(It's set up to not use Tensorflow by default. The model can be. The huggingface_hub library offers two ways to assist you with creating repositories and uploading files: create_repo creates a repository on the Hub. 🤗 Transformers Quick tour Installation. Accelerate. On Colab, run the following line to. The following is a list of the common parameters that should be modified based on your use cases: pretrained_model_name_or_path — Path to pretrained model or model identifier from. XDG_CACHE_HOME. from sagemaker. Hugging Face is most notable for its Transformers library built for natural language processing applications and its platform that allows users to share machine learning models and datasets. Some of the models in the hf-hub under the Helsinki-NLP repo are listed under the apache 2. Download a single file. . The T5 model was presented in Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer by Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. I have several m/P 40 cards. I use the "20,22" memory split so that the display card has some room for the framebuffer to handle display. As the model needs 352GB in bf16 (bfloat16) weights ( 176*2 ), the most efficient set-up is 8x80GB A100 GPUs. 0625 GB/sec bandwidth in each direction between two GPUs. Installation. Follow these steps: Load a Pre-trained Model: Visit. License: Non-commercial license. 0 and was released in lllyasviel/ControlNet-v1-1 by Lvmin Zhang. Running on cpu upgrade2️⃣ Followed by a few practical examples illustrating how to introduce context into the conversation via a few-shot learning approach, using Langchain and HuggingFace. nlp data machine-learning api-rest datasets huggingface. 如果你正在使用Windows 或 macOS，你可以直接下载并解压RVC-beta. From external tools. Open-source version control system for Data Science and Machine Learning projects. This article shows how to get an incredibly fast per token throughput when generating with the 176B parameter BLOOM model. As seen below, I created an. 8-to-be + cuda-11. To allow the container to use 1G of Shared Memory and support SHM sharing, we add --shm-size 1g on the above command. Utilizing CentML's state-of-the-art machine learning optimization software and Oracle's Gen-2 cloud (OCI), the collaboration has achieved significant performance improvements for both training and inference tasks. Adding these tokens work but somehow the tokenizer always ignores the second whitespace. Communication: NCCL-communications network with a fully dedicated subnet. If nvlink connections are utilized, usage should go up during training. Reload to refresh your session. MT-NLG established the state-of-the-art results on the PiQA dev set and LAMBADA test set in all three settings (denoted by *) and outperform results among similar monolithic models in other categories. Some other cards may use a PCI-E 12-Pin connectors, and these can deliver up to 500-600W of power. Gets all the available model tags hosted in the Hub. 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links. The Megatron 530B model is one of the world’s largest LLMs, with 530 billion parameters based on the GPT-3 architecture. You switched accounts on another tab or window. bat以启动WebUI，后者则运行命令sh . Discover pre-trained models and datasets for your projects or play with the thousands of machine learning apps hosted on the Hub. It is, to the best of our knowledge, the largest dense autoregressive model that has publicly available weights at the time of. We fine-tuned StarCoderBase. Good to hear there's still hope. Clearly we need something smarter. The “Fast” implementations allows:Saved searches Use saved searches to filter your results more quicklySuper-Resolution StableDiffusionUpscalePipeline The upscaler diffusion model was created by the researchers and engineers from CompVis, Stability AI, and LAION, as part of Stable Diffusion 2. Compared to deploying regular Hugging Face models, we first need to retrieve the container uri and provide it to our HuggingFaceModel model class with a image_uri pointing to the image. Model checkpoints will soon be available through HuggingFace and NGC, or for use through the service, including: T5: 3BHardware: 2x TITAN RTX 24GB each + NVlink with 2 NVLinks (NV2 in nvidia-smi topo -m) Software: pytorch-1. 0 / transformers==4. Incredibly Fast BLOOM Inference with DeepSpeed and Accelerate. 4 kB Add index 5 months ago; quantization. martin-ha/toxic-comment-model. I know a few people have suggested a standardized prompt format since there seems to be quite a few for the popular models. Here is the full benchmark code and outputs: Develop. com is the world's best emoji reference site, providing up-to-date and well-researched information you can trust. This command shows various information about nvlink including usage. This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model. I am trying to tune Wav2Vec2 Model with a dataset on my local device using my CPU (I don’t have a GPU or Google Colab pro), I am using this as my reference. Install with pip. A tokenizer is in charge of preparing the inputs for a model. dev0 DataLoader One of the important requirements to reach great training speed is the ability to feed the GPU at the maximum speed it can handle. The model is a causal (unidirectional) transformer pre-trained using language modeling on a large corpus with long range dependencies. It can be used in combination with Stable Diffusion, such as runwayml/stable-diffusion-v1-5. If you look. Take a first look at the Hub features. 34 about 1 month ago; tokenizer. distributed. I don't think the NVLink this is an option, and I'd love to hear your experience and plan on sharing mine as well. Transformers, DeepSpeed. 0. Linear(3, 4), nn. ; library_name (str, optional) — The name of the library to which the object corresponds. Hardware. . 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links. Here are some key points to consider: Use vLLM when maximum speed is required for batched prompt delivery. I have not found any information with regards to the 3090 NVLink memory pooling. t5-11b is 45GB in just model params significantly speed up training - finish training that would take a year in hours Each new generation provides a faster bandwidth, e. g. For 4-bit Llama you shouldn't be, unless you're training or finetuning, but in that case even 96 GB would be kind of low. . It provides information for anyone considering using the model or who is affected by the model. Some run like trash. ChatGLM2-6B 开源模型旨在与开源社区一起推动大模型技术发展，恳请开发者和大家遵守开源协议. Assuming you are the owner of that repo on the hub, you can locally clone the repo (in a local terminal):Parameters . We’re on a journey to advance and democratize artificial intelligence through open source and open science. Installation. This should only affect the llama 2 chat models, not the base ones which is where the fine tuning is usually done. For current SOTA models which have about a hundred layers (e. - show activity as N/A, although. GPUs: 288 A100 80GB GPUs with 8 GPUs per node (36 nodes) using NVLink 4 inter-gpu connects, 4 OmniPath links; Communication: NCCL-communications network with a fully dedicated subnet; Software Orchestration: Megatron-DeepSpeed; Optimizer & parallelism: DeepSpeed; Neural networks: PyTorch (pytorch-1. Below is the documentation for the HfApi class, which serves as a Python wrapper for the Hugging Face Hub’s API. Example. You can then use the huggingface-cli login command in. Hi, You can just add as many files as you’d like. TP is almost always used within a single node. 🤗 Accelerate was created for PyTorch users who like to write the training loop of PyTorch models but are reluctant to write and maintain the boilerplate code needed to use multi-GPUs/TPU/fp16. g. The. LIDA is grammar agnostic (will work with any programming language and visualization libraries e. Specify the license. In order to keep the package minimal by default, huggingface_hub comes with optional dependencies useful for some use cases. Includes multi-GPUs support. names. I was actually the who added the ability for that tool to output q8_0 — what I was thinking is that for someone who just wants to do stuff like test different quantizations, etc being able to keep a nearly. Hugging Face datasets supports loading from Spark DataFrames using datasets. You switched accounts on another tab or window. inception_resnet_v2. How would I send data to GPU with and without pipeline? Any advise is highly appreciated. It was trained on 384 GPUs. 学習済 LLM (大規模言語モデル)のパラメータ数と食うメモリ容量（予想含む）、ホストできるGPUを調べたメモ ※適宜修正、拡充していく。. Model. In order to share data between the different devices of a NCCL group, NCCL might fall back to using the host memory if peer-to-peer using NVLink or PCI is not possible. bin] and install fasttext package. As AI has become a critical part of every application, this partnership has felt like a natural match to put tools in the hands of developers to make deploying AI easy and affordable. JumpStart supports task-specific models across fifteen of the most popular problem types. Listen. Falcon is a 40 billion parameters autoregressive decoder-only model trained on 1 trillion tokens. Inter-node connect: Omni-Path Architecture (OPA). This is a good setup for large-scale industry workflows, e. GPU memory: 640GB per node. 3. Some run great. 5 billion in a $235-million funding round backed by technology heavyweights, including Salesforce , Alphabet's Google and Nvidia . 2,24" to put 17. huggingface. In this blog post, we'll walk through the steps to install and use the Hugging Face Unity API. pretrained_model_name (str or os. We have an HD model ready that can be used commercially. So for consumers, I cannot recommend buying. co/new: Specify the owner of the repository: this can be either you or any of the organizations you’re affiliated with. it's usable. CPU: AMD. Along the way, you'll learn how to use the Hugging Face ecosystem — 🤗 Transformers, 🤗 Datasets, 🤗 Tokenizers, and 🤗 Accelerate — as well as. 🤗 Accelerate is a library that enables the same PyTorch code to be run across any distributed configuration by adding just four lines of code! In short, training and inference at scale made simple, efficient and adaptable. Using the root method is more straightforward but the HfApi class gives you more flexibility. Best to experiment to find the winner on your particular setup. here is a quote from Nvidia Ampere GA102 GPU Architecture: Third-Generation NVLink® GA102 GPUs utilize NVIDIA’s third-generation NVLink interface, which includes four x4 links, Learn More. Get started. list_datasets (): To load a dataset from the Hub we use the datasets. The Nvidia system provides 32 petaflops of FP8 performance. Llama 2 is a family of state-of-the-art open-access large language models released by Meta today, and we’re excited to fully support the launch with comprehensive integration in Hugging Face. ago. 2. CPU memory: 512GB per node. You can supply your HF API token ( hf. We used the Noam learning rate sched-uler with 16000 warm-up steps. What you get: 8 x NVIDIA A100 GPUs with 40 GB GPU memory per GPU. py tool is mostly just for converting models in other formats (like HuggingFace) to one that other GGML tools can deal with. This article will break down how it works and what it means for the future of graphics. 0 78244:78465 [0] NCCL INFO Call to connect returned Connection timed. When you have fast inter-node connectivity (e. GPU inference. Jul. For the base model, this is controlled by the denoising_end parameter and for the refiner model, it is controlled by the denoising_start parameter. This command performs a magical link between the folder you cloned the repository to and your python library paths, and it’ll look inside this folder in addition to the normal library-wide paths. After that, click on “Submit”. The AMD Infinity Architecture Platform sounds similar to Nvidia’s DGX H100, which has eight H100 GPUs and 640GB of GPU memory, and overall 2TB of memory in a system. distributed. py --output_path models/faiss_flat_index. Final thoughts :78244:78244 [0] NCCL INFO NET/Plugin : No plugin found (libnccl-net. It is. It appears that two of the links between the GPUs are responding as inactive as shown in the nvidia-smi nv-link status shown below. Transformers, DeepSpeed. Framework. Please use the forums for questions like this as we keep issues for bugs and feature requests only. 1 (note the difference in ETA is just because 3. co/settings/token) with this command: Cmd/Ctrl+Shift+P to open VSCode command palette. 🤗 Accelerate is a library that enables the same PyTorch code to be run across any distributed configuration by adding just four lines of code! In short, training and inference at scale made simple, efficient and adaptable. . Tutorials. Lightning, DeepSpeed. There is a similar issue here: pytorch summary fails with huggingface model II: Expected all tensors to be on the same device, but found at least two devices, cuda:0 and cpu. This extension is for AUTOMATIC1111's Stable Diffusion web UI, allows the Web UI to add ControlNet to the original Stable Diffusion model to generate images. StableDiffusionUpscalePipeline can be used to enhance the resolution of input images by a factor of 4. If you are. Unfortunately I discovered that with larger models the GPU-GPU communication overhead can be prohibitive (most of the cluster nodes only support P2P GPU communication over PCIe, which is a lot slower than NVLink), and Huggingface's implementation actually performed worse on multiple GPUs than on two 3090s with NVLink (I opened an issue. g. Open-source version control system for Data Science and Machine Learning projects. We’re on a journey to advance and democratize artificial intelligence through open source and open science. AI stable-diffusion model v2 with a simple web interface. The main advantage of doing this for big models is that during step 2 of the workflow shown above, each shard of the checkpoint is loaded after the previous one, capping the. In panoptic segmentation, the final prediction contains 2 things: a segmentation map of shape (height, width) where each value encodes the instance ID of a given pixel, as well as a corresponding segments_info. 3. flat index; hnsw (approximate search) index; To build and save FAISS (exact search) index yourself, run python blink/[email protected] . py. g. cc:63 NCCL WARN Failed to open libibverbs. Module object from nn. Dataset. 11 w/ CUDA-11. ; Scalar ServerPCIe server with up to 8x customizable NVIDIA Tensor Core GPUs and dual Xeon or AMD EPYC. No problem. This name is used for multiple purposes, so keep track of it. TGI implements many features, such as: ARMONK, N. NVSwitch connects multiple NVLinks to provide all-to-all GPU communication at full NVLink speed within a single node and between nodes. What is NVLink, and is it useful? Generally, NVLink is not useful. RTX 3080: 760. If Git support is enabled, then entry_point and source_dir should be relative paths in the Git repo if provided. The old ones: RTX 3090: 936. 2:03. The TL;DR. A day after Salesforce CEO Marc Benioff jumped the gun with a post on X saying the company’s venture arm was “thrilled to lead” a new round of financing, Hugging Face has. However, one can also add multiple embedding vectors for the placeholder token to increase the number of fine-tuneable parameters. Already have an account? Log in. Each new generation provides a faster bandwidth, e. When you have fast intranode connectivity like NVLink as compared to PCIe usually the comms overhead is lower and then compute dominates and gpus excel at what they do - fast results. yaml config file from Huggingface. 7. english-gpt2 = your downloaded model name. Installation. So the same limitations apply and in particular, without an NVLink, you will get slower speed indeed. Yes absolutely. Fine-tune GPT-J-6B with Ray Train and DeepSpeed. Valid model ids can be located at the root-level, like bert-base-uncased, or namespaced under a user or organization name, like dbmdz/bert-base-german-cased. GPUs: 64 A100 80GB GPUs with 8 GPUs per node (8 nodes) using NVLink 4 inter-gpu connects, 4 OmniPath links. Automatic models search and training. • 4 mo. Valid model ids can be located at the root-level, like bert-base-uncased, or namespaced under a user or organization name, like dbmdz/bert-base-german-cased. This is the most common setup for researchers and small-scale industry workflows. here is. . 0. "<cat-toy>". a string, the model id of a pretrained model configuration hosted inside a model repo on huggingface. like 6. The most common and practical way to control which GPU to use is to set the CUDA_VISIBLE_DEVICES environment variable. Org profile for NVIDIA on Hugging Face, the AI community building the future. HuggingFace. when comms are slow then the gpus idle a lot - slow results. Images generated with text prompt = “Portrait of happy dog, close up,” using the HuggingFace Diffusers text-to-image model with batch size = 1, number of iterations = 25, float16 precision, DPM Solver Multistep Scheduler, Catalyst Fast. 45. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. AI startup has raised $235 million in a Series D funding round, as first reported by The Information, then seemingly verified by Salesforce CEO Marc Benioff on X (formerly known as Twitter). . 7 kB Init commit 5 months ago; tokenization_chatglm. 8+. Introducing MPT-7B, the first entry in our MosaicML Foundation Series. PyTorch transformer (HuggingFace,2019). 24xlarge When to use it: When you need all the performance you can get. Native support for models from HuggingFace — Easily run your own model or use any of the HuggingFace Model Hub. 8-to-be + cuda-11. Usage (HuggingFace Transformers) Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. env. In particular, you. co Join the Hugging Face community and get access to the augmented documentation experience Collaborate on models, datasets and Spaces Faster examples with accelerated inference Switch between documentation themes to get started Performance and Scalability Training large transformer models and deploying them to production present various challenges. Most of the tokenizers are available in two flavors: a full python implementation and a “Fast” implementation based on the Rust library tokenizers. 1 and 4. Introduction to 3D Gaussian Splatting . Also 2x8x40GB A100s or. Phind-CodeLlama-34B-v2. training/evaluation) built upon the Huggingface PyTorch transformer (HuggingFace,2019). NVLink is a high speed interconnect between GPUs. Harness the power of machine learning while staying out of MLOps!🤗 Datasets is a lightweight library providing two main features:. Hugging Face is more than an emoji: it's an open source data science and machine learning platform. Join the community of machine learners! Hint: Use your organization email to easily find and join your company/team org. An MacBook Pro with M2 Max can be fitted with 96 GB memory, using a 512-bit Quad Channel LPDDR5-6400 configuration for 409. Additionally you want the high-end PSU that has stable. 🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning. Join Hugging Face. Advanced. tail-recursion. Credits ; ContentVec ; VITS ; HIFIGAN ; Gradio ; FFmpeg ; Ultimate Vocal Remover ; audio-slicer ; Vocal pitch extraction:RMVPE ; The pretrained model is trained and tested by yxlllc and RVC-Boss. Different from BERT and encoder-decoder structure, GPT receive some input ids as context, and generates the respective output ids as response. Inference with text-generation-webui works with 65b-4bit and two x090 24GB nvidia cards. Each new generation provides a faster bandwidth, e. Here is the full benchmark code and outputs: Run with two GPUs, NVLink disabled: NCCL_P2P_DISABLE=1 python train_csrc. To extract image features with this model, follow the timm feature extraction examples, just change the name of the model you want to use. g. Optional Arguments:--config_file CONFIG_FILE (str) — The path to use to store the config file. You signed in with another tab or window. For full details of this model please read our paper and release blog post. Run inference with pipelines Write portable code with AutoClass Preprocess data Fine-tune a pretrained model Train with a script Set up distributed training with 🤗 Accelerate Load and train adapters with 🤗 PEFT Share your model Agents Generation with LLMs. Fine-tune GPT-J-6B with Ray Train and DeepSpeed. Choose your model on the Hugging Face Hub, and, in order of precedence, you can either: Set the LLM_NVIM_MODEL environment variable. As the size and complexity of large language models (LLMs) continue to grow, NVIDIA is today announcing updates to the that provide training speed-ups of up to 30%. models, also with Git-based version control; datasets, mainly in text, images, and audio; web applications ("spaces" and "widgets"), intended for small-scale demos of machine learning. To include DeepSpeed in a job using the HuggingFace Trainer class, simply include the argument --deepspeed ds_config. GPU memory: 640GB per node. Note that. get_model_tags(). coI use the stable-diffusion-v1-5 model to render the images using the DDIM Sampler, 30 Steps and 512x512 resolution. Transformers, DeepSpeed. g. We used. list_metrics()) e. State-of-the-art ML for Pytorch, TensorFlow, and JAX. Catalyst Fast. ago. Use BLINK. bin with huggingface_hub 5 months ago; pytorch_model. Accelerate, DeepSpeed. Each new generation provides a faster bandwidth, e. Training. As this process can be compute-intensive, running on a dedicated server can be an interesting option. The real difference will depend on how much data each GPU needs to sync with the others - the more there is to sync, the more a slow link will slow down the total runtime. To use the specific GPU's by setting OS environment variable: Before executing the program, set CUDA_VISIBLE_DEVICES variable as follows: export CUDA_VISIBLE_DEVICES=1,3 (Assuming you want to select 2nd and 4th GPU) Then, within program, you can just use DataParallel () as though you want to use all the GPUs. Upload the new model to the Hub. Hyperplane ServerNVIDIA Tensor Core GPU server with up to 8x A100 or H100 GPUs, NVLink, NVSwitch, and InfiniBand. ai Hugging Face Keras LightGBM MMCV Optuna PyTorch PyTorch Lightning Scikit-learn TensorFlow XGBoost Ultralytics YOLO v8. Install with pip. ZeRO-Inference offers scaling benefits in two ways. HF API token. It's the current state-of-the-art amongst open-source models. It provides information for anyone considering using the model or who is affected by the model. 8-to-be + cuda-11. 3D Gaussian Splatting is a rasterization technique described in 3D Gaussian Splatting for Real-Time Radiance Field Rendering that allows real-time rendering of photorealistic scenes learned from small samples of images. 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links ; CPU: AMD EPYC 7543 32-Core. 16, 2023. Its usage may incur costs. ; library_version (str, optional) — The version of the library. The fine-tuning script is based on this Colab notebook from Huggingface's blog: The Falcon has landed in the Hugging Face ecosystem. 8+. So yeah, i would not expect the new chips to be significantly better in a lot of tasks. Y. An additional level of debug is to add NCCL_DEBUG=INFO environment variable as follows: NCCL_DEBUG=INFO python -m torch. Inference. You will find a lot more details inside the diagnostics script and even a recipe to how you could run it in a SLURM environment. The original implementation requires about 16GB to 24GB in order to fine-tune the model. NO_COLOR. NVLink is a wire-based serial multi-lane near-range communications link developed by Nvidia. Q4_K_M. When training a style I use "artwork style" as the prompt. GQA (Grouped Query Attention) - allowing faster inference and lower cache size. ; A. Code 2. Use it for distributed training on large models and datasets. I don't think the NVLink this is an option, and I'd love to hear your experience and plan on sharing mine as well. 0. Thus in essence. The datacenter AI market is a vast opportunity for AMD, Su said. 🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch. The returned filepath is a pointer to the HF local cache. g. The ControlNet learns task-specific conditions in an end-to-end way, and the learning is robust even when the training dataset is small (< 50k). GPUs, storage, and InfiniBand networking. Images generated with text prompt = “Portrait of happy dog, close up,” using the HuggingFace Diffusers text-to-image model with batch size = 1, number of iterations = 25, float16 precision, DPM Solver Multistep Scheduler,In order to share data between the different devices of a NCCL group, NCCL might fall back to using the host memory ifpeer-to-peer using NVLink or PCI is not possible. py. Programmatic access. Finetuned from model: LLaMA. split='train[:100]+validation[:100]' will create a split from the first 100. here is a quote from Nvidia Ampere GA102 GPU Architecture: Third-Generation NVLink® GA102 GPUs utilize NVIDIA’s third-generation NVLink interface, which includes four x4 links,HuggingFace Diffusers library,12 were launched, queried, and benchmarked on a PowerEdge XE9680 server. 🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning. Now that your environment is set up, you can load and utilize Hugging Face models within your code. Based on the latest NVIDIA Ampere architecture. Huggingface. NVLink. 0. and DGX-1 server - NVLINK is not activated by DeepSpeed. text-generation-inference make use of NCCL to enable Tensor Parallelism to dramatically speed up inference for large language models. ai Hugging Face Keras LightGBM MMCV Optuna PyTorch PyTorch Lightning Scikit-learn TensorFlow XGBoost Ultralytics YOLO v8. Documentations. 🤗 Transformers can be installed using conda as follows: conda install-c huggingface transformers. Sequential( nn. 27,720. This article shows you how to use Hugging Face Transformers for natural language processing (NLP) model inference. 概要. It is unclear if NVIDIA will be able to keep its spot as the main deep learning hardware vendor in 2018 and both AMD and Intel Nervana will have a shot at overtaking NVIDIA. Mathematically this is calculated using entropy. This command shows various information about nvlink including usage. pkl 3. g. 🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch. . Synopsis: This is to demonstrate and articulate how easy it is to deal with your NLP datasets using the Hugginfaces Datasets Library than the old traditional complex ways. The response is paginated, use the Link header to get the next pages. - GitHub - pytorch/benchmark: TorchBench is a collection of open source benchmarks used to evaluate PyTorch performance. ; Opt for Text generation inference if you need native HuggingFace support and don’t plan to use multiple adapters for the core model. Framework. ; user_agent (dict, str, optional) — The user-agent info in the form of a. co. 2. . Four links provide 56. <class_names. Then save the settings and reload the model with them. This will also be the name of the repository. For a quick performance test, I would recommend to run the nccl-tests and also verify the connections between the GPUs via nvidia-smi topo -m. Follow the installation pages of TensorFlow, PyTorch or Flax to see how to install them with conda. Our youtube channel features tuto. ZeRO-Inference offers scaling benefits in two ways. Accelerate is a HuggingFace library that simplifies PyTorch code adaptation for. When you create an HuggingFace Estimator, you can specify a training script that is stored in a GitHub repository as the entry point for the estimator, so that you don’t have to download the scripts locally. . Accelerate. Drag and drop an image into controlnet, select IP-Adapter, and use the "ip-adapter-plus-face_sd15" file that you downloaded as the model.