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Personal Copilot: Train Your Own Coding Assistant
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smangrul
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October 27, 2023
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personal-copilot
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bigcode, llm, nlp, inference, guide
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https://huggingface.co/blog/personal-copilot
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Setting up the VS Code Extension Just follow the installation steps. In the settings, replace the endpoint in the field below, so it points to the HF Inference Endpoint you deployed. Usage will look like below: # Finetuning your own Code Chat Assistant So far, the models we trained were specifically trained as personal co-pilot for code completion tasks. They aren't trained to carry out conversations or for question answering. Octocoder and StarChat are great examples of such models. This section briefly describes how to achieve that. Resources 1. Codebase: link. It uses the recently added Flash Attention V2 support in Transformers. 2. Colab notebook: link. Make sure to choose A100 GPU with High RAM setting. 3. Model: bigcode/stacoderplus 4. Dataset: smangrul/code-chat-assistant-v1. Mix of LIMA+GUANACO with proper formatting in a ready-to-train format. 5. Trained Model: smangrul/peft-lora-starcoderplus-chat-asst-A100-40GB-colab # Dance of LoRAs If you have dabbled with Stable Diffusion models and LoRAs for making your own Dreambooth models, you might be familiar with the concepts of combining different LoRAs with different weights, using a LoRA model with a different base model than the one on which it was trained. In text/code domain, this remains unexplored territory. We carry out experiments in this regard and have observed very promising findings. Are you ready? Let's go! 🚀
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] |
Personal Copilot: Train Your Own Coding Assistant
|
smangrul
|
October 27, 2023
|
personal-copilot
|
bigcode, llm, nlp, inference, guide
|
https://huggingface.co/blog/personal-copilot
|
Setting an Inference Endpoint Below are the screenshots with the steps we followed to create our custom Inference Endpoint. We used our QLoRA model, exported as a full-sized merged model that can be easily loaded in transformers.
|
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] |
Personal Copilot: Train Your Own Coding Assistant
|
smangrul
|
October 27, 2023
|
personal-copilot
|
bigcode, llm, nlp, inference, guide
|
https://huggingface.co/blog/personal-copilot
|
Transfer LoRAs to different base models We can also transfer the LoRA models to different base models. We will take the hot-off-the-press Octocoder model and apply on it the LoRA we trained above with starcoder base model. Please go through the following notebook PEFT_Personal_Code_CoPilot_Adapter_Transfer_Octocoder.ipynb for the entire code. Performance on the Code Completion task We can observe that octocoder is performing great. It is able to complete HF specific code snippets. It is also able to complete generic code snippets as seen in the notebook. Performance on the Chatting/QA task As Octocoder is trained to answer questions and carry out conversations about coding, let's see if it can use our LoRA adapter to answer HF specific questions. Yay! It correctly answers in detail how to create LoraConfig and related peft model along with correctly using the model name, dataset name as well as param values of LoraConfig. On disabling the adapter, it fails to correctly use the API of LoraConfig or to create a PEFT model, suggesting that it isn't part of the training data of Octocoder. # How do I run it locally? I know, after all this, you want to finetune starcoder on your codebase and use it locally on your consumer hardware such as Mac laptops with M1 GPUs, windows with RTX 4090/3090 GPUs ... Don't worry, we have got you covered. We will be using this super cool open source library mlc-llm 🔥. Specifically, we will be using this fork pacman100/mlc-llm which has changes to get it working with the Hugging Face Code Completion extension for VS Code. On my Mac latop with M1 Metal GPU, the 15B model was painfully slow. Hence, we will go small and train a PEFT LoRA version as well as a full finetuned version of bigcode/starcoderbase-1b. The training colab notebooks are linked below: 1. Colab notebook for Full fine-tuning and PEFT LoRA finetuning of starcoderbase-1b: link The training loss, evaluation loss as well as learning rate schedules are plotted below: Now, we will look at detailed steps for locally hosting the merged model smangrul/starcoder1B-v2-personal-copilot-merged and using it with 🤗 llm-vscode VS Code Extension. 1. Clone the repo git clone --recursive https://github.com/pacman100/mlc-llm.git && cd mlc-llm/ 2. Install the mlc-ai and mlc-chat (in editable mode) : pip install --pre --force-reinstall mlc-ai-nightly mlc-chat-nightly -f https://mlc.ai/wheels cd python pip uninstall mlc-chat-nightly pip install -e "." 3. Compile the model via: time python3 -m mlc_llm.build --hf-path smangrul/starcoder1B-v2-personal-copilot-merged --target metal --use-cache=0 4. Update the config with the following values in dist/starcoder1B-v2-personal-copilot-merged-q4f16_1/params/mlc-chat-config.json: diff { "model_lib": "starcoder7B-personal-copilot-merged-q4f16_1", "local_id": "starcoder7B-personal-copilot-merged-q4f16_1", "conv_template": "code_gpt", - "temperature": 0.7, + "temperature": 0.2, - "repetition_penalty": 1.0, "top_p": 0.95, - "mean_gen_len": 128, + "mean_gen_len": 64, - "max_gen_len": 512, + "max_gen_len": 64, "shift_fill_factor": 0.3, "tokenizer_files": [ "tokenizer.json", "merges.txt", "vocab.json" ], "model_category": "gpt_bigcode", "model_name": "starcoder1B-v2-personal-copilot-merged" } 5. Run the local server: python -m mlc_chat.rest --model dist/starcoder1B-v2-personal-copilot-merged-q4f16_1/params --lib-path dist/starcoder1B-v2-personal-copilot-merged-q4f16_1/starcoder1B-v2-personal-copilot-merged-q4f16_1-metal.so 6. Change the endpoint of HF Code Completion extension in VS Code to point to the local server: 7. Open a new file in VS code, paste the code below and have the cursor in-between the doc quotes, so that the model tries to infill the doc string: Voila! ⭐️ The demo at the start of this post is this 1B model running locally on my Mac laptop.
|
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] |
Personal Copilot: Train Your Own Coding Assistant
|
smangrul
|
October 27, 2023
|
personal-copilot
|
bigcode, llm, nlp, inference, guide
|
https://huggingface.co/blog/personal-copilot
|
Let us now consider the code-completion task. On disabling adapters, we observe that the code completion for the generic two-sum works as expected. However, the HF code completion fails with wrong params to LoraConfig, because the base model hasn't seen it in its pretraining data. Enabling assistant performs similar to the disabled case as it was trained on natural language conversations which didn't have any Hugging Face code repos. Now, let's enable the copilot adapter. We can observe that the copilot adapter gets it right in both cases. Therefore, it performs as expected for code-completions when working with HF specific codebase as well as generic codebases. Now, as a user, I want to combine the ability of assistant as well as copilot. This will enable me to use it for code completion while coding in an IDE, and also have it as a chatbot to answer my questions regarding APIs, classes, methods, documentation. It should be able to provide answers to questions like How do I use x, Please write a code snippet for Y on my codebase. PEFT allows you to do it via add_weighted_adapter. Let's create a new adapter code_buddy with equal weights to assistant and copilot adapters. Combining Multiple Adapters Now, let's see how code_buddy performs on the chatting/question_answering tasks. We can observe that code_buddy is performing much better than the assistant or copilot adapters alone! It is able to answer the write a code snippet request to show how to use a specific HF repo API. However, it is also hallucinating the wrong links/explanations, which remains an open challenge for LLMs. Below is the performance of code_buddy on code completion tasks. We can observe that code_buddy is performing on par with copilot, which was specifically finetuned for this task.
|
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] |
Personal Copilot: Train Your Own Coding Assistant
|
smangrul
|
October 27, 2023
|
personal-copilot
|
bigcode, llm, nlp, inference, guide
|
https://huggingface.co/blog/personal-copilot
|
Acknowledgements We would like to thank Pedro Cuenca, Leandro von Werra, Benjamin Bossan, Sylvain Gugger and Loubna Ben Allal for their help with the writing of this blogpost.
|
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] |
Personal Copilot: Train Your Own Coding Assistant
|
smangrul
|
October 27, 2023
|
personal-copilot
|
bigcode, llm, nlp, inference, guide
|
https://huggingface.co/blog/personal-copilot
|
First, let us consider the chatting/QA task. If we disable adapters, we observe that the task fails for both datasets, as the base model (starcoder) is only meant for code completion and not suitable for chatting/question-answering. Enabling copilot adapter performs similar to the disabled case because this LoRA was also specifically fine-tuned for code-completion. Now, let's enable the assistant adapter. Question Answering based on generic code Question Answering based on HF code We can observe that generic question regarding scrapy is being answered properly. However, it is failing for the HF code related question which wasn't part of its pretraining data.
|
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] |
Personal Copilot: Train Your Own Coding Assistant
|
smangrul
|
October 27, 2023
|
personal-copilot
|
bigcode, llm, nlp, inference, guide
|
https://huggingface.co/blog/personal-copilot
|
Finetuning your own Personal Co-Pilot In this section, we show how to fine-tune the following models: bigcode/starcoder (15.5B params), bigcode/starcoderbase-1b (1B params), Deci/DeciCoder-1b (1B params). We'll use a single A100 40GB Colab Notebook using 🤗 PEFT (Parameter-Efficient Fine-Tuning) for all the experiments. Additionally, we'll show how to fully finetune the bigcode/starcoder (15.5B params) on a machine with 8 A100 80GB GPUs using 🤗 Accelerate's FSDP integration. The training objective is fill in the middle (FIM), wherein parts of a training sequence are moved to the end, and the reordered sequence is predicted auto-regressively. Why PEFT? Full fine-tuning is expensive. Let’s have some numbers to put things in perspective: Minimum GPU memory required for full fine-tuning: 1. Weight: 2 bytes (Mixed-Precision training) 2. Weight gradient: 2 bytes 3. Optimizer state when using Adam: 4 bytes for original FP32 weight + 8 bytes for first and second moment estimates 4. Cost per parameter adding all of the above: 16 bytes per parameter 5. 15.5B model -> 248GB of GPU memory without even considering huge memory requirements for storing intermediate activations -> minimum 4X A100 80GB GPUs required Since the hardware requirements are huge, we'll be using parameter-efficient fine-tuning using QLoRA. Here are the minimal GPU memory requirements for fine-tuning StarCoder using QLoRA: > trainable params: 110,428,160 || all params: 15,627,884,544 || trainable%: 0.7066097761926236 1. Base model Weight: 0.5 bytes * 15.51B frozen params = 7.755 GB 2. Adapter weight: 2 bytes * 0.11B trainable params = 0.22GB 3. Weight gradient: 2 bytes * 0.11B trainable params = 0.12GB 4. Optimizer state when using Adam: 4 bytes * 0.11B trainable params * 3 = 1.32GB 5. Adding all of the above -> 9.51 GB ~10GB -> 1 A100 40GB GPU required 🤯. The reason for A100 40GB GPU is that the intermediate activations for long sequence lengths of 2048 and batch size of 4 for training lead to higher memory requirements. As we will see below, GPU memory required is 26GB which can be accommodated on A100 40GB GPU. Also, A100 GPUs have better compatibilty with Flash Attention 2. In the above calculations, we didn't consider memory required for intermediate activation checkpointing which is considerably huge. We leverage Flash Attention V2 and Gradient Checkpointing to overcome this issue. 1. For QLoRA along with flash attention V2 and gradient checkpointing, the total memory occupied by the model on a single A100 40GB GPU is 26 GB with a batch size of 4. 2. For full fine-tuning using FSDP along with Flash Attention V2 and Gradient Checkpointing, the memory occupied per GPU ranges between 70 GB to 77.6 GB with a per_gpu_batch_size of 1. Please refer to the model-memory-usage to easily calculate how much vRAM is needed to train and perform big model inference on a model hosted on the 🤗 Hugging Face Hub.
|
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] |
Personal Copilot: Train Your Own Coding Assistant
|
smangrul
|
October 27, 2023
|
personal-copilot
|
bigcode, llm, nlp, inference, guide
|
https://huggingface.co/blog/personal-copilot
|
How do I use it in VS Code? You can easily configure a custom code-completion LLM in VS Code using 🤗 llm-vscode VS Code Extension, together with hosting the model via 🤗 Inference EndPoints. We'll go through the required steps below. You can learn more details about deploying an endpoint in the inference endpoints documentation.
|
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] |
Personal Copilot: Train Your Own Coding Assistant
|
smangrul
|
October 27, 2023
|
personal-copilot
|
bigcode, llm, nlp, inference, guide
|
https://huggingface.co/blog/personal-copilot
|
Mix-and-Match LoRAs PEFT currently supports 3 ways of combining LoRA models, linear, svd and cat. For more details, refer to tuners#peft.LoraModel.add_weighted_adapter. Our notebook Dance_of_LoRAs.ipynb includes all the inference code and various LoRA loading combinations, like loading the chat assistant on top of starcoder instead of starcodeplus, which is the base model that we fine-tuned. Here, we will consider 2 abilities (chatting/QA and code-completion) on 2 data distributions (top 10 public hf codebase and generic codebase). That gives us 4 axes on which we'll carry out some qualitative evaluation analyses.
|
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] |
Personal Copilot: Train Your Own Coding Assistant
|
smangrul
|
October 27, 2023
|
personal-copilot
|
bigcode, llm, nlp, inference, guide
|
https://huggingface.co/blog/personal-copilot
|
# Personal Copilot: Train Your Own Coding Assistant In the ever-evolving landscape of programming and software development, the quest for efficiency and productivity has led to remarkable innovations. One such innovation is the emergence of code generation models such as Codex, StarCoder and Code Llama. These models have demonstrated remarkable capabilities in generating human-like code snippets, thereby showing immense potential as coding assistants. However, while these pre-trained models can perform impressively across a range of tasks, there's an exciting possibility lying just beyond the horizon: the ability to tailor a code generation model to your specific needs. Think of personalized coding assistants which could be leveraged at an enterprise scale. In this blog post we show how we created HugCoder 🤗, a code LLM fine-tuned on the code contents from the public repositories of the huggingface GitHub organization. We will discuss our data collection workflow, our training experiments, and some interesting results. This will enable you to create your own personal copilot based on your proprietary codebase. We will leave you with a couple of further extensions of this project for experimentation. Let’s begin 🚀
|
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] |
Personal Copilot: Train Your Own Coding Assistant
|
smangrul
|
October 27, 2023
|
personal-copilot
|
bigcode, llm, nlp, inference, guide
|
https://huggingface.co/blog/personal-copilot
|
Conclusion In this blog plost, we saw how to finetune starcoder to create a personal co-pilot that knows about our code. We called it 🤗 HugCoder, as we trained it on Hugging Face code :) After looking at the data collection workflow, we compared training using QLoRA vs full fine-tuning. We also experimented by combining different LoRAs, which is still an unexplored technique in the text/code domain. For deployment, we examined remote inference using 🤗 Inference Endpoints, and also showed on-device execution of a smaller model with VS Code and MLC. Please, let us know if you use these methods for your own codebase!
|
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] |
Creating open machine learning datasets? Share them on the Hugging Face Hub!
|
davanstrien
|
October 30, 2023
|
researcher-dataset-sharing
|
community, research, datasets, guide
|
https://huggingface.co/blog/researcher-dataset-sharing
|
# Creating open machine learning datasets? Share them on the Hugging Face Hub!
|
[
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] |
Creating open machine learning datasets? Share them on the Hugging Face Hub!
|
davanstrien
|
October 30, 2023
|
researcher-dataset-sharing
|
community, research, datasets, guide
|
https://huggingface.co/blog/researcher-dataset-sharing
|
Spotlight Spotlight is a tool that allows you to interactively explore datasets on the Hub with one line of code. You can learn more about how you can use this tool in this blog post.
|
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] |
Creating open machine learning datasets? Share them on the Hugging Face Hub!
|
davanstrien
|
October 30, 2023
|
researcher-dataset-sharing
|
community, research, datasets, guide
|
https://huggingface.co/blog/researcher-dataset-sharing
|
Tools for loading datasets hosted on the Hugging Face Hub Datasets shared on the Hugging Face Hub can be loaded via a variety of tools. The datasets library is a Python library which can directly load datasets from the huggingface hub via a load_dataset command. The datasets library is optimized for working with large datasets (including datasets which won't fit into memory) and supporting machine learning workflows. Alongside this many of the datasets on the Hub can also be loaded directly into Pandas, Polars, and DuckDB. This page provides a more detailed overview of the different ways you can load datasets from the Hub.
|
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] |
Creating open machine learning datasets? Share them on the Hugging Face Hub!
|
davanstrien
|
October 30, 2023
|
researcher-dataset-sharing
|
community, research, datasets, guide
|
https://huggingface.co/blog/researcher-dataset-sharing
|
Who is this blog post for? Are you a researcher doing data-intensive research or using machine learning as a research tool? As part of this research, you have likely created datasets for training and evaluating machine learning models, and like many researchers, you may be sharing these datasets via Google Drive, OneDrive, or your own personal server. In this post, we’ll outline why you might want to consider sharing these datasets on the Hugging Face Hub instead. This post outlines: - Why researchers should openly share their data (feel free to skip this section if you are already convinced about this!) - What the Hugging Face Hub offers for researchers who want to share their datasets. - Resources for getting started with sharing your datasets on the Hugging Face Hub.
|
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] |
Creating open machine learning datasets? Share them on the Hugging Face Hub!
|
davanstrien
|
October 30, 2023
|
researcher-dataset-sharing
|
community, research, datasets, guide
|
https://huggingface.co/blog/researcher-dataset-sharing
|
Other important features for researchers Some other features of the Hub may be of particular interest to researchers wanting to share their machine learning datasets on the Hub: - Organizations allow you to collaborate with other people and share models, datasets and demos under a single organization. This can be an excellent way of highlighting the work of a particular research project or institute. - Gated repositories allow you to add some access restrictions to accessing your dataset. - Download metrics are available for datasets on the Hub; this can be useful for communicating the impact of your researchers to funders and hiring committees. - Digital Object Identifiers (DOI): it’s possible to register a persistent identifier for your dataset.
|
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] |
Creating open machine learning datasets? Share them on the Hugging Face Hub!
|
davanstrien
|
October 30, 2023
|
researcher-dataset-sharing
|
community, research, datasets, guide
|
https://huggingface.co/blog/researcher-dataset-sharing
|
Tools for exploring and working with datasets There are a growing number of tools being created which make it easier to understand datasets hosted on the Hugging Face Hub.
|
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] |
Creating open machine learning datasets? Share them on the Hugging Face Hub!
|
davanstrien
|
October 30, 2023
|
researcher-dataset-sharing
|
community, research, datasets, guide
|
https://huggingface.co/blog/researcher-dataset-sharing
|
What is the Hugging Face Hub? The Hugging Face Hub has become the central hub for sharing open machine learning models, datasets and demos, hosting over 360,000 models and 70,000 datasets. The Hub enables people – including researchers – to access state-of-the-art machine learning models and datasets in a few lines of code. Datasets on the Hugging Face Hub.
|
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] |
Creating open machine learning datasets? Share them on the Hugging Face Hub!
|
davanstrien
|
October 30, 2023
|
researcher-dataset-sharing
|
community, research, datasets, guide
|
https://huggingface.co/blog/researcher-dataset-sharing
|
Datasets Viewer The datasets viewer allows people to explore and interact with datasets hosted on the Hub directly in the browser by visiting the dataset repository on the Hugging Face Hub. This makes it much easier for others to view and explore your data without first having to download it. The datasets viewer also allows you to search and filter datasets, which can be valuable to potential dataset users, understanding the nature of a dataset more quickly. The dataset viewer for the multiconer_v2 Named Entity Recognition dataset.
|
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] |
Creating open machine learning datasets? Share them on the Hugging Face Hub!
|
davanstrien
|
October 30, 2023
|
researcher-dataset-sharing
|
community, research, datasets, guide
|
https://huggingface.co/blog/researcher-dataset-sharing
|
Visibility for your work The Hugging Face Hub has become the central Hub for people to collaborate on open machine learning. Making your datasets available via the Hugging Face Hub ensures it is visible to a wide audience of machine learning researchers. The Hub makes it possible to expose links between datasets, models and demos which makes it easier to see how people are using your datasets for training models and creating demos.
|
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0.019508862867951393,
-0.01396030094474554,
-0.05985509231686592,
-0.030137861147522926
] |
Creating open machine learning datasets? Share them on the Hugging Face Hub!
|
davanstrien
|
October 30, 2023
|
researcher-dataset-sharing
|
community, research, datasets, guide
|
https://huggingface.co/blog/researcher-dataset-sharing
|
Lilac Lilac is a tool that aims to help you "curate better data for LLMs" and allows you to explore natural language datasets more easily. The tool allows you to semantically search your dataset (search by meaning), cluster data and gain high-level insights into your dataset. A Spaces demo of the lilac tool. You can explore the Lilac tool further in a demo. This growing number of tools for exploring datasets on the Hub makes it easier for people to explore and understand your datasets and can help promote your datasets to a wider audience.
|
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] |
Creating open machine learning datasets? Share them on the Hugging Face Hub!
|
davanstrien
|
October 30, 2023
|
researcher-dataset-sharing
|
community, research, datasets, guide
|
https://huggingface.co/blog/researcher-dataset-sharing
|
Why share your data? Machine learning is increasingly utilized across various disciplines, enhancing research efficiency in tackling diverse problems. Data remains crucial for training and evaluating models, especially when developing new machine-learning methods for specific tasks or domains. Large Language Models may not perform well on specialized tasks like bio-medical entity extraction, and computer vision models might struggle with classifying domain specific images. Domain-specific datasets are vital for evaluating and training machine learning models, helping to overcome the limitations of existing models. Creating these datasets, however, is challenging, requiring significant time, resources, and domain expertise, particularly for annotating data. Maximizing the impact of this data is crucial for the benefit of both the researchers involved and their respective fields. The Hugging Face Hub can help achieve this maximum impact.
|
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] |
Creating open machine learning datasets? Share them on the Hugging Face Hub!
|
davanstrien
|
October 30, 2023
|
researcher-dataset-sharing
|
community, research, datasets, guide
|
https://huggingface.co/blog/researcher-dataset-sharing
|
API and client library interaction with the Hub Interacting with the Hugging Face Hub via an API or the huggingface_hub Python library is possible. This includes creating new repositories, uploading data programmatically and creating and modifying metadata for datasets. This can be powerful for research workflows where new data or annotations continue to be created. The client library also makes uploading large datasets much more accessible.
|
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] |
Creating open machine learning datasets? Share them on the Hugging Face Hub!
|
davanstrien
|
October 30, 2023
|
researcher-dataset-sharing
|
community, research, datasets, guide
|
https://huggingface.co/blog/researcher-dataset-sharing
|
What does the Hugging Face Hub offer for data sharing? This blog post won’t cover all of the features and benefits of hosting datasets on the Hugging Face Hub but will instead highlight some that are particularly relevant for researchers.
|
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] |
Creating open machine learning datasets? Share them on the Hugging Face Hub!
|
davanstrien
|
October 30, 2023
|
researcher-dataset-sharing
|
community, research, datasets, guide
|
https://huggingface.co/blog/researcher-dataset-sharing
|
Community The Hugging Face Hub is already home to a large community of researchers, developers, artists, and others interested in using and contributing to an ecosystem of open-source machine learning. Making your datasets accessible to this community increases their visibility, opens them up to new types of users and places your datasets within the context of a larger ecosystem of models, datasets and libraries. The Hub also has features which allow communities to collaborate more easily. This includes a discussion page for each dataset, model and Space hosted on the Hub. This means users of your datasets can quickly ask questions and discuss ideas for working with a dataset. The Hub makes it easy to ask questions and discuss datasets.
|
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] |
Creating open machine learning datasets? Share them on the Hugging Face Hub!
|
davanstrien
|
October 30, 2023
|
researcher-dataset-sharing
|
community, research, datasets, guide
|
https://huggingface.co/blog/researcher-dataset-sharing
|
Support for large datasets The Hub can host large datasets; it currently hosts datasets with multiple TBs of data.The datasets library, which users can use to download and process datasets from the Hub, supports streaming, making it possible to work with large datasets without downloading the entire dataset upfront. This can be invaluable for allowing researchers with less computational resources to work with your datasets, or to select small portions of a huge dataset for testing, development or prototyping. The Hugging Face Hub can host the large datasets often created for machine learning research.
|
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] |
Creating open machine learning datasets? Share them on the Hugging Face Hub!
|
davanstrien
|
October 30, 2023
|
researcher-dataset-sharing
|
community, research, datasets, guide
|
https://huggingface.co/blog/researcher-dataset-sharing
|
How can I share my dataset on the Hugging Face Hub? Here are some resources to help you get started with sharing your datasets on the Hugging Face Hub: - General guidance on creating and sharing datasets on the Hub - Guides for particular modalities: - Creating an audio dataset - Creating an image dataset - Guidance on structuring your repository so a dataset can be automatically loaded from the Hub. The following pages will be useful if you want to share large datasets: - Repository limitations and recommendations provides general guidance on some of the considerations you'll want to make when sharing large datasets. - The Tips and tricks for large uploads page provides some guidance on how to upload large datasets to the Hub. If you want any further help uploading a dataset to the Hub or want to upload a particularly large dataset, please contact [email protected].
|
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] |
Creating open machine learning datasets? Share them on the Hugging Face Hub!
|
davanstrien
|
October 30, 2023
|
researcher-dataset-sharing
|
community, research, datasets, guide
|
https://huggingface.co/blog/researcher-dataset-sharing
|
Community tools Alongside the datasets viewer there are a growing number of community created tools for exploring datasets on the Hub.
|
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] |
Introducing Storage Regions on the HF Hub
|
julien-c
|
November 3, 2023
|
regions
|
announcement, enterprise, hub
|
https://huggingface.co/blog/regions
|
Regulatory and legal compliance In many regulated industries, you may have a requirement to store your data in a specific area. For companies in the EU, that means you can use the Hub to build ML in a GDPR compliant way: with datasets, models and inference endpoints all stored within EU data centers. If you are an Enterprise Hub customer and have further questions about this, please get in touch!
|
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] |
Introducing Storage Regions on the HF Hub
|
julien-c
|
November 3, 2023
|
regions
|
announcement, enterprise, hub
|
https://huggingface.co/blog/regions
|
Performance Storing your models or your datasets closer to your team and infrastructure also means significantly improved performance, for both uploads and downloads. This makes a big difference considering model weights and dataset files are usually very large. As an example, if you are located in Europe and store your repositories in the EU region, you can expect to see ~4-5x faster upload and download speeds vs. if they were stored in the US.
|
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] |
Introducing Storage Regions on the HF Hub
|
julien-c
|
November 3, 2023
|
regions
|
announcement, enterprise, hub
|
https://huggingface.co/blog/regions
|
Repository Tag Any repo (model or dataset) stored in a non-default location will display its Region directly as a tag. That way your organization's members can see at a glance where repos are located.
|
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] |
Introducing Storage Regions on the HF Hub
|
julien-c
|
November 3, 2023
|
regions
|
announcement, enterprise, hub
|
https://huggingface.co/blog/regions
|
# Introducing Storage Regions on the Hub As part of our Enterprise Hub plan, we recently released support for Storage Regions. Regions let you decide where your org's models and datasets will be stored. This has two main benefits, which we'll briefly go over in this blog post: - Regulatory and legal compliance, and more generally, better digital sovereignty - Performance (improved download and upload speeds and latency) Currently we support the following regions: - US 🇺🇸 - EU 🇪🇺 - coming soon: Asia-Pacific 🌏 But first, let's see how to setup this feature in your organization's settings 🔥
|
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] |
Introducing Storage Regions on the HF Hub
|
julien-c
|
November 3, 2023
|
regions
|
announcement, enterprise, hub
|
https://huggingface.co/blog/regions
|
Org settings If your organization is not an Enterprise Hub org yet, you will see the following screen: As soon as you subscribe, you will be able to see the Regions settings page: On that page you can see: - an audit of where your orgs' repos are currently located - dropdowns to select where your repos will be created
|
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] |
Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora
|
mehdiiraqui
|
November 7, 2023
|
Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
nlp, guide, llm, peft
|
https://huggingface.co/blog/Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
Dependencies bash datasets evaluate peft scikit-learn torch transformers wandb Note: For reproducing the reported results, please check the pinned versions in the wandb reports.
|
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] |
Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora
|
mehdiiraqui
|
November 7, 2023
|
Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
nlp, guide, llm, peft
|
https://huggingface.co/blog/Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
RoBERTa First important step is to move the models to the GPU device for training. python roberta_model = roberta_model.cuda() roberta_model.device() It will print the following: device(type='cuda', index=0) Then, we set the training arguments: python from transformers import TrainingArguments lr = 1e-4 batch_size = 8 num_epochs = 5 training_args = TrainingArguments( output_dir="roberta-large-lora-token-classification", learning_rate=lr, lr_scheduler_type= "constant", warmup_ratio= 0.1, max_grad_norm= 0.3, per_device_train_batch_size=batch_size, per_device_eval_batch_size=batch_size, num_train_epochs=num_epochs, weight_decay=0.001, evaluation_strategy="epoch", save_strategy="epoch", load_best_model_at_end=True, report_to="wandb", fp16=False, gradient_checkpointing=True, ) Finally, we define the RoBERTa trainer by providing the model, the training arguments and the tokenized datasets: python roberta_trainer = WeightedCELossTrainer( model=roberta_model, args=training_args, train_dataset=roberta_tokenized_datasets['train'], eval_dataset=roberta_tokenized_datasets["val"], data_collator=roberta_data_collator, compute_metrics=compute_metrics )
|
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] |
Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora
|
mehdiiraqui
|
November 7, 2023
|
Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
nlp, guide, llm, peft
|
https://huggingface.co/blog/Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
Hardware Used - Number of nodes: 1 - Number of GPUs per node: 1 - GPU type: A6000 - GPU memory: 48GB
|
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] |
Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora
|
mehdiiraqui
|
November 7, 2023
|
Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
nlp, guide, llm, peft
|
https://huggingface.co/blog/Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
Pre-trained Models
|
[
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Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora
|
mehdiiraqui
|
November 7, 2023
|
Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
nlp, guide, llm, peft
|
https://huggingface.co/blog/Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
# Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora - Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with LoRA - Introduction - Hardware Used - Goals - Dependencies - Pre-trained Models - RoBERTa - Llama 2 - Mistral 7B - LoRA - Setup - Data preparation - Data loading - Data Processing - Models - RoBERTa - Load RoBERTA Checkpoints for the Classification Task - LoRA setup for RoBERTa classifier - Mistral - Load checkpoints for the classfication model - LoRA setup for Mistral 7B classifier - Llama 2 - Load checkpoints for the classification mode - LoRA setup for Llama 2 classifier - Setup the trainer - Evaluation Metrics - Custom Trainer for Weighted Loss - Trainer Setup - RoBERTa - Mistral-7B - Llama 2 - Hyperparameter Tuning - Results - Conclusion - Resources
|
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] |
Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora
|
mehdiiraqui
|
November 7, 2023
|
Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
nlp, guide, llm, peft
|
https://huggingface.co/blog/Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
Load checkpoints for the classfication mode Let's load pre-trained Llama 2 model with a sequence classification header. python from transformers import AutoModelForSequenceClassification import torch llama_model = AutoModelForSequenceClassification.from_pretrained( pretrained_model_name_or_path=llama_checkpoint, num_labels=2, device_map="auto", offload_folder="offload", trust_remote_code=True ) For Llama 2, we have to add the padding token id as it is not defined by default. python llama_model.config.pad_token_id = llama_model.config.eos_token_id
|
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] |
Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora
|
mehdiiraqui
|
November 7, 2023
|
Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
nlp, guide, llm, peft
|
https://huggingface.co/blog/Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
Data preparation
|
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] |
Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora
|
mehdiiraqui
|
November 7, 2023
|
Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
nlp, guide, llm, peft
|
https://huggingface.co/blog/Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
RoBERTa
|
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Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora
|
mehdiiraqui
|
November 7, 2023
|
Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
nlp, guide, llm, peft
|
https://huggingface.co/blog/Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
Data Processing Let's take a look to one row example of training data: python data['train'][0] {'id': 5285, 'keyword': 'fear', 'location': 'Thibodaux, LA', 'text': 'my worst fear. https://t.co/iH8UDz8mq3', 'target': 0} The data comprises a keyword, a location and the text of the tweet. For the sake of simplicity, we select the text feature as the only input to the LLM. At this stage, we prepared the train, validation, and test sets in the HuggingFace format expected by the pre-trained LLMs. The next step is to define the tokenized dataset for training using the appropriate tokenizer to transform the text feature into two Tensors of sequence of token ids and attention masks. As each model has its specific tokenizer, we will need to define three different datasets. We start by defining the RoBERTa dataloader: - Load the tokenizer: python from transformers import AutoTokenizer roberta_tokenizer = AutoTokenizer.from_pretrained(roberta_checkpoint, add_prefix_space=True) Note: The RoBERTa tokenizer has been trained to treat spaces as part of the token. As a result, the first word of the sentence is encoded differently if it is not preceded by a white space. To ensure the first word includes a space, we set add_prefix_space=True. Also, to maintain consistent pre-processing for all three models, we set the parameter to 'True' for Llama 2 and Mistral 7b. - Define the preprocessing function for converting one row of the dataframe: python def roberta_preprocessing_function(examples): return roberta_tokenizer(examples['text'], truncation=True, max_length=MAX_LEN) By applying the preprocessing function to the first example of our training dataset, we have the tokenized inputs (input_ids) and the attention mask: python roberta_preprocessing_function(data['train'][0]) {'input_ids': [0, 127, 2373, 2490, 4, 1205, 640, 90, 4, 876, 73, 118, 725, 398, 13083, 329, 398, 119, 1343, 246, 2], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]} - Now, let's apply the preprocessing function to the entire dataset: python col_to_delete = ['id', 'keyword','location', 'text'] # Apply the preprocessing function and remove the undesired columns roberta_tokenized_datasets = data.map(roberta_preprocessing_function, batched=True, remove_columns=col_to_delete) # Rename the target to label as for HugginFace standards roberta_tokenized_datasets = roberta_tokenized_datasets.rename_column("target", "label") # Set to torch format roberta_tokenized_datasets.set_format("torch") Note: we deleted the undesired columns from our data: id, keyword, location and text. We have deleted the text because we have already converted it into the inputs ids and the attention mask: We can have a look into our tokenized training dataset: python roberta_tokenized_datasets['train'][0] {'label': tensor(0), 'input_ids': tensor([ 0, 127, 2373, 2490, 4, 1205, 640, 90, 4, 876, 73, 118, 725, 398, 13083, 329, 398, 119, 1343, 246, 2]), 'attention_mask': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1])} - For generating the training batches, we also need to pad the rows of a given batch to the maximum length found in the batch. For that, we will use the DataCollatorWithPadding class: python # Data collator for padding a batch of examples to the maximum length seen in the batch from transformers import DataCollatorWithPadding roberta_data_collator = DataCollatorWithPadding(tokenizer=roberta_tokenizer) You can follow the same steps for preparing the data for Mistral 7B and Llama 2 models: Note that Llama 2 and Mistral 7B don't have a default pad_token_id. So, we use the eos_token_id for padding as well. - Mistral 7B: python # Load Mistral 7B Tokenizer from transformers import AutoTokenizer, DataCollatorWithPadding mistral_tokenizer = AutoTokenizer.from_pretrained(mistral_checkpoint, add_prefix_space=True) mistral_tokenizer.pad_token_id = mistral_tokenizer.eos_token_id mistral_tokenizer.pad_token = mistral_tokenizer.eos_token def mistral_preprocessing_function(examples): return mistral_tokenizer(examples['text'], truncation=True, max_length=MAX_LEN) mistral_tokenized_datasets = data.map(mistral_preprocessing_function, batched=True, remove_columns=col_to_delete) mistral_tokenized_datasets = mistral_tokenized_datasets.rename_column("target", "label") mistral_tokenized_datasets.set_format("torch") # Data collator for padding a batch of examples to the maximum length seen in the batch mistral_data_collator = DataCollatorWithPadding(tokenizer=mistral_tokenizer) - Llama 2: python # Load Llama 2 Tokenizer from transformers import AutoTokenizer, DataCollatorWithPadding llama_tokenizer = AutoTokenizer.from_pretrained(llama_checkpoint, add_prefix_space=True) llama_tokenizer.pad_token_id = llama_tokenizer.eos_token_id llama_tokenizer.pad_token = llama_tokenizer.eos_token def llama_preprocessing_function(examples): return llama_tokenizer(examples['text'], truncation=True, max_length=MAX_LEN) llama_tokenized_datasets = data.map(llama_preprocessing_function, batched=True, remove_columns=col_to_delete) llama_tokenized_datasets = llama_tokenized_datasets.rename_column("target", "label") llama_tokenized_datasets.set_format("torch") # Data collator for padding a batch of examples to the maximum length seen in the batch llama_data_collator = DataCollatorWithPadding(tokenizer=llama_tokenizer) Now that we have prepared the tokenized datasets, the next section will showcase how to load the pre-trained LLMs checkpoints and how to set the LoRa weights.
|
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] |
Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora
|
mehdiiraqui
|
November 7, 2023
|
Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
nlp, guide, llm, peft
|
https://huggingface.co/blog/Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
LoRA PEFT, Parameter Efficient Fine-Tuning, is a collection of techniques (p-tuning, prefix-tuning, IA3, Adapters, and LoRa) designed to fine-tune large models using a much smaller set of training parameters while preserving the performance levels typically achieved through full fine-tuning. LoRA, Low-Rank Adaptation, is a PEFT method that shares similarities with Adapter layers. Its primary objective is to reduce the model's trainable parameters. LoRA's operation involves learning a low rank update matrix while keeping the pre-trained weights frozen.
|
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] |
Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora
|
mehdiiraqui
|
November 7, 2023
|
Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
nlp, guide, llm, peft
|
https://huggingface.co/blog/Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
Hyperparameter Tuning We have used Wandb Sweep API to run hyperparameter tunning with Bayesian search strategy (30 runs). The hyperparameters tuned are the following. | method | metric | lora_alpha | lora_bias | lora_dropout | lora_rank | lr | max_length | |--------|---------------------|-------------------------------------------|---------------------------|-------------------------|----------------------------------------------------|-----------------------------|---------------------------| | bayes | goal: maximize | distribution: categorical | distribution: categorical | distribution: uniform | distribution: categorical | distribution: uniform | distribution: categorical | | | name: eval/f1-score | values: -16 -32 -64 | values: None | -max: 0.1 -min: 0 | values: -4 -8 -16 -32 | -max: 2e-04-min: 1e-05 | values: 512 | | For more information, you can check the Wandb experiment report in the resources sections.
|
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] |
Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora
|
mehdiiraqui
|
November 7, 2023
|
Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
nlp, guide, llm, peft
|
https://huggingface.co/blog/Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
LoRa setup for Llama 2 classifier We define LoRa for Llama 2 with the same parameters as for Mistral: python from peft import get_peft_model, LoraConfig, TaskType llama_peft_config = LoraConfig( task_type=TaskType.SEQ_CLS, r=16, lora_alpha=16, lora_dropout=0.05, bias="none", target_modules=[ "q_proj", "v_proj", ], ) llama_model = get_peft_model(llama_model, llama_peft_config) llama_model.print_trainable_parameters() The number of trainable parameters reprents only 0.12% of the Llama 2 model parameters: trainable params: 8,404,992 || all params: 6,615,748,608 || trainable%: 0.1270452143516515 At this point, we defined the tokenized dataset for training as well as the LLMs setup with LoRa layers. The following section will introduce how to launch training using the HuggingFace Trainer class.
|
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] |
Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora
|
mehdiiraqui
|
November 7, 2023
|
Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
nlp, guide, llm, peft
|
https://huggingface.co/blog/Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
Mistral-7B Similar to RoBERTa, we initialize the WeightedCELossTrainer as follows: python from transformers import TrainingArguments, Trainer mistral_model = mistral_model.cuda() lr = 1e-4 batch_size = 8 num_epochs = 5 training_args = TrainingArguments( output_dir="mistral-lora-token-classification", learning_rate=lr, lr_scheduler_type= "constant", warmup_ratio= 0.1, max_grad_norm= 0.3, per_device_train_batch_size=batch_size, per_device_eval_batch_size=batch_size, num_train_epochs=num_epochs, weight_decay=0.001, evaluation_strategy="epoch", save_strategy="epoch", load_best_model_at_end=True, report_to="wandb", fp16=True, gradient_checkpointing=True, ) mistral_trainer = WeightedCELossTrainer( model=mistral_model, args=training_args, train_dataset=mistral_tokenized_datasets['train'], eval_dataset=mistral_tokenized_datasets["val"], data_collator=mistral_data_collator, compute_metrics=compute_metrics ) Note that we needed to enable half-precision training by setting fp16 to True. The main reason is that Mistral-7B is large, and its weights cannot fit into one GPU memory (48GB) with full float32 precision.
|
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] |
Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora
|
mehdiiraqui
|
November 7, 2023
|
Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
nlp, guide, llm, peft
|
https://huggingface.co/blog/Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
Setup the trainer
|
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] |
Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora
|
mehdiiraqui
|
November 7, 2023
|
Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
nlp, guide, llm, peft
|
https://huggingface.co/blog/Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
Introduction In the fast-moving world of Natural Language Processing (NLP), we often find ourselves comparing different language models to see which one works best for specific tasks. This blog post is all about comparing three models: RoBERTa, Mistral-7b, and Llama-2-7b. We used them to tackle a common problem - classifying tweets about disasters. It is important to note that Mistral and Llama 2 are large models with 7 billion parameters. In contrast, RoBERTa-large (355M parameters) is a relatively smaller model used as a baseline for the comparison study. In this blog, we used PEFT (Parameter-Efficient Fine-Tuning) technique: LoRA (Low-Rank Adaptation of Large Language Models) for fine-tuning the pre-trained model on the sequence classification task. LoRa is designed to significantly reduce the number of trainable parameters while maintaining strong downstream task performance. The main objective of this blog post is to implement LoRA fine-tuning for sequence classification tasks using three pre-trained models from Hugging Face: meta-llama/Llama-2-7b-hf, mistralai/Mistral-7B-v0.1, and roberta-large
|
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] |
Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora
|
mehdiiraqui
|
November 7, 2023
|
Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
nlp, guide, llm, peft
|
https://huggingface.co/blog/Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
Mistral
|
[
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] |
Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora
|
mehdiiraqui
|
November 7, 2023
|
Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
nlp, guide, llm, peft
|
https://huggingface.co/blog/Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
Results | Models | F1 score | Training time | Memory consumption | Number of trainable parameters | |---------|----------|----------------|------------------------------|--------------------------------| | RoBERTa | 0.8077 | 538 seconds | GPU1: 9.1 GbGPU2: 8.3 Gb | 0.64% | | Mistral 7B | 0.7364 | 2030 seconds | GPU1: 29.6 GbGPU2: 29.5 Gb | 0.024% | | Llama 2 | 0.7638 | 2052 seconds | GPU1: 35 Gb GPU2: 33.9 Gb | 0.12% |
|
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] |
Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora
|
mehdiiraqui
|
November 7, 2023
|
Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
nlp, guide, llm, peft
|
https://huggingface.co/blog/Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
Mistral 7B Mistral 7B v0.1, with 7.3 billion parameters, is the first LLM introduced by Mistral AI. The main novel techniques used in Mistral 7B's architecture are: - Sliding Window Attention: Replace the full attention (square compute cost) with a sliding window based attention where each token can attend to at most 4,096 tokens from the previous layer (linear compute cost). This mechanism enables Mistral 7B to handle longer sequences, where higher layers can access historical information beyond the window size of 4,096 tokens. - Grouped-query Attention: used in Llama 2 as well, the technique optimizes the inference process (reduce processing time) by caching the key and value vectors for previously decoded tokens in the sequence.
|
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] |
Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora
|
mehdiiraqui
|
November 7, 2023
|
Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
nlp, guide, llm, peft
|
https://huggingface.co/blog/Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
Conclusion In this blog post, we compared the performance of three large language models (LLMs) - RoBERTa, Mistral 7b, and Llama 2 - for disaster tweet classification using LoRa. From the performance results, we can see that RoBERTa is outperforming Mistral 7B and Llama 2 by a large margin. This raises the question about whether we really need a complex and large LLM for tasks like short-sequence binary classification? One learning we can draw from this study is that one should account for the specific project requirements, available resources, and performance needs to choose the LLMs model to use. Also, for relatively simple prediction tasks with short sequences base models such as RoBERTa remain competitive. Finally, we showcase that LoRa method can be applied to both encoder (RoBERTa) and decoder (Llama 2 and Mistral 7B) models.
|
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] |
Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora
|
mehdiiraqui
|
November 7, 2023
|
Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
nlp, guide, llm, peft
|
https://huggingface.co/blog/Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
Llama 2 Llama 2 models, which stands for Large Language Model Meta AI, belong to the family of large language models (LLMs) introduced by Meta AI. The Llama 2 models vary in size, with parameter counts ranging from 7 billion to 65 billion. Llama 2 is an auto-regressive language model, based on the transformer decoder architecture. To generate text, Llama 2 processes a sequence of words as input and iteratively predicts the next token using a sliding window. Llama 2 architecture is slightly different from models like GPT-3. For instance, Llama 2 employs the SwiGLU activation function rather than ReLU and opts for rotary positional embeddings in place of absolute learnable positional embeddings. The recently released Llama 2 introduced architectural refinements to better leverage very long sequences by extending the context length to up to 4096 tokens, and using grouped-query attention (GQA) decoding.
|
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] |
Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora
|
mehdiiraqui
|
November 7, 2023
|
Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
nlp, guide, llm, peft
|
https://huggingface.co/blog/Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
Load RoBERTa Checkpoints for the Classification Task We load the pre-trained RoBERTa model with a sequence classification head using the Hugging Face AutoModelForSequenceClassification class: python from transformers import AutoModelForSequenceClassification roberta_model = AutoModelForSequenceClassification.from_pretrained(roberta_checkpoint, num_labels=2)
|
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] |
Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora
|
mehdiiraqui
|
November 7, 2023
|
Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
nlp, guide, llm, peft
|
https://huggingface.co/blog/Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
Resources 1. You can find the code script in the following Github project. 2. You can check the hyper-param search results in the following Weight&Bias reports: - RoBERTa - Mistral 7B - Llama 2
|
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] |
Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora
|
mehdiiraqui
|
November 7, 2023
|
Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
nlp, guide, llm, peft
|
https://huggingface.co/blog/Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
Setup RoBERTa has a limitatiom of maximum sequence length of 512, so we set the MAX_LEN=512 for all models to ensure a fair comparison. python MAX_LEN = 512 roberta_checkpoint = "roberta-large" mistral_checkpoint = "mistralai/Mistral-7B-v0.1" llama_checkpoint = "meta-llama/Llama-2-7b-hf"
|
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] |
Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora
|
mehdiiraqui
|
November 7, 2023
|
Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
nlp, guide, llm, peft
|
https://huggingface.co/blog/Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
Llama 2
|
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] |
Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora
|
mehdiiraqui
|
November 7, 2023
|
Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
nlp, guide, llm, peft
|
https://huggingface.co/blog/Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
Evaluation Metrics First, we define the performance metrics we will use to compare the three models: F1 score, recall, precision and accuracy: python import evaluate import numpy as np def compute_metrics(eval_pred): # All metrics are already predefined in the HF `evaluate` package precision_metric = evaluate.load("precision") recall_metric = evaluate.load("recall") f1_metric= evaluate.load("f1") accuracy_metric = evaluate.load("accuracy") logits, labels = eval_pred # eval_pred is the tuple of predictions and labels returned by the model predictions = np.argmax(logits, axis=-1) precision = precision_metric.compute(predictions=predictions, references=labels)["precision"] recall = recall_metric.compute(predictions=predictions, references=labels)["recall"] f1 = f1_metric.compute(predictions=predictions, references=labels)["f1"] accuracy = accuracy_metric.compute(predictions=predictions, references=labels)["accuracy"] # The trainer is expecting a dictionary where the keys are the metrics names and the values are the scores. return {"precision": precision, "recall": recall, "f1-score": f1, 'accuracy': accuracy}
|
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] |
Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora
|
mehdiiraqui
|
November 7, 2023
|
Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
nlp, guide, llm, peft
|
https://huggingface.co/blog/Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
Custom Trainer for Weighted Loss As mentioned at the beginning of this post, we have an imbalanced distribution between positive and negative classes. We need to train our models with a weighted cross-entropy loss to account for that. The Trainer class doesn't support providing a custom loss as it expects to get the loss directly from the model's outputs. So, we need to define our custom WeightedCELossTrainer that overrides the compute_loss method to calculate the weighted cross-entropy loss based on the model's predictions and the input labels: python from transformers import Trainer class WeightedCELossTrainer(Trainer): def compute_loss(self, model, inputs, return_outputs=False): labels = inputs.pop("labels") # Get model's predictions outputs = model(**inputs) logits = outputs.get("logits") # Compute custom loss loss_fct = torch.nn.CrossEntropyLoss(weight=torch.tensor([neg_weights, pos_weights], device=model.device, dtype=logits.dtype)) loss = loss_fct(logits.view(-1, self.model.config.num_labels), labels.view(-1)) return (loss, outputs) if return_outputs else loss
|
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] |
Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora
|
mehdiiraqui
|
November 7, 2023
|
Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
nlp, guide, llm, peft
|
https://huggingface.co/blog/Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
LoRA setup for RoBERTa classifier We import LoRa configuration and set some parameters for RoBERTa classifier: - TaskType: Sequence classification - r(rank): Rank for our decomposition matrices - lora_alpha: Alpha parameter to scale the learned weights. LoRA paper advises fixing alpha at 16 - lora_dropout: Dropout probability of the LoRA layers - bias: Whether to add bias term to LoRa layers The code below uses the values recommended by the Lora paper. Later in this post we will perform hyperparameter tuning of these parameters using wandb. python from peft import get_peft_model, LoraConfig, TaskType roberta_peft_config = LoraConfig( task_type=TaskType.SEQ_CLS, r=2, lora_alpha=16, lora_dropout=0.1, bias="none", ) roberta_model = get_peft_model(roberta_model, roberta_peft_config) roberta_model.print_trainable_parameters() We can see that the number of trainable parameters represents only 0.64% of the RoBERTa model parameters: bash trainable params: 2,299,908 || all params: 356,610,052 || trainable%: 0.6449363911929212
|
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] |
Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora
|
mehdiiraqui
|
November 7, 2023
|
Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
nlp, guide, llm, peft
|
https://huggingface.co/blog/Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
Llama 2 Similar to Mistral 7B, we define the trainer as follows: python from transformers import TrainingArguments, Trainer llama_model = llama_model.cuda() lr = 1e-4 batch_size = 8 num_epochs = 5 training_args = TrainingArguments( output_dir="llama-lora-token-classification", learning_rate=lr, lr_scheduler_type= "constant", warmup_ratio= 0.1, max_grad_norm= 0.3, per_device_train_batch_size=batch_size, per_device_eval_batch_size=batch_size, num_train_epochs=num_epochs, weight_decay=0.001, evaluation_strategy="epoch", save_strategy="epoch", load_best_model_at_end=True, report_to="wandb", fp16=True, gradient_checkpointing=True, ) llama_trainer = WeightedCELossTrainer( model=llama_model, args=training_args, train_dataset=llama_tokenized_datasets['train'], eval_dataset=llama_tokenized_datasets["val"], data_collator=llama_data_collator, compute_metrics=compute_metrics )
|
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] |
Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora
|
mehdiiraqui
|
November 7, 2023
|
Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
nlp, guide, llm, peft
|
https://huggingface.co/blog/Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
RoBERTa RoBERTa (Robustly Optimized BERT Approach) is an advanced variant of the BERT model proposed by Meta AI research team. BERT is a transformer-based language model using self-attention mechanisms for contextual word representations and trained with a masked language model objective. Note that BERT is an encoder only model used for natural language understanding tasks (such as sequence classification and token classification). RoBERTa is a popular model to fine-tune and appropriate as a baseline for our experiments. For more information, you can check the Hugging Face model card.
|
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] |
Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora
|
mehdiiraqui
|
November 7, 2023
|
Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
nlp, guide, llm, peft
|
https://huggingface.co/blog/Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
LoRa setup for Mistral 7B classifier For Mistral 7B model, we need to specify the target_modules (the query and value vectors from the attention modules): python from peft import get_peft_model, LoraConfig, TaskType mistral_peft_config = LoraConfig( task_type=TaskType.SEQ_CLS, r=2, lora_alpha=16, lora_dropout=0.1, bias="none", target_modules=[ "q_proj", "v_proj", ], ) mistral_model = get_peft_model(mistral_model, mistral_peft_config) mistral_model.print_trainable_parameters() The number of trainable parameters reprents only 0.024% of the Mistral model parameters: trainable params: 1,720,320 || all params: 7,112,380,416 || trainable%: 0.02418768259540745
|
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] |
Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora
|
mehdiiraqui
|
November 7, 2023
|
Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
nlp, guide, llm, peft
|
https://huggingface.co/blog/Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
Trainer Setup Let's set the training arguments and the trainer for the three models.
|
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] |
Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora
|
mehdiiraqui
|
November 7, 2023
|
Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
nlp, guide, llm, peft
|
https://huggingface.co/blog/Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
Models
|
[
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Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora
|
mehdiiraqui
|
November 7, 2023
|
Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
nlp, guide, llm, peft
|
https://huggingface.co/blog/Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
Data loading We will load the dataset from Hugging Face: python from datasets import load_dataset dataset = load_dataset("mehdiiraqui/twitter_disaster") Now, let's split the dataset into training and validation datasets. Then add the test set: python from datasets import Dataset # Split the dataset into training and validation datasets data = dataset['train'].train_test_split(train_size=0.8, seed=42) # Rename the default "test" split to "validation" data['val'] = data.pop("test") # Convert the test dataframe to HuggingFace dataset and add it into the first dataset data['test'] = dataset['test'] Here's an overview of the dataset: bash DatasetDict({ train: Dataset({ features: ['id', 'keyword', 'location', 'text', 'target'], num_rows: 6090 }) val: Dataset({ features: ['id', 'keyword', 'location', 'text', 'target'], num_rows: 1523 }) test: Dataset({ features: ['id', 'keyword', 'location', 'text', 'target'], num_rows: 3263 }) }) Let's check the data distribution: python import pandas as pd data['train'].to_pandas().info() data['test'].to_pandas().info() - Train dataset <class 'pandas.core.frame.DataFrame'> RangeIndex: 7613 entries, 0 to 7612 Data columns (total 5 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 7613 non-null int64 1 keyword 7552 non-null object 2 location 5080 non-null object 3 text 7613 non-null object 4 target 7613 non-null int64 dtypes: int64(2), object(3) memory usage: 297.5+ KB - Test dataset <class 'pandas.core.frame.DataFrame'> RangeIndex: 3263 entries, 0 to 3262 Data columns (total 5 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 id 3263 non-null int64 1 keyword 3237 non-null object 2 location 2158 non-null object 3 text 3263 non-null object 4 target 3263 non-null int64 dtypes: int64(2), object(3) memory usage: 127.6+ KB Target distribution in the train dataset target 0 4342 1 3271 Name: count, dtype: int64 As the classes are not balanced, we will compute the positive and negative weights and use them for loss calculation later: python pos_weights = len(data['train'].to_pandas()) / (2 * data['train'].to_pandas().target.value_counts()[1]) neg_weights = len(data['train'].to_pandas()) / (2 * data['train'].to_pandas().target.value_counts()[0]) The final weights are: POS_WEIGHT, NEG_WEIGHT = (1.1637114032405993, 0.8766697374481806) Then, we compute the maximum length of the column text: python # Number of Characters max_char = data['train'].to_pandas()['text'].str.len().max() # Number of Words max_words = data['train'].to_pandas()['text'].str.split().str.len().max() The maximum number of characters is 152. The maximum number of words is 31.
|
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] |
Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora
|
mehdiiraqui
|
November 7, 2023
|
Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
nlp, guide, llm, peft
|
https://huggingface.co/blog/Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
Load checkpoints for the classfication model Let's load the pre-trained Mistral-7B model with a sequence classification head: python from transformers import AutoModelForSequenceClassification import torch mistral_model = AutoModelForSequenceClassification.from_pretrained( pretrained_model_name_or_path=mistral_checkpoint, num_labels=2, device_map="auto" ) For Mistral 7B, we have to add the padding token id as it is not defined by default. python mistral_model.config.pad_token_id = mistral_model.config.eos_token_id
|
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] |
Comparing the Performance of LLMs: A Deep Dive into Roberta, Llama 2, and Mistral for Disaster Tweets Analysis with Lora
|
mehdiiraqui
|
November 7, 2023
|
Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
nlp, guide, llm, peft
|
https://huggingface.co/blog/Lora-for-sequence-classification-with-Roberta-Llama-Mistral
|
Goals - Implement fine-tuning of pre-trained LLMs using LoRA PEFT methods. - Learn how to use the HuggingFace APIs (transformers, peft, and datasets). - Setup the hyperparameter tuning and experiment logging using Weights & Biases.
|
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] |
Introducing Prodigy-HF: a direct integration with Hugging Face
|
koaning
|
November 7, 2023
|
prodigy-hf
|
community, nlp, datasets, guide
|
https://huggingface.co/blog/prodigy-hf
|
Features The first main feature is that this plugin allows you to train and re-use Hugging Face models on your annotated data. That means if you've been annotating data in our interface for named entity recognition, you can directly fine-tune BERT models against it. What the Prodigy NER interface looks like. After installing the plugin you can call the hf.train.ner recipe from the command line to train a transformer model directly on your own data. python -m prodigy hf.train.ner fashion-train,eval:fashion-eval path/to/model-out --model "distilbert-base-uncased" This will fine-tune the distilbert-base-uncased model for the dataset you've stored in Prodigy and save it to disk. Similarly, this plugin also supports models for text classification via a very similar interface. python -m prodigy hf.train.textcat fashion-train,eval:fashion-eval path/to/model-out --model "distilbert-base-uncased" This offers a lot of flexibility because the tool directly integrates with the AutoTokenizer and AutoModel classes of Hugging Face transformers. Any transformer model on the hub can be fine-tuned on your own dataset with just a single command. These models will be serialised on disk, which means that you can upload them to the Hugging Face Hub, or re-use them to help you annotate data. This can save a lot of time, especially for NER tasks. To re-use a trained NER model you can use the hf.correct.ner recipe. python -m prodigy hf.correct.ner fashion-train path/to/model-out examples.jsonl This will give you a similar interface as before, but now the model predictions will be shown in the interface as well.
|
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] |
Introducing Prodigy-HF: a direct integration with Hugging Face
|
koaning
|
November 7, 2023
|
prodigy-hf
|
community, nlp, datasets, guide
|
https://huggingface.co/blog/prodigy-hf
|
# Introducing Prodigy-HF Prodigy is an annotation tool made by Explosion, a company well known as the creators of spaCy. It's a fully scriptable product with a large community around it. The product has many features, including tight integration with spaCy and active learning capabilities. But the main feature of the product is that it is programmatically customizable with Python. To foster this customisability, Explosion has started releasing plugins. These plugins integrate with third-party tools in an open way that encourages users to work on bespoke annotation workflows. However, one customization specifically deserves to be celebrated explicitly. Last week, Explosion introduced Prodigy-HF, which offers code recipes that directly integrate with the Hugging Face stack. It's been a much-requested feature on the Prodigy support forum, so we're super excited to have it out there.
|
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] |
Introducing Prodigy-HF: a direct integration with Hugging Face
|
koaning
|
November 7, 2023
|
prodigy-hf
|
community, nlp, datasets, guide
|
https://huggingface.co/blog/prodigy-hf
|
More to come We hope that this direct integration with the Hugging Face ecosystem enables many users to experiment more. The Hugging Face Hub offers many models for a wide array of tasks as well as a wide array of languages. We really hope that this integration makes it easier to get data annotated, even if you've got a more domain specific and experimental use-case. More features for this library are on their way, and feel free to reach out on the Prodigy forum if you have more questions. We'd also like to thank the team over at Hugging Face for their feedback on this plugin, specifically @davanstrien, who suggested to add the upload feature. Thanks!
|
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] |
Introducing Prodigy-HF: a direct integration with Hugging Face
|
koaning
|
November 7, 2023
|
prodigy-hf
|
community, nlp, datasets, guide
|
https://huggingface.co/blog/prodigy-hf
|
Upload The second feature, which is equally exciting, is that you can now also publish your annotated datasets on the Hugging Face Hub. This is great if you're interested in sharing datasets that others would like to use. python -m prodigy hf.upload <dataset_name> <username>/<repo_name> We're particularly fond of this upload feature because it encourages collaboration. People can annotate their own datasets independently of each other, but still benefit when they share the data with the wider community.
|
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] |
Make your llama generation time fly with AWS Inferentia2
|
dacorvo
|
November 7, 2023
|
inferentia-llama2
|
guide, text-generation, llama2, aws
|
https://huggingface.co/blog/inferentia-llama2
|
Generate Text using Llama 2 on AWS Inferentia2 Once your model has been exported, you can generate text using the transformers library, as it has been described in detail in this previous post. >>> from optimum.neuron import NeuronModelForCausalLM >>> from transformers import AutoTokenizer >>> model = NeuronModelForCausalLM.from_pretrained('aws-neuron/Llama-2-7b-hf-neuron-latency') >>> tokenizer = AutoTokenizer.from_pretrained("aws-neuron/Llama-2-7b-hf-neuron-latency") >>> inputs = tokenizer("What is deep-learning ?", return_tensors="pt") >>> outputs = model.generate(**inputs, max_new_tokens=128, do_sample=True, temperature=0.9, top_k=50, top_p=0.9) >>> tokenizer.batch_decode(outputs, skip_special_tokens=True) ['What is deep-learning ?\nThe term “deep-learning” refers to a type of machine-learning that aims to model high-level abstractions of the data in the form of a hierarchy of multiple layers of increasingly complex processing nodes.'] Note: when passing multiple input prompts to a model, the resulting token sequences must be padded to the left with an end-of-stream token. The tokenizers saved with the exported models are configured accordingly. The following generation strategies are supported: - greedy search, - multinomial sampling with top-k and top-p (with temperature). Most logits pre-processing/filters (such as repetition penalty) are supported.
|
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] |
Make your llama generation time fly with AWS Inferentia2
|
dacorvo
|
November 7, 2023
|
inferentia-llama2
|
guide, text-generation, llama2, aws
|
https://huggingface.co/blog/inferentia-llama2
|
# Make your llama generation time fly with AWS Inferentia2 In a previous post on the Hugging Face blog, we introduced AWS Inferentia2, the second-generation AWS Inferentia accelerator, and explained how you could use optimum-neuron to quickly deploy Hugging Face models for standard text and vision tasks on AWS Inferencia 2 instances. In a further step of integration with the AWS Neuron SDK, it is now possible to use 🤗 optimum-neuron to deploy LLM models for text generation on AWS Inferentia2. And what better model could we choose for that demonstration than Llama 2, one of the most popular models on the Hugging Face hub.
|
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] |
Make your llama generation time fly with AWS Inferentia2
|
dacorvo
|
November 7, 2023
|
inferentia-llama2
|
guide, text-generation, llama2, aws
|
https://huggingface.co/blog/inferentia-llama2
|
Benchmarks But how much efficient is text-generation on Inferentia2? Let's figure out! We have uploaded on the hub pre-compiled versions of the LLama 2 7B and 13B models with different configurations: | Model type | num cores | batch_size | Hugging Face Hub model | |----------------------------|-----------|------------|-------------------------------------------| | Llama2 7B - B (budget) | 2 | 1 |aws-neuron/Llama-2-7b-hf-neuron-budget | | Llama2 7B - L (latency) | 24 | 1 |aws-neuron/Llama-2-7b-hf-neuron-latency | | Llama2 7B - T (throughput) | 24 | 4 |aws-neuron/Llama-2-7b-hf-neuron-throughput | | Llama2 13B - L (latency) | 24 | 1 |aws-neuron/Llama-2-13b-hf-neuron-latency | | Llama2 13B - T (throughput)| 24 | 4 |aws-neuron/Llama-2-13b-hf-neuron-throughput| Note: all models are compiled with a maximum sequence length of 2048. The llama2 7B "budget" model is meant to be deployed on inf2.xlarge instance that has only one neuron device, and enough cpu memory to load the model. All other models are compiled to use the full extent of cores available on the inf2.48xlarge instance. Note: please refer to the inferentia2 product page for details on the available instances. We created two "latency" oriented configurations for the llama2 7B and llama2 13B models that can serve only one request at a time, but at full speed. We also created two "throughput" oriented configurations to serve up to four requests in parallel. To evaluate the models, we generate tokens up to a total sequence length of 1024, starting from 256 input tokens (i.e. we generate 256, 512 and 768 tokens). Note: the "budget" model numbers are reported but not included in the graphs for better readability.
|
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] |
Make your llama generation time fly with AWS Inferentia2
|
dacorvo
|
November 7, 2023
|
inferentia-llama2
|
guide, text-generation, llama2, aws
|
https://huggingface.co/blog/inferentia-llama2
|
Setup 🤗 optimum-neuron on your Inferentia2 instance Our recommendation is to use the Hugging Face Neuron Deep Learning AMI (DLAMI). The DLAMI comes with all required libraries pre-packaged for you, including the Optimum Neuron, Neuron Drivers, Transformers, Datasets, and Accelerate. Alternatively, you can use the Hugging Face Neuron SDK DLC to deploy on Amazon SageMaker. Note: stay tuned for an upcoming post dedicated to SageMaker deployment. Finally, these components can also be installed manually on a fresh Inferentia2 instance following the optimum-neuron installation instructions.
|
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] |
Make your llama generation time fly with AWS Inferentia2
|
dacorvo
|
November 7, 2023
|
inferentia-llama2
|
guide, text-generation, llama2, aws
|
https://huggingface.co/blog/inferentia-llama2
|
Encoding time The encoding time is the time required to process the input tokens and generate the first output token. It is a very important metric, as it corresponds to the latency directly perceived by the user when streaming generated tokens. We test the encoding time for increasing context sizes, 256 input tokens corresponding roughly to a typical Q/A usage, while 768 is more typical of a Retrieval Augmented Generation (RAG) use-case. The "budget" model (Llama2 7B-B) is deployed on an inf2.xlarge instance while other models are deployed on an inf2.48xlarge instance. Encoding time is expressed in seconds. | input tokens | Llama2 7B-L | Llama2 7B-T | Llama2 13B-L | Llama2 13B-T | Llama2 7B-B | |-----------------|----------------|----------------|-----------------|-----------------|----------------| | 256 | 0.5 | 0.9 | 0.6 | 1.8 | 0.3 | | 512 | 0.7 | 1.6 | 1.1 | 3.0 | 0.4 | | 768 | 1.1 | 3.3 | 1.7 | 5.2 | 0.5 | We can see that all deployed models exhibit excellent response times, even for long contexts.
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] |
Make your llama generation time fly with AWS Inferentia2
|
dacorvo
|
November 7, 2023
|
inferentia-llama2
|
guide, text-generation, llama2, aws
|
https://huggingface.co/blog/inferentia-llama2
|
Throughput We adopt the same convention as other benchmarks to evaluate the throughput, by dividing the end-to-end latency by the sum of both input and output tokens. In other words, we divide the end-to-end latency by batch_size * sequence_length to obtain the number of generated tokens per second. The "budget" model (Llama2 7B-B) is deployed on an inf2.xlarge instance while other models are deployed on an inf2.48xlarge instance. Throughput is expressed in tokens/second. | new tokens | Llama2 7B-L | Llama2 7B-T | Llama2 13B-L | Llama2 13B-T | Llama2 7B-B | |---------------|----------------|----------------|-----------------|-----------------|----------------| | 256 | 227 | 750 | 145 | 504 | 32 | | 512 | 177 | 579 | 111 | 394 | 24 | | 768 | 164 | 529 | 101 | 370 | 22 | Again, the models deployed on the high-end instance have a very good throughput, even those optimized for latency. The "budget" model has a much lower throughput, but still ok for a streaming use-case, considering that an average reader reads around 5 words per-second.
|
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] |
Make your llama generation time fly with AWS Inferentia2
|
dacorvo
|
November 7, 2023
|
inferentia-llama2
|
guide, text-generation, llama2, aws
|
https://huggingface.co/blog/inferentia-llama2
|
Conclusion We have illustrated how easy it is to deploy llama2 models from the Hugging Face hub on AWS Inferentia2 using 🤗 optimum-neuron. The deployed models demonstrate very good performance in terms of encoding time, latency and throughput. Interestingly, the deployed models latency is not too sensitive to the batch size, which opens the way for their deployment on inference endpoints serving multiple requests in parallel. There is still plenty of room for improvement though: - in the current implementation, the only way to augment the throughput is to increase the batch size, but it is currently limited by the device memory. Alternative options such as pipelining are currently integrated, - the static sequence length limits the model ability to encode long contexts. It would be interesting to see if attention sinks might be a valid option to address this.
|
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] |
Make your llama generation time fly with AWS Inferentia2
|
dacorvo
|
November 7, 2023
|
inferentia-llama2
|
guide, text-generation, llama2, aws
|
https://huggingface.co/blog/inferentia-llama2
|
Export the Llama 2 model to Neuron As explained in the optimum-neuron documentation, models need to be compiled and exported to a serialized format before running them on Neuron devices. Fortunately, 🤗 optimum-neuron offers a very simple API to export standard 🤗 transformers models to the Neuron format. >>> from optimum.neuron import NeuronModelForCausalLM >>> compiler_args = {"num_cores": 24, "auto_cast_type": 'fp16'} >>> input_shapes = {"batch_size": 1, "sequence_length": 2048} >>> model = NeuronModelForCausalLM.from_pretrained( "meta-llama/Llama-2-7b-hf", export=True, **compiler_args, **input_shapes) This deserves a little explanation: - using compiler_args, we specify on how many cores we want the model to be deployed (each neuron device has two cores), and with which precision (here float16), - using input_shape, we set the static input and output dimensions of the model. All model compilers require static shapes, and neuron makes no exception. Note that the sequence_length not only constrains the length of the input context, but also the length of the KV cache, and thus, the output length. Depending on your choice of parameters and inferentia host, this may take from a few minutes to more than an hour. Fortunately, you will need to do this only once because you can save your model and reload it later. >>> model.save_pretrained("a_local_path_for_compiled_neuron_model") Even better, you can push it to the Hugging Face hub. >>> model.push_to_hub( "a_local_path_for_compiled_neuron_model", repository_id="aws-neuron/Llama-2-7b-hf-neuron-latency")
|
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Make your llama generation time fly with AWS Inferentia2
|
dacorvo
|
November 7, 2023
|
inferentia-llama2
|
guide, text-generation, llama2, aws
|
https://huggingface.co/blog/inferentia-llama2
|
All-in-one with optimum-neuron pipelines For those who like to keep it simple, there is an even simpler way to use an LLM model on AWS inferentia 2 using optimum-neuron pipelines. Using them is as simple as: >>> from optimum.neuron import pipeline >>> p = pipeline('text-generation', 'aws-neuron/Llama-2-7b-hf-neuron-budget') >>> p("My favorite place on earth is", max_new_tokens=64, do_sample=True, top_k=50) [{'generated_text': 'My favorite place on earth is the ocean. It is where I feel most at peace. I love to travel and see new places. I have a'}]
|
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] |
Make your llama generation time fly with AWS Inferentia2
|
dacorvo
|
November 7, 2023
|
inferentia-llama2
|
guide, text-generation, llama2, aws
|
https://huggingface.co/blog/inferentia-llama2
|
End-to-end Latency The end-to-end latency corresponds to the total time to reach a sequence length of 1024 tokens. It therefore includes the encoding and generation time. The "budget" model (Llama2 7B-B) is deployed on an inf2.xlarge instance while other models are deployed on an inf2.48xlarge instance. Latency is expressed in seconds. | new tokens | Llama2 7B-L | Llama2 7B-T | Llama2 13B-L | Llama2 13B-T | Llama2 7B-B | |---------------|----------------|----------------|-----------------|-----------------|----------------| | 256 | 2.3 | 2.7 | 3.5 | 4.1 | 15.9 | | 512 | 4.4 | 5.3 | 6.9 | 7.8 | 31.7 | | 768 | 6.2 | 7.7 | 10.2 | 11.1 | 47.3 | All models deployed on the high-end instance exhibit a good latency, even those actually configured to optimize throughput. The "budget" deployed model latency is significantly higher, but still ok.
|
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] |
SDXL in 4 steps with Latent Consistency LoRAs
|
pcuenq
|
November 9, 2023
|
lcm_lora
|
sdxl, lcm, stable diffusion, guide
|
https://huggingface.co/blog/lcm_lora
|
Resources - Latent Consistency Models project page, paper. - LCM LoRAs - For SDXL. - For Stable Diffusion v1.5. - For Segmind's SSD-1B. - Technical Report. - Demos - SDXL in 4 steps with Latent Consistency LoRAs - Near real-time video stream - LoRA the Explorer (experimental LCM version) - PEFT: intro, repo - Training scripts - For Stable Diffusion 1.5 - For SDXL
|
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] |
SDXL in 4 steps with Latent Consistency LoRAs
|
pcuenq
|
November 9, 2023
|
lcm_lora
|
sdxl, lcm, stable diffusion, guide
|
https://huggingface.co/blog/lcm_lora
|
Full Diffusers Integration The integration of LCM in diffusers makes it possible to take advantage of many features and workflows that are part of the diffusers toolbox. For example: - Out of the box mps support for Macs with Apple Silicon. - Memory and performance optimizations like flash attention or torch.compile(). - Additional memory saving strategies for low-RAM environments, including model offload. - Workflows like ControlNet or image-to-image. - Training and fine-tuning scripts.
|
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] |
SDXL in 4 steps with Latent Consistency LoRAs
|
pcuenq
|
November 9, 2023
|
lcm_lora
|
sdxl, lcm, stable diffusion, guide
|
https://huggingface.co/blog/lcm_lora
|
Bonus: Combine LCM LoRAs with regular SDXL LoRAs Using the diffusers + PEFT integration, you can combine LCM LoRAs with regular SDXL LoRAs, giving them the superpower to run LCM inference in only 4 steps. Here we are going to combine CiroN2022/toy_face LoRA with the LCM LoRA: py from diffusers import DiffusionPipeline, LCMScheduler import torch model_id = "stabilityai/stable-diffusion-xl-base-1.0" lcm_lora_id = "latent-consistency/lcm-lora-sdxl" pipe = DiffusionPipeline.from_pretrained(model_id, variant="fp16") pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) pipe.load_lora_weights(lcm_lora_id) pipe.load_lora_weights("CiroN2022/toy-face", weight_name="toy_face_sdxl.safetensors", adapter_name="toy") pipe.set_adapters(["lora", "toy"], adapter_weights=[1.0, 0.8]) pipe.to(device="cuda", dtype=torch.float16) prompt = "a toy_face man" negative_prompt = "blurry, low quality, render, 3D, oversaturated" images = pipe( prompt=prompt, negative_prompt=negative_prompt, num_inference_steps=4, guidance_scale=0.5, ).images[0] images Standard and LCM LoRAs combined for fast (4 step) inference. Need ideas to explore some LoRAs? Check out our experimental LoRA the Explorer (LCM version) Space to test amazing creations by the community and get inspired!
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] |
SDXL in 4 steps with Latent Consistency LoRAs
|
pcuenq
|
November 9, 2023
|
lcm_lora
|
sdxl, lcm, stable diffusion, guide
|
https://huggingface.co/blog/lcm_lora
|
Method Overview So, what’s the trick? For latent consistency distillation, each model needs to be distilled separately. The core idea with LCM LoRA is to train just a small number of adapters, known as LoRA layers, instead of the full model. The resulting LoRAs can then be applied to any fine-tuned version of the model without having to distil them separately. If you are itching to see how this looks in practice, just jump to the next section to play with the inference code. If you want to train your own LoRAs, this is the process you’d use: 1. Select an available teacher model from the Hub. For example, you can use SDXL (base), or any fine-tuned or dreamboothed version you like. 2. Train a LCM LoRA on the model. LoRA is a type of performance-efficient fine-tuning, or PEFT, that is much cheaper to accomplish than full model fine-tuning. For additional details on PEFT, please check this blog post or the diffusers LoRA documentation. 3. Use the LoRA with any SDXL diffusion model and the LCM scheduler; bingo! You get high-quality inference in just a few steps. For more details on the process, please download our paper.
|
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] |
SDXL in 4 steps with Latent Consistency LoRAs
|
pcuenq
|
November 9, 2023
|
lcm_lora
|
sdxl, lcm, stable diffusion, guide
|
https://huggingface.co/blog/lcm_lora
|
Quality Comparison Let’s see how the number of steps impacts generation quality. The following code will generate images with 1 to 8 total inference steps: py images = [] for steps in range(8): generator = torch.Generator(device=pipe.device).manual_seed(1337) image = pipe( prompt=prompt, num_inference_steps=steps+1, guidance_scale=1, generator=generator, ).images[0] images.append(image) These are the 8 images displayed in a grid: LCM LoRA generations with 1 to 8 steps. As expected, using just 1 step produces an approximate shape without discernible features and lacking texture. However, results quickly improve, and they are usually very satisfactory in just 4 to 6 steps. Personally, I find the 8-step image in the previous test to be a bit too saturated and “cartoony” for my taste, so I’d probably choose between the ones with 5 and 6 steps in this example. Generation is so fast that you can create a bunch of different variants using just 4 steps, and then select the ones you like and iterate using a couple more steps and refined prompts as necessary.
|
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] |
SDXL in 4 steps with Latent Consistency LoRAs
|
pcuenq
|
November 9, 2023
|
lcm_lora
|
sdxl, lcm, stable diffusion, guide
|
https://huggingface.co/blog/lcm_lora
|
LCM LoRAs and Models Released Today - Latent Consistency Models LoRAs Collection - latent-consistency/lcm-lora-sdxl. LCM LoRA for SDXL 1.0 base, as seen in the examples above. - latent-consistency/lcm-lora-sdv1-5. LCM LoRA for Stable Diffusion 1.5. - latent-consistency/lcm-lora-ssd-1b. LCM LoRA for segmind/SSD-1B, a distilled SDXL model that's 50% smaller and 60% faster than the original SDXL. - latent-consistency/lcm-sdxl. Full fine-tuned consistency model derived from SDXL 1.0 base. - latent-consistency/lcm-ssd-1b. Full fine-tuned consistency model derived from segmind/SSD-1B.
|
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] |
SDXL in 4 steps with Latent Consistency LoRAs
|
pcuenq
|
November 9, 2023
|
lcm_lora
|
sdxl, lcm, stable diffusion, guide
|
https://huggingface.co/blog/lcm_lora
|
Credits The amazing work on Latent Consistency Models was performed by the LCM Team, please make sure to check out their code, report and paper. This project is a collaboration between the diffusers team, the LCM team, and community contributor Daniel Gu. We believe it's a testament to the enabling power of open source AI, the cornerstone that allows researchers, practitioners and tinkerers to explore new ideas and collaborate. We'd also like to thank @madebyollin for their continued contributions to the community, including the float16 autoencoder we use in our training scripts.
|
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] |
SDXL in 4 steps with Latent Consistency LoRAs
|
pcuenq
|
November 9, 2023
|
lcm_lora
|
sdxl, lcm, stable diffusion, guide
|
https://huggingface.co/blog/lcm_lora
|
Contents - Method Overview - Why does this matter - Fast Inference with SDXL LCM LoRAs - Quality Comparison - Guidance Scale and Negative Prompts - Quality vs base SDXL - LCM LoRAs with other Models - Full Diffusers Integration - Benchmarks - LCM LoRAs and Models Released Today - Bonus: Combine LCM LoRAs with regular SDXL LoRAs - How to train LCM LoRAs - Resources - Credits
|
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] |
SDXL in 4 steps with Latent Consistency LoRAs
|
pcuenq
|
November 9, 2023
|
lcm_lora
|
sdxl, lcm, stable diffusion, guide
|
https://huggingface.co/blog/lcm_lora
|
LCM LoRAs with other models This technique also works for any other fine-tuned SDXL or Stable Diffusion model. To demonstrate, let's see how to run inference on collage-diffusion, a model fine-tuned from Stable Diffusion v1.5 using Dreambooth. The code is similar to the one we saw in the previous examples. We load the fine-tuned model, and then the LCM LoRA suitable for Stable Diffusion v1.5. py from diffusers import DiffusionPipeline, LCMScheduler import torch model_id = "wavymulder/collage-diffusion" lcm_lora_id = "latent-consistency/lcm-lora-sdv1-5" pipe = DiffusionPipeline.from_pretrained(model_id, variant="fp16") pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) pipe.load_lora_weights(lcm_lora_id) pipe.to(device="cuda", dtype=torch.float16) prompt = "collage style kid sits looking at the night sky, full of stars" generator = torch.Generator(device=pipe.device).manual_seed(1337) images = pipe( prompt=prompt, generator=generator, negative_prompt=negative_prompt, num_inference_steps=4, guidance_scale=1, ).images[0] images LCM LoRA technique with a Dreambooth Stable Diffusion v1.5 model, allowing 4-step inference.
|
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] |
SDXL in 4 steps with Latent Consistency LoRAs
|
pcuenq
|
November 9, 2023
|
lcm_lora
|
sdxl, lcm, stable diffusion, guide
|
https://huggingface.co/blog/lcm_lora
|
Fast Inference with SDXL LCM LoRAs The version of diffusers released today makes it very easy to use LCM LoRAs: py from diffusers import DiffusionPipeline, LCMScheduler import torch model_id = "stabilityai/stable-diffusion-xl-base-1.0" lcm_lora_id = "latent-consistency/lcm-lora-sdxl" pipe = DiffusionPipeline.from_pretrained(model_id, variant="fp16") pipe.load_lora_weights(lcm_lora_id) pipe.scheduler = LCMScheduler.from_config(pipe.scheduler.config) pipe.to(device="cuda", dtype=torch.float16) prompt = "close-up photography of old man standing in the rain at night, in a street lit by lamps, leica 35mm summilux" images = pipe( prompt=prompt, num_inference_steps=4, guidance_scale=1, ).images[0] Note how the code: - Instantiates a standard diffusion pipeline with the SDXL 1.0 base model. - Applies the LCM LoRA. - Changes the scheduler to the LCMScheduler, which is the one used in latent consistency models. - That’s it! This would result in the following full-resolution image: Image generated with SDXL in 4 steps using an LCM LoRA.
|
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] |
SDXL in 4 steps with Latent Consistency LoRAs
|
pcuenq
|
November 9, 2023
|
lcm_lora
|
sdxl, lcm, stable diffusion, guide
|
https://huggingface.co/blog/lcm_lora
|
Guidance Scale and Negative Prompts Note that in the previous examples we used a guidance_scale of 1, which effectively disables it. This works well for most prompts, and it’s fastest, but ignores negative prompts. You can also explore using negative prompts by providing a guidance scale between 1 and 2 – we found that larger values don’t work.
|
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] |
SDXL in 4 steps with Latent Consistency LoRAs
|
pcuenq
|
November 9, 2023
|
lcm_lora
|
sdxl, lcm, stable diffusion, guide
|
https://huggingface.co/blog/lcm_lora
|
How to Train LCM Models and LoRAs As part of the diffusers release today, we are providing training and fine-tuning scripts developed in collaboration with the LCM team authors. They allow users to: - Perform full-model distillation of Stable Diffusion or SDXL models on large datasets such as Laion. - Train LCM LoRAs, which is a much easier process. As we've shown in this post, it also makes it possible to run fast inference with Stable Diffusion, without having to go through distillation training. For more details, please check the instructions for SDXL or Stable Diffusion in the repo. We hope these scripts inspire the community to try their own fine-tunes. Please, do let us know if you use them for your projects!
|
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] |
SDXL in 4 steps with Latent Consistency LoRAs
|
pcuenq
|
November 9, 2023
|
lcm_lora
|
sdxl, lcm, stable diffusion, guide
|
https://huggingface.co/blog/lcm_lora
|
Benchmarks This section is not meant to be exhaustive, but illustrative of the generation speed we achieve on various computers. Let us stress again how liberating it is to explore image generation so easily. | Hardware | SDXL LoRA LCM (4 steps) | SDXL standard (25 steps) | |----------------------------------------|-------------------------|--------------------------| | Mac, M1 Max | 6.5s | 64s | | 2080 Ti | 4.7s | 10.2s | | 3090 | 1.4s | 7s | | 4090 | 0.7s | 3.4s | | T4 (Google Colab Free Tier) | 8.4s | 26.5s | | A100 (80 GB) | 1.2s | 3.8s | | Intel i9-10980XE CPU (1/36 cores used) | 29s | 219s | These tests were run with a batch size of 1 in all cases, using this script by Sayak Paul. For cards with a lot of capacity, such as A100, performance increases significantly when generating multiple images at once, which is usually the case for production workloads.
|
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] |
SDXL in 4 steps with Latent Consistency LoRAs
|
pcuenq
|
November 9, 2023
|
lcm_lora
|
sdxl, lcm, stable diffusion, guide
|
https://huggingface.co/blog/lcm_lora
|
# SDXL in 4 steps with Latent Consistency LoRAs Latent Consistency Models (LCM) are a way to decrease the number of steps required to generate an image with Stable Diffusion (or SDXL) by distilling the original model into another version that requires fewer steps (4 to 8 instead of the original 25 to 50). Distillation is a type of training procedure that attempts to replicate the outputs from a source model using a new one. The distilled model may be designed to be smaller (that’s the case of DistilBERT or the recently-released Distil-Whisper) or, in this case, require fewer steps to run. It’s usually a lengthy and costly process that requires huge amounts of data, patience, and a few GPUs. Well, that was the status quo before today! We are delighted to announce a new method that can essentially make Stable Diffusion and SDXL faster, as if they had been distilled using the LCM process! How does it sound to run any SDXL model in about 1 second instead of 7 on a 3090, or 10x faster on Mac? Read on for details!
|
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0.04326567426323891,
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SDXL in 4 steps with Latent Consistency LoRAs
|
pcuenq
|
November 9, 2023
|
lcm_lora
|
sdxl, lcm, stable diffusion, guide
|
https://huggingface.co/blog/lcm_lora
|
Why does this matter? Fast inference of Stable Diffusion and SDXL enables new use-cases and workflows. To name a few: - Accessibility: generative tools can be used effectively by more people, even if they don’t have access to the latest hardware. - Faster iteration: get more images and multiple variants in a fraction of the time! This is great for artists and researchers; whether for personal or commercial use. - Production workloads may be possible on different accelerators, including CPUs. - Cheaper image generation services. To gauge the speed difference we are talking about, generating a single 1024x1024 image on an M1 Mac with SDXL (base) takes about a minute. Using the LCM LoRA, we get great results in just ~6s (4 steps). This is an order of magnitude faster, and not having to wait for results is a game-changer. Using a 4090, we get almost instant response (less than 1s). This unlocks the use of SDXL in applications where real-time events are a requirement.
|
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] |
SDXL in 4 steps with Latent Consistency LoRAs
|
pcuenq
|
November 9, 2023
|
lcm_lora
|
sdxl, lcm, stable diffusion, guide
|
https://huggingface.co/blog/lcm_lora
|
Quality vs base SDXL How does this compare against the standard SDXL pipeline, in terms of quality? Let’s see an example! We can quickly revert our pipeline to a standard SDXL pipeline by unloading the LoRA weights and switching to the default scheduler: py from diffusers import EulerDiscreteScheduler pipe.unload_lora_weights() pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config) Then we can run inference as usual for SDXL. We’ll gather results using varying number of steps: py images = [] for steps in (1, 4, 8, 15, 20, 25, 30, 50): generator = torch.Generator(device=pipe.device).manual_seed(1337) image = pipe( prompt=prompt, num_inference_steps=steps, generator=generator, ).images[0] images.append(image) SDXL pipeline results (same prompt and random seed), using 1, 4, 8, 15, 20, 25, 30, and 50 steps. As you can see, images in this example are pretty much useless until ~20 steps (second row), and quality still increases niteceably with more steps. The details in the final image are amazing, but it took 50 steps to get there.
|
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] |
Open LLM Leaderboard: DROP deep dive
|
clefourrier
|
December 1, 2023
|
leaderboard-drop-dive
|
community, research, nlp, evaluation, leaderboard
|
https://huggingface.co/blog/leaderboard-drop-dive
|
Changing the end of generation token So we gave it a try! We investigated using \n as the end of generation token on the available results. We split the generated answer on the first \n it contained, if one was present, and recomputed the scores. Note that this is only an approximation of the correct result, as it won't fix answers that were cut too early on . (for example floating point answers) - but it also won’t give unfair advantage to any model, as all of them were affected by this problem. However it’s the best we could do without rerunning models (as we wanted to keep the community posted as soon as possible). The results we got were the following - splitting on \n correlates really well with other scores and therefore with overall performance. We can see in orange that the scores computed on the new strings correlate much better with the average performance.
|
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] |
Open LLM Leaderboard: DROP deep dive
|
clefourrier
|
December 1, 2023
|
leaderboard-drop-dive
|
community, research, nlp, evaluation, leaderboard
|
https://huggingface.co/blog/leaderboard-drop-dive
|
So what's next? A quick calculation shows that re-running the full evaluation of all models would be quite costly (the full update took 8 years of GPU time, and a lot of it was taken by DROP), we estimated how much it would cost to only re-run failing examples. In 10% of the cases, the gold answer is a floating number (for example 12.25) and model predictions start with the correct beginning (for our example, 12) but are cut off on a . - these predictions likely would have actually been correct if the generation was to continue. We would definitely need to re-run them! Our estimation does not count generated sentences that finish with a number which was possibly interrupted (40% of the other generations), nor any prediction messed up by its normalization. To get correct results, we would thus need to re-run more than 50% of the examples, a huge amount of GPU time! We need to be certain that the implementation we'll run is correct this time. After discussing it with the fantastic EleutherAI team (both on GitHub and internally), who guided us through the code and helped our investigations, it became very clear that the LM Eval Harness implementation follows the "official DROP" code very strictly: a new version of this benchmark’s evaluation thus needs to be developed! We have therefore taken the decision to remove DROP from the Open LLM Leaderboard until a new version arises. One take away of this investiguation is the value in having the many eyes of the community collaboratively investiguate a benchmark in order to detect errors that were previously missed. Here again the power of open-source, community and developping in the open-shines in that it allows to transparently investigate the root cause of an issue on a benchmark which has been out there for a couple of years. We hope that interested members of the community will join forces with academics working on DROP evaluation to fix both its scoring and its normalization. We'd love it becomes usable again, as the dataset itself is really quite interesting and cool. We encourage you to provide feedback on how we should evaluate DROP on this issue. Thanks to the many community members who pointed out issues on DROP scores, and many thanks to the EleutherAI Harness and Zeno teams for their great help on this issue.
|
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0.009056121110916138,
0.011439600959420204,
-0.03614192083477974,
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0.022021887823939323,
0.011830589734017849,
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0.035788118839263916,
0.01670384220778942,
0.0116372499614954,
0.01273287646472454,
0.007922236807644367,
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0.07141537219285965,
-0.0396757535636425,
-0.023674778640270233,
0.05068385601043701
] |
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