arm has enhanced by contributing the latest Arm Kleidi Technologies. Further optimizations are achieved by using the smaller 8 billion parameter Llama 3.1 model, which has been quantized to optimize memory usage. - - Chat with the Llama-3.1-8B LLM below to see the performance for yourself, then follow the learning path to build your own Generative AI service on Arm Neoverse. + Insert helpful overview here. demo_steps: - Record your voice (giving mic permissions to your browser). - - Review and send to the LLM. + - Review and send to _________________insert technical thing here_____________. - Get transcription and view stats. diff --git a/content/learning-paths/servers-and-cloud-computing/aaaademo-audio/audio-pic-clearer.png b/content/learning-paths/servers-and-cloud-computing/whisper/audio-pic-clearer.png similarity index 100% rename from content/learning-paths/servers-and-cloud-computing/aaaademo-audio/audio-pic-clearer.png rename to content/learning-paths/servers-and-cloud-computing/whisper/audio-pic-clearer.png diff --git a/content/learning-paths/servers-and-cloud-computing/aaaademo-audio/audio-pic.png b/content/learning-paths/servers-and-cloud-computing/whisper/audio-pic.png similarity index 100% rename from content/learning-paths/servers-and-cloud-computing/aaaademo-audio/audio-pic.png rename to content/learning-paths/servers-and-cloud-computing/whisper/audio-pic.png diff --git a/content/learning-paths/servers-and-cloud-computing/aaaademo-audio/demo-terms-and-conditions.txt b/content/learning-paths/servers-and-cloud-computing/whisper/demo-terms-and-conditions.txt similarity index 100% rename from content/learning-paths/servers-and-cloud-computing/aaaademo-audio/demo-terms-and-conditions.txt rename to content/learning-paths/servers-and-cloud-computing/whisper/demo-terms-and-conditions.txt diff --git a/themes/arm-design-system-hugo-theme/layouts/partials/demo-components/llm-voice-transcriber/javascript--llm-voice-transcriber.html b/themes/arm-design-system-hugo-theme/layouts/partials/demo-components/llm-voice-transcriber/javascript--llm-voice-transcriber.html index ece77a4879..d3889e46a0 100644 --- a/themes/arm-design-system-hugo-theme/layouts/partials/demo-components/llm-voice-transcriber/javascript--llm-voice-transcriber.html +++ b/themes/arm-design-system-hugo-theme/layouts/partials/demo-components/llm-voice-transcriber/javascript--llm-voice-transcriber.html @@ -345,12 +345,10 @@ // Set timeout const controller = new AbortController(); - const timeoutId = setTimeout(() => controller.abort(), 2000); // 20 seconds timeout + const timeoutId = setTimeout(() => controller.abort(), 20000); // 20 seconds timeout console.log('Sending audio to server...'); - console.log('--------------------------'); - console.log('Server location: ',atob(server_location_encoded)); - + console.log('--------------------------'); const stripped_data = data.split(",")[1]; console.log('Data: ',stripped_data); @@ -392,18 +390,22 @@ if (error.name === 'AbortError') { console.error('Timeout error: Please check your internet connection and try again.',error); showPopupPostConnection('Server timeout - refresh the page and try again.',"error",true); + submit_btn.disabled = 'false'; } else if (error.name === 'NetworkError') { console.error('Network error: Please check your internet connection and try again.',error); showPopupPostConnection('Network error - refresh the page and try again.',"error",true); + submit_btn.disabled = 'false'; } else if (error.name === 'TypeError') { console.error('Input stream error: There was a problem with the data stream.',error); showPopupPostConnection('Stream interrupted - try sending your audio again.',"warning"); + submit_btn.disabled = 'false'; } else { console.error('An unexpected error occurred:', error); showPopupPostConnection('Server error - try sending your audio again.',"error"); + submit_btn.disabled = 'false'; } }); }