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Introducing IDEFICS: An Open Reproduction of State-of-the-art Visual Language Model
|
VictorSanh
|
August 22, 2023
|
idefics
|
research, nlp, cv
|
https://huggingface.co/blog/idefics
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# Introducing IDEFICS: An Open Reproduction of State-of-the-Art Visual Language Model We are excited to release IDEFICS (Image-aware Decoder Enhanced à la Flamingo with Interleaved Cross-attentionS), an open-access visual language model. IDEFICS is based on Flamingo, a state-of-the-art visual language model initially developed by DeepMind, which has not been released publicly. Similarly to GPT-4, the model accepts arbitrary sequences of image and text inputs and produces text outputs. IDEFICS is built solely on publicly available data and models (LLaMA v1 and OpenCLIP) and comes in two variants—the base version and the instructed version. Each variant is available at the 9 billion and 80 billion parameter sizes. The development of state-of-the-art AI models should be more transparent. Our goal with IDEFICS is to reproduce and provide the AI community with systems that match the capabilities of large proprietary models like Flamingo. As such, we took important steps contributing to bringing transparency to these AI systems: we used only publicly available data, we provided tooling to explore training datasets, we shared technical lessons and mistakes of building such artifacts and assessed the model’s harmfulness by adversarially prompting it before releasing it. We are hopeful that IDEFICS will serve as a solid foundation for more open research in multimodal AI systems, alongside models like OpenFlamingo-another open reproduction of Flamingo at the 9 billion parameter scale. Try out the demo and the models on the Hub!
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] |
Introducing IDEFICS: An Open Reproduction of State-of-the-art Visual Language Model
|
VictorSanh
|
August 22, 2023
|
idefics
|
research, nlp, cv
|
https://huggingface.co/blog/idefics
|
What is IDEFICS? IDEFICS is an 80 billion parameters multimodal model that accepts sequences of images and texts as input and generates coherent text as output. It can answer questions about images, describe visual content, create stories grounded in multiple images, etc. IDEFICS is an open-access reproduction of Flamingo and is comparable in performance with the original closed-source model across various image-text understanding benchmarks. It comes in two variants - 80 billion parameters and 9 billion parameters. We also provide fine-tuned versions idefics-80B-instruct and idefics-9B-instruct adapted for conversational use cases.
|
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] |
Introducing IDEFICS: An Open Reproduction of State-of-the-art Visual Language Model
|
VictorSanh
|
August 22, 2023
|
idefics
|
research, nlp, cv
|
https://huggingface.co/blog/idefics
|
Ethical evaluation At the outset of this project, through a set of discussions, we developed an ethical charter that would help steer decisions made during the project. This charter sets out values, including being self-critical, transparent, and fair which we have sought to pursue in how we approached the project and the release of the models. As part of the release process, we internally evaluated the model for potential biases by adversarially prompting the model with images and text that might elicit responses we do not want from the model (a process known as red teaming). Please try out IDEFICS with the demo, check out the corresponding model cards and dataset card and let us know your feedback using the community tab! We are committed to improving these models and making large multimodal AI models accessible to the machine learning community.
|
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] |
Introducing SafeCoder
|
jeffboudier
|
August 22, 2023
|
safecoder
|
announcement, partnerships, vmware, bigcode
|
https://huggingface.co/blog/safecoder
|
Using SafeCoder Once SafeCoder is deployed and its endpoints are live within the customer VPC, developers can install compatible SafeCoder IDE plugins to get code suggestions as they work. Today, SafeCoder supports popular IDEs, including VSCode, IntelliJ and with more plugins coming from our partners.
|
[
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] |
Introducing SafeCoder
|
jeffboudier
|
August 22, 2023
|
safecoder
|
announcement, partnerships, vmware, bigcode
|
https://huggingface.co/blog/safecoder
|
How does it work? SafeCoder is a complete commercial solution, including service, software and support.
|
[
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] |
Introducing SafeCoder
|
jeffboudier
|
August 22, 2023
|
safecoder
|
announcement, partnerships, vmware, bigcode
|
https://huggingface.co/blog/safecoder
|
Compliance as a Core Principle As the regulation framework around machine learning models and datasets is still being written across the world, global companies need to make sure the solutions they use minimize legal risks. Data sources, data governance, management of copyrighted data are just a few of the most important compliance areas to consider. BigScience, the older cousin and inspiration for BigCode, addressed these areas in working groups before they were broadly recognized by the draft AI EU Act, and as a result was graded as most compliant among Foundational Model Providers in a Stanford CRFM study. BigCode expanded upon this work by implementing novel techniques for the code domain and building The Stack with compliance as a core principle, such as commercially permissible license filtering, consent mechanisms (developers can easily find out if their code is present and request to be opted out of the dataset), and extensive documentation and tools to inspect the source data, and dataset improvements (such as deduplication and PII removal). All these efforts translate into legal risk minimization for users of the StarCoder models, and customers of SafeCoder. And for SafeCoder users, these efforts translate into compliance features: when software developers get code completions these suggestions are checked against The Stack, so users know if the suggested code matches existing code in the source dataset, and what the license is. Customers can specify which licenses are preferred and surface those preferences to their users.
|
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] |
Introducing SafeCoder
|
jeffboudier
|
August 22, 2023
|
safecoder
|
announcement, partnerships, vmware, bigcode
|
https://huggingface.co/blog/safecoder
|
Privacy and Security as a Core Principle For any company, the internal codebase is some of its most important and valuable intellectual property. A core principle of SafeCoder is that the customer internal codebase will never be accessible to any third party (including Hugging Face) during training or inference. In the initial set up phase of SafeCoder, the Hugging Face team provides containers, scripts and examples to work hand in hand with the customer to select, extract, prepare, duplicate, deidentify internal codebase data into a training dataset to be used in a Hugging Face provided training container configured to the hardware infrastructure available to the customer. In the deployment phase of SafeCoder, the customer deploys containers provided by Hugging Face on their own infrastructure to expose internal private endpoints within their VPC. These containers are configured to the exact hardware configuration available to the customer, including NVIDIA GPUs, AMD Instinct GPUs, Intel Xeon CPUs, AWS Inferentia2 or Habana Gaudi accelerators.
|
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] |
Introducing SafeCoder
|
jeffboudier
|
August 22, 2023
|
safecoder
|
announcement, partnerships, vmware, bigcode
|
https://huggingface.co/blog/safecoder
|
Training your own SafeCoder model StarCoder was trained in more than 80 programming languages and offers state of the art performance on multiple benchmarks. To offer better code suggestions specifically for a SafeCoder customer, we start the engagement with an optional training phase, where the Hugging Face team works directly with the customer team to guide them through the steps to prepare and build a training code dataset, and to create their own code generation model through fine-tuning, without ever exposing their codebase to third parties or the internet. The end result is a model that is adapted to the code languages, standards and practices of the customer. Through this process, SafeCoder customers learn the process and build a pipeline for creating and updating their own models, ensuring no vendor lock-in, and keeping control of their AI capabilities.
|
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] |
Introducing SafeCoder
|
jeffboudier
|
August 22, 2023
|
safecoder
|
announcement, partnerships, vmware, bigcode
|
https://huggingface.co/blog/safecoder
|
Why SafeCoder? Code assistant solutions built upon LLMs, such as GitHub Copilot, are delivering strong productivity boosts. For the enterprise, the ability to tune Code LLMs on the company code base to create proprietary Code LLMs improves reliability and relevance of completions to create another level of productivity boost. For instance, Google internal LLM code assistant reports a completion acceptance rate of 25-34% by being trained on an internal code base. However, relying on closed-source Code LLMs to create internal code assistants exposes companies to compliance and security issues. First during training, as fine-tuning a closed-source Code LLM on an internal codebase requires exposing this codebase to a third party. And then during inference, as fine-tuned Code LLMs are likely to “leak” code from their training dataset during inference. To meet compliance requirements, enterprises need to deploy fine-tuned Code LLMs within their own infrastructure - which is not possible with closed source LLMs. With SafeCoder, Hugging Face will help customers build their own Code LLMs, fine-tuned on their proprietary codebase, using state of the art open models and libraries, without sharing their code with Hugging Face or any other third party. With SafeCoder, Hugging Face delivers a containerized, hardware-accelerated Code LLM inference solution, to be deployed by the customer directly within the Customer secure infrastructure, without code inputs and completions leaving their secure IT environment.
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] |
Introducing SafeCoder
|
jeffboudier
|
August 22, 2023
|
safecoder
|
announcement, partnerships, vmware, bigcode
|
https://huggingface.co/blog/safecoder
|
# Introducing SafeCoder Today we are excited to announce SafeCoder - a code assistant solution built for the enterprise. The goal of SafeCoder is to unlock software development productivity for the enterprise, with a fully compliant and self-hosted pair programmer. In marketing speak: “your own on-prem GitHub copilot”. Before we dive deeper, here’s what you need to know: - SafeCoder is not a model, but a complete end-to-end commercial solution - SafeCoder is built with security and privacy as core principles - code never leaves the VPC during training or inference - SafeCoder is designed for self-hosting by the customer on their own infrastructure - SafeCoder is designed for customers to own their own Code Large Language Model
|
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] |
Introducing SafeCoder
|
jeffboudier
|
August 22, 2023
|
safecoder
|
announcement, partnerships, vmware, bigcode
|
https://huggingface.co/blog/safecoder
|
Deploying SafeCoder During the setup phase, SafeCoder customers and Hugging Face design and provision the optimal infrastructure to support the required concurrency to offer a great developer experience. Hugging Face then builds SafeCoder inference containers that are hardware-accelerated and optimized for throughput, to be deployed by the customer on their own infrastructure. SafeCoder inference supports various hardware to give customers a wide range of options: NVIDIA Ampere GPUs, AMD Instinct GPUs, Habana Gaudi2, AWS Inferentia 2, Intel Xeon Sapphire Rapids CPUs and more.
|
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] |
Introducing SafeCoder
|
jeffboudier
|
August 22, 2023
|
safecoder
|
announcement, partnerships, vmware, bigcode
|
https://huggingface.co/blog/safecoder
|
From StarCoder to SafeCoder At the core of the SafeCoder solution is the StarCoder family of Code LLMs, created by the BigCode project, a collaboration between Hugging Face, ServiceNow and the open source community. The StarCoder models offer unique characteristics ideally suited to enterprise self-hosted solution: - State of the art code completion results - see benchmarks in the paper and multilingual code evaluation leaderboard - Designed for inference performance: a 15B parameters model with code optimizations, Multi-Query Attention for reduced memory footprint, and Flash Attention to scale to 8,192 tokens context. - Trained on the Stack, an ethically sourced, open source code dataset containing only commercially permissible licensed code, with a developer opt-out mechanism from the get-go, refined through intensive PII removal and deduplication efforts. Note: While StarCoder is the inspiration and model powering the initial version of SafeCoder, an important benefit of building a LLM solution upon open source models is that it can adapt to the latest and greatest open source models available. In the future, SafeCoder may offer other similarly commercially permissible open source models built upon ethically sourced and transparent datasets as the base LLM available for fine-tuning.
|
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] |
Introducing SafeCoder
|
jeffboudier
|
August 22, 2023
|
safecoder
|
announcement, partnerships, vmware, bigcode
|
https://huggingface.co/blog/safecoder
|
How can I get SafeCoder? Today, we are announcing SafeCoder in collaboration with VMware at the VMware Explore conference and making SafeCoder available to VMware enterprise customers. Working with VMware helps ensure the deployment of SafeCoder on customers’ VMware Cloud infrastructure is successful – whichever cloud, on-premises or hybrid infrastructure scenario is preferred by the customer. In addition to utilizing SafeCoder, VMware has published a reference architecture with code samples to enable the fastest possible time-to-value when deploying and operating SafeCoder on VMware infrastructure. VMware’s Private AI Reference Architecture makes it easy for organizations to quickly leverage popular open source projects such as ray and kubeflow to deploy AI services adjacent to their private datasets, while working with Hugging Face to ensure that organizations maintain the flexibility to take advantage of the latest and greatest in open-source models. This is all without tradeoffs in total cost of ownership or performance. “Our collaboration with Hugging Face around SafeCoder fully aligns to VMware’s goal of enabling customer choice of solutions while maintaining privacy and control of their business data. In fact, we have been running SafeCoder internally for months and have seen excellent results. Best of all, our collaboration with Hugging Face is just getting started, and I’m excited to take our solution to our hundreds of thousands of customers worldwide,” says Chris Wolf, Vice President of VMware AI Labs. Learn more about private AI and VMware’s differentiation in this emerging space here. --- If you’re interested in SafeCoder for your company, please contact us here - our team will contact you to discuss your requirements!
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] |
Making LLMs lighter with AutoGPTQ and transformers
|
marcsun13
|
August 23, 2023
|
gptq-integration
|
llm, optimization, quantization
|
https://huggingface.co/blog/gptq-integration
|
Table of contents - Resources - A gentle summary of the GPTQ paper - AutoGPTQ library – the one-stop library for efficiently leveraging GPTQ for LLMs - Native support of GPTQ models in 🤗 Transformers - Quantizing models with the Optimum library - Running GPTQ models through Text-Generation-Inference - Fine-tune quantized models with PEFT - Room for improvement * Supported models - Conclusion and final words - Acknowledgements
|
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] |
Making LLMs lighter with AutoGPTQ and transformers
|
marcsun13
|
August 23, 2023
|
gptq-integration
|
llm, optimization, quantization
|
https://huggingface.co/blog/gptq-integration
|
Supported models In this initial implementation, only large language models with a decoder or encoder only architecture are supported. This may sound a bit restrictive, but it encompasses most state of the art LLMs such as Llama, OPT, GPT-Neo, GPT-NeoX. Very large vision, audio, and multi-modal models are currently not supported.
|
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] |
Making LLMs lighter with AutoGPTQ and transformers
|
marcsun13
|
August 23, 2023
|
gptq-integration
|
llm, optimization, quantization
|
https://huggingface.co/blog/gptq-integration
|
Native support of GPTQ models in 🤗 Transformers After installing the AutoGPTQ library and optimum (pip install optimum), running GPTQ models in Transformers is now as simple as: python from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("TheBloke/Llama-2-7b-Chat-GPTQ", torch_dtype=torch.float16, device_map="auto") Check out the Transformers documentation to learn more about all the features. Our AutoGPTQ integration has many advantages: - Quantized models are serializable and can be shared on the Hub. - GPTQ drastically reduces the memory requirements to run LLMs, while the inference latency is on par with FP16 inference. - AutoGPTQ supports Exllama kernels for a wide range of architectures. - The integration comes with native RoCm support for AMD GPUs. - Finetuning with PEFT is available. You can check on the Hub if your favorite model has already been quantized. TheBloke, one of Hugging Face top contributors, has quantized a lot of models with AutoGPTQ and shared them on the Hugging Face Hub. We worked together to make sure that these repositories will work out of the box with our integration. This is a benchmark sample for the batch size = 1 case. The benchmark was run on a single NVIDIA A100-SXM4-80GB GPU. We used a prompt length of 512, and generated exactly 512 new tokens. The first row is the unquantized fp16 baseline, while the other rows show memory consumption and performance using different AutoGPTQ kernels. | gptq | act_order | bits | group_size | kernel | Load time (s) | Per-token latency (ms) | Throughput (tokens/s) | Peak memory (MB) | |-------|-----------|------|------------|-------------------|---------------|------------------------|-----------------------|------------------| | False | None | None | None | None | 26.0 | 36.958 | 27.058 | 29152.98 | | True | False | 4 | 128 | exllama | 36.2 | 33.711 | 29.663 | 10484.34 | | True | False | 4 | 128 | autogptq-cuda-old | 36.2 | 46.44 | 21.53 | 10344.62 | A more comprehensive reproducible benchmark is available here.
|
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Making LLMs lighter with AutoGPTQ and transformers
|
marcsun13
|
August 23, 2023
|
gptq-integration
|
llm, optimization, quantization
|
https://huggingface.co/blog/gptq-integration
|
A gentle summary of the GPTQ paper Quantization methods usually belong to one of two categories: 1. Post-Training Quantization (PTQ): We quantize a pre-trained model using moderate resources, such as a calibration dataset and a few hours of computation. 2. Quantization-Aware Training (QAT): Quantization is performed before training or further fine-tuning. GPTQ falls into the PTQ category and this is particularly interesting for massive models, for which full model training or even fine-tuning can be very expensive. Specifically, GPTQ adopts a mixed int4/fp16 quantization scheme where weights are quantized as int4 while activations remain in float16. During inference, weights are dequantized on the fly and the actual compute is performed in float16. The benefits of this scheme are twofold: - Memory savings close to x4 for int4 quantization, as the dequantization happens close to the compute unit in a fused kernel, and not in the GPU global memory. - Potential speedups thanks to the time saved on data communication due to the lower bitwidth used for weights. The GPTQ paper tackles the layer-wise compression problem: Given a layer \(l\) with weight matrix \(W_{l}\) and layer input \(X_{l}\), we want to find a quantized version of the weight \(\hat{W}{l}\) to minimize the mean squared error (MSE): \({\hat{W}{l}}^{} = argmin_{\hat{W_{l}}} \|W_{l}X-\hat{W}{l}X\|^{2}{2}\) Once this is solved per layer, a solution to the global problem can be obtained by combining the layer-wise solutions. In order to solve this layer-wise compression problem, the author uses the Optimal Brain Quantization framework (Frantar et al 2022). The OBQ method starts from the observation that the above equation can be written as the sum of the squared errors, over each row of \(W_{l}\). \( \sum_{i=0}^{d_{row}} \|W_{l[i,:]}X-\hat{W}{l[i,:]}X\|^{2}{2} \) This means that we can quantize each row independently. This is called per-channel quantization. For each row \(W_{l[i,:]}\), OBQ quantizes one weight at a time while always updating all not-yet-quantized weights, in order to compensate for the error incurred by quantizing a single weight. The update on selected weights has a closed-form formula, utilizing Hessian matrices. The GPTQ paper improves this framework by introducing a set of optimizations that reduces the complexity of the quantization algorithm while retaining the accuracy of the model. Compared to OBQ, the quantization step itself is also faster with GPTQ: it takes 2 GPU-hours to quantize a BERT model (336M) with OBQ, whereas with GPTQ, a Bloom model (176B) can be quantized in less than 4 GPU-hours. To learn more about the exact algorithm and the different benchmarks on perplexity and speedups, check out the original paper.
|
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] |
Making LLMs lighter with AutoGPTQ and transformers
|
marcsun13
|
August 23, 2023
|
gptq-integration
|
llm, optimization, quantization
|
https://huggingface.co/blog/gptq-integration
|
Quantizing models with the Optimum library To seamlessly integrate AutoGPTQ into Transformers, we used a minimalist version of the AutoGPTQ API that is available in Optimum, Hugging Face's toolkit for training and inference optimization. By following this approach, we achieved easy integration with Transformers, while allowing people to use the Optimum API if they want to quantize their own models! Check out the Optimum documentation if you want to quantize your own LLMs. Quantizing 🤗 Transformers models with the GPTQ method can be done in a few lines: python from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig model_id = "facebook/opt-125m" tokenizer = AutoTokenizer.from_pretrained(model_id) quantization_config = GPTQConfig(bits=4, dataset = "c4", tokenizer=tokenizer) model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", quantization_config=quantization_config) Quantizing a model may take a long time. Note that for a 175B model, at least 4 GPU-hours are required if one uses a large dataset (e.g. `"c4"``). As mentioned above, many GPTQ models are already available on the Hugging Face Hub, which bypasses the need to quantize a model yourself in most use cases. Nevertheless, you can also quantize a model using your own dataset appropriate for the particular domain you are working on.
|
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] |
Making LLMs lighter with AutoGPTQ and transformers
|
marcsun13
|
August 23, 2023
|
gptq-integration
|
llm, optimization, quantization
|
https://huggingface.co/blog/gptq-integration
|
Running GPTQ models through Text-Generation-Inference In parallel to the integration of GPTQ in Transformers, GPTQ support was added to the Text-Generation-Inference library (TGI), aimed at serving large language models in production. GPTQ can now be used alongside features such as dynamic batching, paged attention and flash attention for a wide range of architectures. As an example, this integration allows to serve a 70B model on a single A100-80GB GPU! This is not possible using a fp16 checkpoint as it exceeds the available GPU memory. You can find out more about the usage of GPTQ in TGI in the documentation. Note that the kernel integrated in TGI does not scale very well with larger batch sizes. Although this approach saves memory, slowdowns are expected at larger batch sizes.
|
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] |
Making LLMs lighter with AutoGPTQ and transformers
|
marcsun13
|
August 23, 2023
|
gptq-integration
|
llm, optimization, quantization
|
https://huggingface.co/blog/gptq-integration
|
Room for improvement Our AutoGPTQ integration already brings impressive benefits at a small cost in the quality of prediction. There is still room for improvement, both in the quantization techniques and the kernel implementations. First, while AutoGPTQ integrates (to the best of our knowledge) with the most performant W4A16 kernel (weights as int4, activations as fp16) from the exllama implementation, there is a good chance that the kernel can still be improved. There have been other promising implementations from Kim et al. and from MIT Han Lab that appear to be promising. Moreover, from internal benchmarks, there appears to still be no open-source performant W4A16 kernel written in Triton, which could be a direction to explore. On the quantization side, let’s emphasize again that this method only quantizes the weights. There have been other approaches proposed for LLM quantization that can quantize both weights and activations at a small cost in prediction quality, such as LLM-QAT where a mixed int4/int8 scheme can be used, as well as quantization of the key-value cache. One of the strong advantages of this technique is the ability to use actual integer arithmetic for the compute, with e.g. Nvidia Tensor Cores supporting int8 compute. However, to the best of our knowledge, there are no open-source W4A8 quantization kernels available, but this may well be an interesting direction to explore. On the kernel side as well, designing performant W4A16 kernels for larger batch sizes remains an open challenge.
|
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] |
Making LLMs lighter with AutoGPTQ and transformers
|
marcsun13
|
August 23, 2023
|
gptq-integration
|
llm, optimization, quantization
|
https://huggingface.co/blog/gptq-integration
|
Conclusion and final words In this blogpost we have presented the integration of the AutoGPTQ library in Transformers, making it possible to quantize LLMs with the GPTQ method to make them more accessible for anyone in the community and empower them to build exciting tools and applications with LLMs. This integration is available both for Nvidia GPUs, and RoCm-powered AMD GPUs, which is a huge step towards democratizing quantized models for broader GPU architectures. The collaboration with the AutoGPTQ team has been very fruitful, and we are very grateful for their support and their work on this library. We hope that this integration will make it easier for everyone to use LLMs in their applications, and we are looking forward to seeing what you will build with it! Do not miss the useful resources shared above for better understanding the integration and how to quickly get started with GPTQ quantization. - Original Paper - Basic usage Google Colab notebook - This notebook shows how to quantize your transformers model with GPTQ method, how to do inference, and how to do fine-tuning with the quantized model. - Transformers integration documentation - Optimum integration documentation - The Bloke repositories with compatible GPTQ models.
|
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] |
Making LLMs lighter with AutoGPTQ and transformers
|
marcsun13
|
August 23, 2023
|
gptq-integration
|
llm, optimization, quantization
|
https://huggingface.co/blog/gptq-integration
|
AutoGPTQ library – the one-stop library for efficiently leveraging GPTQ for LLMs The AutoGPTQ library enables users to quantize 🤗 Transformers models using the GPTQ method. While parallel community efforts such as GPTQ-for-LLaMa, Exllama and llama.cpp implement quantization methods strictly for the Llama architecture, AutoGPTQ gained popularity through its smooth coverage of a wide range of transformer architectures. Since the AutoGPTQ library has a larger coverage of transformers models, we decided to provide an integrated 🤗 Transformers API to make LLM quantization more accessible to everyone. At this time we have integrated the most common optimization options, such as CUDA kernels. For more advanced options like Triton kernels or fused-attention compatibility, check out the AutoGPTQ library.
|
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] |
Making LLMs lighter with AutoGPTQ and transformers
|
marcsun13
|
August 23, 2023
|
gptq-integration
|
llm, optimization, quantization
|
https://huggingface.co/blog/gptq-integration
|
Resources This blogpost and release come with several resources to get started with GPTQ quantization: - Original Paper - Basic usage Google Colab notebook - This notebook shows how to quantize your transformers model with GPTQ method, how to do inference, and how to do fine-tuning with the quantized model. - Transformers integration documentation - Optimum integration documentation - The Bloke repositories with compatible GPTQ models.
|
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] |
Making LLMs lighter with AutoGPTQ and transformers
|
marcsun13
|
August 23, 2023
|
gptq-integration
|
llm, optimization, quantization
|
https://huggingface.co/blog/gptq-integration
|
Acknowledgements We would like to thank William for his support and his work on the amazing AutoGPTQ library and for his help in the integration. We would also like to thank TheBloke for his work on quantizing many models with AutoGPTQ and sharing them on the Hub and for his help with the integration. We would also like to aknowledge qwopqwop200 for his continuous contributions on AutoGPTQ library and his work on extending the library for CPU that is going to be released in the next versions of AutoGPTQ. Finally, we would like to thank Pedro Cuenca for his help with the writing of this blogpost.
|
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Making LLMs lighter with AutoGPTQ and transformers
|
marcsun13
|
August 23, 2023
|
gptq-integration
|
llm, optimization, quantization
|
https://huggingface.co/blog/gptq-integration
|
# Making LLMs lighter with AutoGPTQ and transformers Large language models have demonstrated remarkable capabilities in understanding and generating human-like text, revolutionizing applications across various domains. However, the demands they place on consumer hardware for training and deployment have become increasingly challenging to meet. 🤗 Hugging Face's core mission is to democratize good machine learning, and this includes making large models as accessible as possible for everyone. In the same spirit as our bitsandbytes collaboration, we have just integrated the AutoGPTQ library in Transformers, making it possible for users to quantize and run models in 8, 4, 3, or even 2-bit precision using the GPTQ algorithm (Frantar et al. 2023). There is negligible accuracy degradation with 4-bit quantization, with inference speed comparable to the fp16 baseline for small batch sizes. Note that GPTQ method slightly differs from post-training quantization methods proposed by bitsandbytes as it requires to pass a calibration dataset. This integration is available both for Nvidia GPUs, and RoCm-powered AMD GPUs.
|
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] |
Making LLMs lighter with AutoGPTQ and transformers
|
marcsun13
|
August 23, 2023
|
gptq-integration
|
llm, optimization, quantization
|
https://huggingface.co/blog/gptq-integration
|
Fine-tune quantized models with PEFT* You can not further train a quantized model using the regular methods. However, by leveraging the PEFT library, you can train adapters on top! To do that, we freeze all the layers of the quantized model and add the trainable adapters. Here are some examples on how to use PEFT with a GPTQ model: colab notebook and finetuning script.
|
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] |
Deprecation of Git Authentication using password
|
Sylvestre
|
August 25, 2023
|
password-git-deprecation
|
announcement, security
|
https://huggingface.co/blog/password-git-deprecation
|
Background In recent months, we have implemented various security enhancements, including sign-in alerts and support for SSH keys in Git. However, users have still been able to authenticate Git operations using their username and password. To further improve security, we are now transitioning to token-based or SSH key authentication. Token-based and SSH key authentication offer several advantages over traditional password authentication, including unique, revocable, and random features that enhance security and control.
|
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] |
Deprecation of Git Authentication using password
|
Sylvestre
|
August 25, 2023
|
password-git-deprecation
|
announcement, security
|
https://huggingface.co/blog/password-git-deprecation
|
Action Required Today If you currently use your HF account password to authenticate with Git, please switch to using a personal access token or SSH keys before October 1st, 2023.
|
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] |
Deprecation of Git Authentication using password
|
Sylvestre
|
August 25, 2023
|
password-git-deprecation
|
announcement, security
|
https://huggingface.co/blog/password-git-deprecation
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# Hugging Face Hub: Important Git Authentication Changes Because we are committed to improving the security of our services, we are making changes to the way you authenticate when interacting with the Hugging Face Hub through Git. Starting from October 1st, 2023, we will no longer accept passwords as a way to authenticate your command-line Git operations. Instead, we recommend using more secure authentication methods, such as replacing the password with a personal access token or using an SSH key.
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] |
Deprecation of Git Authentication using password
|
Sylvestre
|
August 25, 2023
|
password-git-deprecation
|
announcement, security
|
https://huggingface.co/blog/password-git-deprecation
|
Switching to SSH keys Follow our guide to generate an SSH key and add it to your account: https://huggingface.co/docs/hub/security-git-ssh Then you'll be able to update your Git repository using: bash $: git remote set-url origin [email protected]:<repo_path> # see above for the format of the repo path
|
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] |
Deprecation of Git Authentication using password
|
Sylvestre
|
August 25, 2023
|
password-git-deprecation
|
announcement, security
|
https://huggingface.co/blog/password-git-deprecation
|
Timeline Here's what you can expect in the coming weeks: - Today: Users relying on passwords for Git authentication may receive emails urging them to update their authentication method. - October 1st: Personal access tokens or SSH keys will be mandatory for all Git operations. For more details, reach out to HF Support to address any questions or concerns at [email protected]
|
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] |
Deprecation of Git Authentication using password
|
Sylvestre
|
August 25, 2023
|
password-git-deprecation
|
announcement, security
|
https://huggingface.co/blog/password-git-deprecation
|
Switching to personal access token You will need to generate an access token for your account; you can follow https://huggingface.co/docs/hub/security-tokens#user-access-tokens to generate one. After generating your access token, you can update your Git repository using the following commands: bash $: git remote set-url origin https://<user_name>:<token>@huggingface.co/<repo_path> $: git pull origin where <repo_path> is in the form of: - <user_name>/<repo_name> for models - datasets/<user_name>/<repo_name> for datasets - spaces/<user_name>/<repo_name> for Spaces If you clone a new repo, you can just input a token in place of your password when your Git credential manager asks you for your authentication credentials.
|
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] |
Code Llama: Llama 2 learns to code
|
philschmid
|
August 25, 2023
|
codellama
|
nlp, community, research, LLM
|
https://huggingface.co/blog/codellama
|
4-bit Loading Integration of Code Llama in Transformers means that you get immediate support for advanced features like 4-bit loading. This allows you to run the big 32B parameter models on consumer GPUs like nvidia 3090 cards! Here's how you can run inference in 4-bit mode: Python from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig import torch model_id = "codellama/CodeLlama-34b-hf" quantization_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_compute_dtype=torch.float16 ) tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, quantization_config=quantization_config, device_map="auto", ) prompt = 'def remove_non_ascii(s: str) -> str:\n """ ' inputs = tokenizer(prompt, return_tensors="pt").to("cuda") output = model.generate( inputs["input_ids"], max_new_tokens=200, do_sample=True, top_p=0.9, temperature=0.1, ) output = output[0].to("cpu") print(tokenizer.decode(output))
|
[
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] |
Code Llama: Llama 2 learns to code
|
philschmid
|
August 25, 2023
|
codellama
|
nlp, community, research, LLM
|
https://huggingface.co/blog/codellama
|
Using VS Code extension HF Code Autocomplete is a VS Code extension for testing open source code completion models. The extension was developed as part of StarCoder project and was updated to support the medium-sized base model, Code Llama 13B. Find more here on how to install and run the extension with Code Llama.
|
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] |
Code Llama: Llama 2 learns to code
|
philschmid
|
August 25, 2023
|
codellama
|
nlp, community, research, LLM
|
https://huggingface.co/blog/codellama
|
# Code Llama: Llama 2 learns to code
|
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] |
Code Llama: Llama 2 learns to code
|
philschmid
|
August 25, 2023
|
codellama
|
nlp, community, research, LLM
|
https://huggingface.co/blog/codellama
|
Transformers Starting with transformers 4.33, you can use Code Llama and leverage all the tools within the HF ecosystem, such as: - training and inference scripts and examples - safe file format (safetensors) - integrations with tools such as bitsandbytes (4-bit quantization) and PEFT (parameter efficient fine-tuning) - utilities and helpers to run generation with the model - mechanisms to export the models to deploy bash !pip install --upgrade transformers
|
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] |
Code Llama: Llama 2 learns to code
|
philschmid
|
August 25, 2023
|
codellama
|
nlp, community, research, LLM
|
https://huggingface.co/blog/codellama
|
Code Completion The 7B and 13B models can be used for text/code completion or infilling. The following code snippet uses the pipeline interface to demonstrate text completion. It runs on the free tier of Colab, as long as you select a GPU runtime. python from transformers import AutoTokenizer import transformers import torch tokenizer = AutoTokenizer.from_pretrained("codellama/CodeLlama-7b-hf") pipeline = transformers.pipeline( "text-generation", model="codellama/CodeLlama-7b-hf", torch_dtype=torch.float16, device_map="auto", ) sequences = pipeline( 'def fibonacci(', do_sample=True, temperature=0.2, top_p=0.9, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, max_length=100, ) for seq in sequences: print(f"Result: {seq['generated_text']}") This may produce output like the following: python Result: def fibonacci(n): if n == 0: return 0 elif n == 1: return 1 else: return fibonacci(n-1) + fibonacci(n-2) def fibonacci_memo(n, memo={}): if n == 0: return 0 elif n == 1: return Code Llama is specialized in code understanding, but it's a language model in its own right. You can use the same generation strategy to autocomplete comments or general text.
|
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] |
Code Llama: Llama 2 learns to code
|
philschmid
|
August 25, 2023
|
codellama
|
nlp, community, research, LLM
|
https://huggingface.co/blog/codellama
|
Additional Resources - Models on the Hub - Paper Page - Official Meta announcement - Responsible Use Guide - Demo (code completion, streaming server) - Demo (instruction fine-tuned, self-contained & clonable)
|
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] |
Code Llama: Llama 2 learns to code
|
philschmid
|
August 25, 2023
|
codellama
|
nlp, community, research, LLM
|
https://huggingface.co/blog/codellama
|
Using text-generation-inference and Inference Endpoints Text Generation Inference is a production-ready inference container developed by Hugging Face to enable easy deployment of large language models. It has features such as continuous batching, token streaming, tensor parallelism for fast inference on multiple GPUs, and production-ready logging and tracing. You can try out Text Generation Inference on your own infrastructure, or you can use Hugging Face's Inference Endpoints. To deploy a Codellama 2 model, go to the model page and click on the Deploy -> Inference Endpoints widget. - For 7B models, we advise you to select "GPU [medium] - 1x Nvidia A10G". - For 13B models, we advise you to select "GPU [xlarge] - 1x Nvidia A100". - For 34B models, we advise you to select "GPU [1xlarge] - 1x Nvidia A100" with bitsandbytes quantization enabled or "GPU [2xlarge] - 2x Nvidia A100" Note: You might need to request a quota upgrade via email to [email protected] to access A100s You can learn more on how to Deploy LLMs with Hugging Face Inference Endpoints in our blog. The blog includes information about supported hyperparameters and how to stream your response using Python and Javascript.
|
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] |
Code Llama: Llama 2 learns to code
|
philschmid
|
August 25, 2023
|
codellama
|
nlp, community, research, LLM
|
https://huggingface.co/blog/codellama
|
A Note on dtypes When using models like Code Llama, it's important to take a look at the data types of the models. * 32-bit floating point (float32): PyTorch convention on model initialization is to load models in float32, no matter with which precision the model weights were stored. transformers also follows this convention for consistency with PyTorch. * 16-bit Brain floating point (bfloat16): Code Llama was trained with this precision, so we recommend using it for further training or fine-tuning. * 16-bit floating point (float16): We recommend running inference using this precision, as it's usually faster than bfloat16, and evaluation metrics show no discernible degradation with respect to bfloat16. You can also run inference using bfloat16, and we recommend you check inference results with both float16 and bfloat16 after fine-tuning. As mentioned above, transformers loads weights using float32 (no matter with which precision the models are stored), so it's important to specify the desired dtype when loading the models. If you want to fine-tune Code Llama, it's recommended to use bfloat16, as using float16 can lead to overflows and NaNs. If you run inference, we recommend using float16 because bfloat16 can be slower.
|
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] |
Code Llama: Llama 2 learns to code
|
philschmid
|
August 25, 2023
|
codellama
|
nlp, community, research, LLM
|
https://huggingface.co/blog/codellama
|
Demo You can easily try the Code Llama Model (13 billion parameters!) in this Space or in the playground embedded below: Under the hood, this playground uses Hugging Face's Text Generation Inference, the same technology that powers HuggingChat, and we'll share more in the following sections. If you want to try out the bigger instruct-tuned 34B model, it is now available on HuggingChat! You can try it out here: hf.co/chat. Make sure to specify the Code Llama model. You can also check this chat-based demo and duplicate it for your use – it's self-contained, so you can examine the source code and adapt it as you wish!
|
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] |
Code Llama: Llama 2 learns to code
|
philschmid
|
August 25, 2023
|
codellama
|
nlp, community, research, LLM
|
https://huggingface.co/blog/codellama
|
Evaluation Language models for code are typically benchmarked on datasets such as HumanEval. It consists of programming challenges where the model is presented with a function signature and a docstring and is tasked to complete the function body. The proposed solution is then verified by running a set of predefined unit tests. Finally, a pass rate is reported which describes how many solutions passed all tests. The pass@1 rate describes how often the model generates a passing solution when having one shot whereas pass@10 describes how often at least one solution passes out of 10 proposed candidates. While HumanEval is a Python benchmark there have been significant efforts to translate it to more programming languages and thus enable a more holistic evaluation. One such approach is MultiPL-E which translates HumanEval to over a dozen languages. We are hosting a multilingual code leaderboard based on it to allow the community to compare models across different languages to evaluate which model fits their use-case best. | Model | License | Dataset known | Commercial use? | Pretraining length [tokens] | Python | JavaScript | Leaderboard Avg Score | | ---------------------- | ------------------ | ------------- | --------------- | --------------------------- | ------ | ---------- | --------------------- | | CodeLlaMa-34B | Llama 2 license | ❌ | ✅ | 2,500B | 45.11 | 41.66 | 33.89 | | CodeLlaMa-13B | Llama 2 license | ❌ | ✅ | 2,500B | 35.07 | 38.26 | 28.35 | | CodeLlaMa-7B | Llama 2 license | ❌ | ✅ | 2,500B | 29.98 | 31.8 | 24.36 | | CodeLlaMa-34B-Python | Llama 2 license | ❌ | ✅ | 2,620B | 53.29 | 44.72 | 33.87 | | CodeLlaMa-13B-Python | Llama 2 license | ❌ | ✅ | 2,620B | 42.89 | 40.66 | 28.67 | | CodeLlaMa-7B-Python | Llama 2 license | ❌ | ✅ | 2,620B | 40.48 | 36.34 | 23.5 | | CodeLlaMa-34B-Instruct | Llama 2 license | ❌ | ✅ | 2,620B | 50.79 | 45.85 | 35.09 | | CodeLlaMa-13B-Instruct | Llama 2 license | ❌ | ✅ | 2,620B | 50.6 | 40.91 | 31.29 | | CodeLlaMa-7B-Instruct | Llama 2 license | ❌ | ✅ | 2,620B | 45.65 | 33.11 | 26.45 | | StarCoder-15B | BigCode-OpenRail-M | ✅ | ✅ | 1,035B | 33.57 | 30.79 | 22.74 | | StarCoderBase-15B | BigCode-OpenRail-M | ✅ | ✅ | 1,000B | 30.35 | 31.7 | 22.4 | | WizardCoder-15B | BigCode-OpenRail-M | ❌ | ✅ | 1,035B | 58.12 | 41.91 | 32.07 | | OctoCoder-15B | BigCode-OpenRail-M | ✅ | ✅ | 1,000B | 45.3 | 32.8 | 24.01 | | CodeGeeX-2-6B | CodeGeeX License | ❌ | ❌ | 2,000B | 33.49 | 29.9 | 21.23 | | CodeGen-2.5-7B-Mono | Apache-2.0 | ✅ | ✅ | 1400B | 45.65 | 23.22 | 12.1 | | CodeGen-2.5-7B-Multi | Apache-2.0 | ✅ | ✅ | 1400B | 28.7 | 26.27 | 20.04 | Note: The scores presented in the table above are sourced from our code leaderboard, where we evaluate all models with the same settings. For more details, please refer to the leaderboard.
|
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] |
Code Llama: Llama 2 learns to code
|
philschmid
|
August 25, 2023
|
codellama
|
nlp, community, research, LLM
|
https://huggingface.co/blog/codellama
|
Conversational Instructions The base model can be used for both completion and infilling, as described. The Code Llama release also includes an instruction fine-tuned model that can be used in conversational interfaces. To prepare inputs for this task we have to use a prompt template like the one described in our Llama 2 blog post, which we reproduce again here: <s>[INST] <<SYS>> {{ system_prompt }} <</SYS>> {{ user_msg_1 }} [/INST] {{ model_answer_1 }} </s><s>[INST] {{ user_msg_2 }} [/INST] Note that the system prompt is optional - the model will work without it, but you can use it to further configure its behavior or style. For example, if you'd always like to get answers in JavaScript, you could state that here. After the system prompt, you need to provide all the previous interactions in the conversation: what was asked by the user and what was answered by the model. As in the infilling case, you need to pay attention to the delimiters used. The final component of the input must always be a new user instruction, which will be the signal for the model to provide an answer. The following code snippets demonstrate how the template works in practice. 1. First user query, no system prompt python user = 'In Bash, how do I list all text files in the current directory (excluding subdirectories) that have been modified in the last month?' prompt = f"<s>[INST] {user.strip()} [/INST]" inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda") 2. First user query with system prompt python system = "Provide answers in JavaScript" user = "Write a function that computes the set of sums of all contiguous sublists of a given list." prompt = f"<s>[INST] <<SYS>>\\n{system}\\n<</SYS>>\\n\\n{user}[/INST]" inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda") 3. On-going conversation with previous answers The process is the same as in Llama 2. We haven’t used loops or generalized this example code for maximum clarity: python system = "System prompt" user_1 = "user_prompt_1" answer_1 = "answer_1" user_2 = "user_prompt_2" answer_2 = "answer_2" user_3 = "user_prompt_3" prompt = f"<<SYS>>\\n{system}\\n<</SYS>>\\n\\n{user_1}" prompt += f"<s>[INST] {prompt.strip()} [/INST] {answer_1.strip()} </s>" prompt += f"<s>[INST] {user_2.strip()} [/INST] {answer_2.strip()} </s>" prompt += f"<s>[INST] {user_3.strip()} [/INST]" inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
|
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] |
Code Llama: Llama 2 learns to code
|
philschmid
|
August 25, 2023
|
codellama
|
nlp, community, research, LLM
|
https://huggingface.co/blog/codellama
|
Code Infilling This is a specialized task particular to code models. The model is trained to generate the code (including comments) that best matches an existing prefix and suffix. This is the strategy typically used by code assistants: they are asked to fill the current cursor position, considering the contents that appear before and after it. This task is available in the base and instruction variants of the 7B and 13B models. It is not available for any of the 34B models or the Python versions. To use this feature successfully, you need to pay close attention to the format used to train the model for this task, as it uses special separators to identify the different parts of the prompt. Fortunately, transformers' CodeLlamaTokenizer makes this very easy, as demonstrated below: python from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model_id = "codellama/CodeLlama-7b-hf" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16 ).to("cuda") prompt = '''def remove_non_ascii(s: str) -> str: """ <FILL_ME> return result ''' input_ids = tokenizer(prompt, return_tensors="pt")["input_ids"].to("cuda") output = model.generate( input_ids, max_new_tokens=200, ) output = output[0].to("cpu") filling = tokenizer.decode(output[input_ids.shape[1]:], skip_special_tokens=True) print(prompt.replace("<FILL_ME>", filling)) Python def remove_non_ascii(s: str) -> str: """ Remove non-ASCII characters from a string. Args: s: The string to remove non-ASCII characters from. Returns: The string with non-ASCII characters removed. """ result = "" for c in s: if ord(c) < 128: result += c return result Under the hood, the tokenizer automatically splits by <FILL_ME> to create a formatted input string that follows the original training pattern. This is more robust than preparing the pattern yourself: it avoids pitfalls, such as token glueing, that are very hard to debug.
|
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] |
Code Llama: Llama 2 learns to code
|
philschmid
|
August 25, 2023
|
codellama
|
nlp, community, research, LLM
|
https://huggingface.co/blog/codellama
|
What’s Code Llama? The Code Llama release introduces a family of models of 7, 13, and 34 billion parameters. The base models are initialized from Llama 2 and then trained on 500 billion tokens of code data. Meta fine-tuned those base models for two different flavors: a Python specialist (100 billion additional tokens) and an instruction fine-tuned version, which can understand natural language instructions. The models show state-of-the-art performance in Python, C++, Java, PHP, C#, TypeScript, and Bash. The 7B and 13B base and instruct variants support infilling based on surrounding content, making them ideal for use as code assistants. Code Llama was trained on a 16k context window. In addition, the three model variants had additional long-context fine-tuning, allowing them to manage a context window of up to 100,000 tokens. Increasing Llama 2’s 4k context window to Code Llama’s 16k (that can extrapolate up to 100k) was possible due to recent developments in RoPE scaling. The community found that Llama’s position embeddings can be interpolated linearly or in the frequency domain, which eases the transition to a larger context window through fine-tuning. In the case of Code Llama, the frequency domain scaling is done with a slack: the fine-tuning length is a fraction of the scaled pretrained length, giving the model powerful extrapolation capabilities. All models were initially trained with 500 billion tokens on a near-deduplicated dataset of publicly available code. The dataset also contains some natural language datasets, such as discussions about code and code snippets. Unfortunately, there is not more information about the dataset. For the instruction model, they used two datasets: the instruction tuning dataset collected for Llama 2 Chat and a self-instruct dataset. The self-instruct dataset was created by using Llama 2 to create interview programming questions and then using Code Llama to generate unit tests and solutions, which are later evaluated by executing the tests.
|
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] |
Code Llama: Llama 2 learns to code
|
philschmid
|
August 25, 2023
|
codellama
|
nlp, community, research, LLM
|
https://huggingface.co/blog/codellama
|
Table of Contents - Introduction - Table of Contents - What’s Code Llama? - How to use Code Llama? - Demo - Transformers - A Note on dtypes - Code Completion - Code Infilling - Conversational Instructions - 4-bit Loading - Using text-generation-inference and Inference Endpoints - Using VS Code extension - Evaluation - Additional Resources
|
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] |
Code Llama: Llama 2 learns to code
|
philschmid
|
August 25, 2023
|
codellama
|
nlp, community, research, LLM
|
https://huggingface.co/blog/codellama
|
How to use Code Llama? Code Llama is available in the Hugging Face ecosystem, starting with transformers version 4.33.
|
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] |
Code Llama: Llama 2 learns to code
|
philschmid
|
August 25, 2023
|
codellama
|
nlp, community, research, LLM
|
https://huggingface.co/blog/codellama
|
Introduction Code Llama is a family of state-of-the-art, open-access versions of Llama 2 specialized on code tasks, and we’re excited to release integration in the Hugging Face ecosystem! Code Llama has been released with the same permissive community license as Llama 2 and is available for commercial use. Today, we’re excited to release: - Models on the Hub with their model cards and license - Transformers integration - Integration with Text Generation Inference for fast and efficient production-ready inference - Integration with Inference Endpoints - Integration with VS Code extension - Code benchmarks Code LLMs are an exciting development for software engineers because they can boost productivity through code completion in IDEs, take care of repetitive or annoying tasks like writing docstrings, or create unit tests.
|
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] |
AudioLDM 2, but faster ⚡️
|
sanchit-gandhi
|
Aug 30, 2023
|
audioldm2
|
guide, audio, diffusers, diffusion
|
https://huggingface.co/blog/audioldm2
|
Conclusion In this blog post, we showcased four optimisation methods that are available out of the box with 🧨 Diffusers, taking the generation time of AudioLDM 2 from 14 seconds down to less than 1 second. We also highlighted how to employ memory saving tricks, such as half-precision and CPU offload, to reduce peak memory usage for long audio samples or large checkpoint sizes. Blog post by Sanchit Gandhi. Many thanks to Vaibhav Srivastav and Sayak Paul for their constructive comments. Spectrogram image source: Getting to Know the Mel Spectrogram. Waveform image source: Aalto Speech Processing.
|
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] |
AudioLDM 2, but faster ⚡️
|
sanchit-gandhi
|
Aug 30, 2023
|
audioldm2
|
guide, audio, diffusers, diffusion
|
https://huggingface.co/blog/audioldm2
|
Optimisation 1: Flash Attention PyTorch 2.0 and upwards includes an optimised and memory-efficient implementation of the attention operation through the torch.nn.functional.scaled_dot_product_attention (SDPA) function. This function automatically applies several in-built optimisations depending on the inputs, and runs faster and more memory-efficient than the vanilla attention implementation. Overall, the SDPA function gives similar behaviour to flash attention, as proposed in the paper Fast and Memory-Efficient Exact Attention with IO-Awareness by Dao et. al. These optimisations will be enabled by default in Diffusers if PyTorch 2.0 is installed and if torch.nn.functional.scaled_dot_product_attention is available. To use it, just install torch 2.0 or higher as per the official instructions, and then use the pipeline as is 🚀 python audio = pipe(prompt, negative_prompt=negative_prompt, generator=generator.manual_seed(0)).audios[0] Output: 100%|███████████████████████████████████████████| 200/200 [00:12<00:00, 16.60it/s] For more details on the use of SDPA in diffusers, refer to the corresponding documentation.
|
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] |
AudioLDM 2, but faster ⚡️
|
sanchit-gandhi
|
Aug 30, 2023
|
audioldm2
|
guide, audio, diffusers, diffusion
|
https://huggingface.co/blog/audioldm2
|
# AudioLDM 2, but faster ⚡️ AudioLDM 2 was proposed in AudioLDM 2: Learning Holistic Audio Generation with Self-supervised Pretraining by Haohe Liu et al. AudioLDM 2 takes a text prompt as input and predicts the corresponding audio. It can generate realistic sound effects, human speech and music. While the generated audios are of high quality, running inference with the original implementation is very slow: a 10 second audio sample takes upwards of 30 seconds to generate. This is due to a combination of factors, including a deep multi-stage modelling approach, large checkpoint sizes, and un-optimised code. In this blog post, we showcase how to use AudioLDM 2 in the Hugging Face 🧨 Diffusers library, exploring a range of code optimisations such as half-precision, flash attention, and compilation, and model optimisations such as scheduler choice and negative prompting, to reduce the inference time by over 10 times, with minimal degradation in quality of the output audio. The blog post is also accompanied by a more streamlined Colab notebook, that contains all the code but fewer explanations. Read to the end to find out how to generate a 10 second audio sample in just 1 second!
|
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] |
AudioLDM 2, but faster ⚡️
|
sanchit-gandhi
|
Aug 30, 2023
|
audioldm2
|
guide, audio, diffusers, diffusion
|
https://huggingface.co/blog/audioldm2
|
What about memory? The length of the audio sample we want to generate dictates the width of the latent variables we de-noise in the LDM. Since the memory of the cross-attention layers in the UNet scales with sequence length (width) squared, generating very long audio samples might lead to out-of-memory errors. Our batch size also governs our memory usage, controlling the number of samples that we generate. We've already mentioned that loading the model in float16 half precision gives strong memory savings. Using PyTorch 2.0 SDPA also gives a memory improvement, but this might not be suffienct for extremely large sequence lengths. Let's try generating an audio sample 2.5 minutes (150 seconds) in duration. We'll also generate 4 candidate audios by setting num_waveforms_per_prompt=4. Once num_waveforms_per_prompt>1, automatic scoring is performed between the generated audios and the text prompt: the audios and text prompts are embedded in the CLAP audio-text embedding space, and then ranked based on their cosine similarity scores. We can access the 'best' waveform as that in position 0. Since we've changed the width of the latent variables in the UNet, we'll have to perform another torch compilation step with the new latent variable shapes. In the interest of time, we'll re-load the pipeline without torch compile, such that we're not hit with a lengthy compilation step first up: python pipe = AudioLDM2Pipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe.to("cuda") audio = pipe(prompt, negative_prompt=negative_prompt, num_waveforms_per_prompt=4, audio_length_in_s=150, num_inference_steps=20, generator=generator.manual_seed(0)).audios[0] Output: --------------------------------------------------------------------------- OutOfMemoryError Traceback (most recent call last) <ipython-input-33-c4cae6410ff5> in <cell line: 5>() 3 pipe.to("cuda") 4 ----> 5 audio = pipe(prompt, negative_prompt=negative_prompt, num_waveforms_per_prompt=4, audio_length_in_s=150, num_inference_steps=20, generator=generator.manual_seed(0)).audios[0] 23 frames /usr/local/lib/python3.10/dist-packages/torch/nn/modules/linear.py in forward(self, input) 112 113 def forward(self, input: Tensor) -> Tensor: --> 114 return F.linear(input, self.weight, self.bias) 115 116 def extra_repr(self) -> str: OutOfMemoryError: CUDA out of memory. Tried to allocate 1.95 GiB. GPU 0 has a total capacty of 14.75 GiB of which 1.66 GiB is free. Process 414660 has 13.09 GiB memory in use. Of the allocated memory 10.09 GiB is allocated by PyTorch, and 1.92 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF Unless you have a GPU with high RAM, the code above probably returned an OOM error. While the AudioLDM 2 pipeline involves several components, only the model being used has to be on the GPU at any one time. The remainder of the modules can be offloaded to the CPU. This technique, called CPU offload, can reduce memory usage, with a very low penalty to inference time. We can enable CPU offload on our pipeline with the function enable_model_cpu_offload(): python pipe.enable_model_cpu_offload() Running generation with CPU offload is then the same as before: python audio = pipe(prompt, negative_prompt=negative_prompt, num_waveforms_per_prompt=4, audio_length_in_s=150, num_inference_steps=20, generator=generator.manual_seed(0)).audios[0] Output: 100%|███████████████████████████████████████████| 20/20 [00:36<00:00, 1.82s/it] And with that, we can generate four samples, each of 150 seconds in duration, all in one call to the pipeline! Using the large AudioLDM 2 checkpoint will result in higher overall memory usage than the base checkpoint, since the UNet is over twice the size (750M parameters compared to 350M), so this memory saving trick is particularly beneficial here.
|
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AudioLDM 2, but faster ⚡️
|
sanchit-gandhi
|
Aug 30, 2023
|
audioldm2
|
guide, audio, diffusers, diffusion
|
https://huggingface.co/blog/audioldm2
|
Model overview Inspired by Stable Diffusion, AudioLDM 2 is a text-to-audio latent diffusion model (LDM) that learns continuous audio representations from text embeddings. The overall generation process is summarised as follows: 1. Given a text input \(\boldsymbol{x}\), two text encoder models are used to compute the text embeddings: the text-branch of CLAP, and the text-encoder of Flan-T5 $$ \boldsymbol{E}{1} = \text{CLAP}\left(\boldsymbol{x} \right); \quad \boldsymbol{E}{2} = \text{T5}\left(\boldsymbol{x}\right) $$ The CLAP text embeddings are trained to be aligned with the embeddings of the corresponding audio sample, whereas the Flan-T5 embeddings give a better representation of the semantics of the text. 2. These text embeddings are projected to a shared embedding space through individual linear projections: $$ \boldsymbol{P}{1} = \boldsymbol{W}{\text{CLAP}} \boldsymbol{E}{1}; \quad \boldsymbol{P}{2} = \boldsymbol{W}{\text{T5}}\boldsymbol{E}{2} $$ In the diffusers implementation, these projections are defined by the AudioLDM2ProjectionModel. 3. A GPT2 language model (LM) is used to auto-regressively generate a sequence of \(N\) new embedding vectors, conditional on the projected CLAP and Flan-T5 embeddings: $$ \tilde{\boldsymbol{E}}{i} = \text{GPT2}\left(\boldsymbol{P}{1}, \boldsymbol{P}{2}, \tilde{\boldsymbol{E}}{1:i-1}\right) \qquad \text{for } i=1,\dots,N $$ 4. The generated embedding vectors \(\tilde{\boldsymbol{E}}{1:N}\) and Flan-T5 text embeddings \(\boldsymbol{E}{2}\) are used as cross-attention conditioning in the LDM, which de-noises a random latent via a reverse diffusion process. The LDM is run in the reverse diffusion process for a total of \(T\) inference steps: $$ \boldsymbol{z}{t} = \text{LDM}\left(\boldsymbol{z}{t-1} | \tilde{\boldsymbol{E}}{1:N}, \boldsymbol{E}{2}\right) \qquad \text{for } t = 1, \dots, T $$ where the initial latent variable \(\boldsymbol{z}{0}\) is drawn from a normal distribution \(\mathcal{N} \left(\boldsymbol{0}, \boldsymbol{I} \right)\). The UNet of the LDM is unique in the sense that it takes two sets of cross-attention embeddings, \(\tilde{\boldsymbol{E}}{1:N}\) from the GPT2 language model and \(\boldsymbol{E}{2}\) from Flan-T5, as opposed to one cross-attention conditioning as in most other LDMs. 5. The final de-noised latents \(\boldsymbol{z}{T}\) are passed to the VAE decoder to recover the Mel spectrogram \(\boldsymbol{s}\): $$ \boldsymbol{s} = \text{VAE}{\text{dec}} \left(\boldsymbol{z}{T}\right) $$ 6. The Mel spectrogram is passed to the vocoder to obtain the output audio waveform \(\mathbf{y}\): $$ \boldsymbol{y} = \text{Vocoder}\left(\boldsymbol{s}\right) $$ The diagram below demonstrates how a text input is passed through the text conditioning models, with the two prompt embeddings used as cross-conditioning in the LDM: For full details on how the AudioLDM 2 model is trained, the reader is referred to the AudioLDM 2 paper. Hugging Face 🧨 Diffusers provides an end-to-end inference pipeline class AudioLDM2Pipeline that wraps this multi-stage generation process into a single callable object, enabling you to generate audio samples from text in just a few lines of code. AudioLDM 2 comes in three variants. Two of these checkpoints are applicable to the general task of text-to-audio generation. The third checkpoint is trained exclusively on text-to-music generation. See the table below for details on the three official checkpoints, which can all be found on the Hugging Face Hub: | Checkpoint | Task | Model Size | Training Data / h | |-----------------------------------------------------------------------|---------------|------------|-------------------| | cvssp/audioldm2 | Text-to-audio | 1.1B | 1150k | | cvssp/audioldm2-music | Text-to-music | 1.1B | 665k | | cvssp/audioldm2-large | Text-to-audio | 1.5B | 1150k | Now that we've covered a high-level overview of how the AudioLDM 2 generation process works, let's put this theory into practice!
|
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] |
AudioLDM 2, but faster ⚡️
|
sanchit-gandhi
|
Aug 30, 2023
|
audioldm2
|
guide, audio, diffusers, diffusion
|
https://huggingface.co/blog/audioldm2
|
Optimisation 4: Scheduler Another option is to reduce the number of inference steps. Choosing a more efficient scheduler can help decrease the number of steps without sacrificing the output audio quality. You can find which schedulers are compatible with the AudioLDM2Pipeline by calling the schedulers.compatibles attribute: python pipe.scheduler.compatibles Output: [diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteScheduler, diffusers.schedulers.scheduling_k_dpm_2_discrete.KDPM2DiscreteScheduler, diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler, diffusers.schedulers.scheduling_unipc_multistep.UniPCMultistepScheduler, diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler, diffusers.schedulers.scheduling_pndm.PNDMScheduler, diffusers.schedulers.scheduling_dpmsolver_singlestep.DPMSolverSinglestepScheduler, diffusers.schedulers.scheduling_heun_discrete.HeunDiscreteScheduler, diffusers.schedulers.scheduling_ddpm.DDPMScheduler, diffusers.schedulers.scheduling_deis_multistep.DEISMultistepScheduler, diffusers.utils.dummy_torch_and_torchsde_objects.DPMSolverSDEScheduler, diffusers.schedulers.scheduling_ddim.DDIMScheduler, diffusers.schedulers.scheduling_k_dpm_2_ancestral_discrete.KDPM2AncestralDiscreteScheduler, diffusers.schedulers.scheduling_euler_ancestral_discrete.EulerAncestralDiscreteScheduler] Alright! We've got a long list of schedulers to choose from 📝. By default, AudioLDM 2 uses the DDIMScheduler, and requires 200 inference steps to get good quality audio generations. However, more performant schedulers, like DPMSolverMultistepScheduler, require only 20-25 inference steps to achieve similar results. Let's see how we can switch the AudioLDM 2 scheduler from DDIM to DPM Multistep. We'll use the ConfigMixin.from_config() method to load a DPMSolverMultistepScheduler from the configuration of our original DDIMScheduler: python from diffusers import DPMSolverMultistepScheduler pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config) Let's set the number of inference steps to 20 and re-run the generation with the new scheduler. Since the shape of the LDM latents are un-changed, we don't have to repeat the compilation step: python audio = pipe(prompt, negative_prompt=negative_prompt, num_inference_steps=20, generator=generator.manual_seed(0)).audios[0] Output: 100%|███████████████████████████████████████████| 20/20 [00:00<00:00, 49.14it/s] That took less than 1 second to generate the audio! Let's have a listen to the resulting generation: python Audio(audio, rate=16000) Your browser does not support the audio element. More or less the same as our original audio sample, but only a fraction of the generation time! 🧨 Diffusers pipelines are designed to be composable, allowing you two swap out schedulers and other components for more performant counterparts with ease.
|
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] |
AudioLDM 2, but faster ⚡️
|
sanchit-gandhi
|
Aug 30, 2023
|
audioldm2
|
guide, audio, diffusers, diffusion
|
https://huggingface.co/blog/audioldm2
|
Optimisation 3: Torch Compile To get an additional speed-up, we can use the new torch.compile feature. Since the UNet of the pipeline is usually the most computationally expensive, we wrap the unet with torch.compile, leaving the rest of the sub-models (text encoders and VAE) as is: python pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True) After wrapping the UNet with torch.compile the first inference step we run is typically going to be slow, due to the overhead of compiling the forward pass of the UNet. Let's run the pipeline forward with the compilation step get this longer run out of the way. Note that the first inference step might take up to 2 minutes to compile, so be patient! python audio = pipe(prompt, negative_prompt=negative_prompt, generator=generator.manual_seed(0)).audios[0] Output: 100%|███████████████████████████████████████████| 200/200 [01:23<00:00, 2.39it/s] Great! Now that the UNet is compiled, we can now run the full diffusion process and reap the benefits of faster inference: python audio = pipe(prompt, negative_prompt=negative_prompt, generator=generator.manual_seed(0)).audios[0] Output: 100%|███████████████████████████████████████████| 200/200 [00:04<00:00, 48.98it/s] Only 4 seconds to generate! In practice, you will only have to compile the UNet once, and then get faster inference for all successive generations. This means that the time taken to compile the model is amortised by the gains in subsequent inference time. For more information and options regarding torch.compile, refer to the torch compile docs.
|
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] |
AudioLDM 2, but faster ⚡️
|
sanchit-gandhi
|
Aug 30, 2023
|
audioldm2
|
guide, audio, diffusers, diffusion
|
https://huggingface.co/blog/audioldm2
|
Load the pipeline For the purposes of this tutorial, we'll initialise the pipeline with the pre-trained weights from the base checkpoint, cvssp/audioldm2. We can load the entirety of the pipeline using the .from_pretrained method, which will instantiate the pipeline and load the pre-trained weights: python from diffusers import AudioLDM2Pipeline model_id = "cvssp/audioldm2" pipe = AudioLDM2Pipeline.from_pretrained(model_id) Output: Loading pipeline components...: 100%|███████████████████████████████████████████| 11/11 [00:01<00:00, 7.62it/s] The pipeline can be moved to the GPU in much the same way as a standard PyTorch nn module: python pipe.to("cuda"); Great! We'll define a Generator and set a seed for reproducibility. This will allow us to tweak our prompts and observe the effect that they have on the generations by fixing the starting latents in the LDM model: python import torch generator = torch.Generator("cuda").manual_seed(0) Now we're ready to perform our first generation! We'll use the same running example throughout this notebook, where we'll condition the audio generations on a fixed text prompt and use the same seed throughout. The audio_length_in_s argument controls the length of the generated audio. It defaults to the audio length that the LDM was trained on (10.24 seconds): python prompt = "The sound of Brazilian samba drums with waves gently crashing in the background" audio = pipe(prompt, audio_length_in_s=10.24, generator=generator).audios[0] Output: 100%|███████████████████████████████████████████| 200/200 [00:13<00:00, 15.27it/s] Cool! That run took about 13 seconds to generate. Let's have a listen to the output audio: python from IPython.display import Audio Audio(audio, rate=16000) Your browser does not support the audio element. Sounds much like our text prompt! The quality is good, but still has artefacts of background noise. We can provide the pipeline with a negative prompt to discourage the pipeline from generating certain features. In this case, we'll pass a negative prompt that discourages the model from generating low quality audio in the outputs. We'll omit the audio_length_in_s argument and leave it to take its default value: python negative_prompt = "Low quality, average quality." audio = pipe(prompt, negative_prompt=negative_prompt, generator=generator.manual_seed(0)).audios[0] Output: 100%|███████████████████████████████████████████| 200/200 [00:12<00:00, 16.50it/s] The inference time is un-changed when using a negative prompt \({}^1\); we simply replace the unconditional input to the LDM with the negative input. That means any gains we get in audio quality we get for free. Let's take a listen to the resulting audio: python Audio(audio, rate=16000) Your browser does not support the audio element. There's definitely an improvement in the overall audio quality - there are less noise artefacts and the audio generally sounds sharper. \({}^1\) Note that in practice, we typically see a reduction in inference time going from our first generation to our second. This is due to a CUDA "warm-up" that occurs the first time we run the computation. The second generation is a better benchmark for our actual inference time.
|
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] |
AudioLDM 2, but faster ⚡️
|
sanchit-gandhi
|
Aug 30, 2023
|
audioldm2
|
guide, audio, diffusers, diffusion
|
https://huggingface.co/blog/audioldm2
|
Optimisation 2: Half-Precision By default, the AudioLDM2Pipeline loads the model weights in float32 (full) precision. All the model computations are also performed in float32 precision. For inference, we can safely convert the model weights and computations to float16 (half) precision, which will give us an improvement to inference time and GPU memory, with an impercivable change to generation quality. We can load the weights in float16 precision by passing the torch_dtype argument to .from_pretrained: python pipe = AudioLDM2Pipeline.from_pretrained(model_id, torch_dtype=torch.float16) pipe.to("cuda"); Let's run generation in float16 precision and listen to the audio outputs: python audio = pipe(prompt, negative_prompt=negative_prompt, generator=generator.manual_seed(0)).audios[0] Audio(audio, rate=16000) Output: 100%|███████████████████████████████████████████| 200/200 [00:09<00:00, 20.94it/s] Your browser does not support the audio element. The audio quality is largely un-changed from the full precision generation, with an inference speed-up of about 2 seconds. In our experience, we've not seen any significant audio degradation using diffusers pipelines with float16 precision, but consistently reap a substantial inference speed-up. Thus, we recommend using float16 precision by default.
|
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] |
Fetch Cuts ML Processing Latency by 50% Using Amazon SageMaker & Hugging Face
|
Violette
|
September 1, 2023
|
fetch-case-study
|
case-studies
|
https://huggingface.co/blog/fetch-case-study
|
# Fetch Cuts ML Processing Latency by 50% Using Amazon SageMaker & Hugging Face This article is a cross-post from an originally published post on September 2023 on AWS's website.
|
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] |
Fetch Cuts ML Processing Latency by 50% Using Amazon SageMaker & Hugging Face
|
Violette
|
September 1, 2023
|
fetch-case-study
|
case-studies
|
https://huggingface.co/blog/fetch-case-study
|
Outcome | Expanding ML to New Use Cases The ML team at Fetch is continually working on new models and iterating on existing ones to tune them for better performance. “Another thing we like is being able to keep our technology stack up to date with new features of Amazon SageMaker,” says Chris Lee, ML developer at Fetch. The company will continue expanding its use of AWS to different ML use cases, such as fraud prevention, across multiple teams. Already one of the biggest consumer engagement software companies, Fetch aims to continue growing. “AWS is a key part of how we plan to scale, and we’ll lean into the features of Amazon SageMaker to continue improving our accuracy,” says Corzine.
|
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] |
Fetch Cuts ML Processing Latency by 50% Using Amazon SageMaker & Hugging Face
|
Violette
|
September 1, 2023
|
fetch-case-study
|
case-studies
|
https://huggingface.co/blog/fetch-case-study
|
Opportunity | Using Amazon SageMaker to Accelerate an ML Pipeline in 12 Months for Fetch Using the Fetch app, customers can scan receipts, receive points, and redeem those points for gift cards. To reward users for receipt scans instantaneously, Fetch needed to be able to capture text from a receipt, extract the pertinent data, and structure it so that the rest of its system can process and analyze it. With over 80 million receipts processed per week—hundreds of receipts per second at peak traffic—it needed to perform this process quickly, accurately, and at scale. In 2021, Fetch set out to optimize its app’s scanning functionality. Fetch is an AWS-native company, and its ML operations team was already using Amazon SageMaker for many of its models. This made the decision to enhance its ML pipeline by migrating its models to Amazon SageMaker a straightforward one. Throughout the project, Fetch had weekly calls with the AWS team and received support from a subject matter expert whom AWS paired with Fetch. The company built, trained, and deployed more than five ML models using Amazon SageMaker in 12 months. In late 2022, Fetch rolled out its updated mobile app and new ML pipeline.
|
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] |
Fetch Cuts ML Processing Latency by 50% Using Amazon SageMaker & Hugging Face
|
Violette
|
September 1, 2023
|
fetch-case-study
|
case-studies
|
https://huggingface.co/blog/fetch-case-study
|
About Fetch Fetch is a consumer engagement company that provides insights on consumer purchases to brand partners. It also offers a mobile rewards app that lets users earn rewards on purchases through a receipt-scanning feature. If you need support in using Hugging Face on SageMaker for your company, please contact us here - our team will contact you to discuss your requirements!
|
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] |
Fetch Cuts ML Processing Latency by 50% Using Amazon SageMaker & Hugging Face
|
Violette
|
September 1, 2023
|
fetch-case-study
|
case-studies
|
https://huggingface.co/blog/fetch-case-study
|
Overview Consumer engagement and rewards company Fetch offers an application that lets users earn rewards on their purchases by scanning their receipts. The company also parses these receipts to generate insights into consumer behavior and provides those insights to brand partners. As weekly scans rapidly grew, Fetch needed to improve its speed and precision. On Amazon Web Services (AWS), Fetch optimized its machine learning (ML) pipeline using Hugging Face and Amazon SageMaker , a service for building, training, and deploying ML models with fully managed infrastructure, tools, and workflows. Now, the Fetch app can process scans faster and with significantly higher accuracy.
|
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] |
Fetch Cuts ML Processing Latency by 50% Using Amazon SageMaker & Hugging Face
|
Violette
|
September 1, 2023
|
fetch-case-study
|
case-studies
|
https://huggingface.co/blog/fetch-case-study
|
Solution | Cutting Latency by 50% Using ML & Hugging Face on Amazon SageMaker GPU Instances
|
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] |
Fetch Cuts ML Processing Latency by 50% Using Amazon SageMaker & Hugging Face
|
Violette
|
September 1, 2023
|
fetch-case-study
|
case-studies
|
https://huggingface.co/blog/fetch-case-study
|
"Amazon SageMaker is a game changer for Fetch. We use almost every feature extensively. As new features come out, they are immediately valuable. It’s hard to imagine having done this project without the features of Amazon SageMaker.” Sam Corzine, Machine Learning Engineer, Fetch
|
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] |
Fetch Cuts ML Processing Latency by 50% Using Amazon SageMaker & Hugging Face
|
Violette
|
September 1, 2023
|
fetch-case-study
|
case-studies
|
https://huggingface.co/blog/fetch-case-study
|
"Using the flexibility of the Hugging Face AWS Deep Learning Container, we could improve the quality of our models,and Hugging Face’s partnership with AWS meant that it was simple to deploy these models.” Sam Corzine, Machine Learning Engineer, Fetch Fetch’s ML pipeline is powered by several Amazon SageMaker features, particularly Amazon SageMaker Model Training, which reduces the time and cost to train and tune ML models at scale, and Amazon SageMaker Processing, a simplified, managed experience to run data-processing workloads. The company runs its custom ML models using multi-GPU instances for fast performance. “The GPU instances on Amazon SageMaker are simple to use,” says Ellen Light, backend engineer at Fetch. Fetch trains these models to identify and extract key information on receipts that the company can use to generate valuable insights and reward users. And on Amazon SageMaker, Fetch’s custom ML system is seamlessly scalable. “By using Amazon SageMaker, we have a simple way to scale up our systems, especially for inference and runtime,” says Sam Corzine, ML engineer at Fetch. Meanwhile, standardized model deployments mean less manual work. Fetch heavily relied on the ML training features of Amazon SageMaker, particularly its training jobs, as it refined and iterated on its models. Fetch can also train ML models in parallel, which speeds up development and deployments. “There’s little friction for us to deploy models,” says Alec Stashevsky, applied scientist at Fetch. “Basically, we don’t have to think about it.” This has increased confidence and improved productivity for the entire company. In one example, a new intern was able to deploy a model himself by his third day on the job. Since adopting Amazon SageMaker for ML tuning, training, and retraining, Fetch has enhanced the accuracy of its document-understanding model by 200 percent. It continues to fine-tune its models for further improvement. “Amazon SageMaker has been a key tool in building these outstanding models,” says Quency Yu, ML engineer at Fetch. To optimize the tuning process, Fetch relies on Amazon SageMaker Inference Recommender, a capability of Amazon SageMaker that reduces the time required to get ML models in production by automating load testing and model tuning. In addition to its custom ML models, Fetch uses AWS Deep Learning Containers (AWS DL Containers), which businesses can use to quickly deploy deep learning environments with optimized, prepackaged container images. This simplifies the process of using libraries from Hugging Face Inc.(Hugging Face), an artificial intelligence technology company and AWS Partner. Specifically, Fetch uses the Amazon SageMaker Hugging Face Inference Toolkit, an open-source library for serving transformers models, and the Hugging Face AWS Deep Learning Container for training and inference. “Using the flexibility of the Hugging Face AWS Deep Learning Container, we could improve the quality of our models,” says Corzine. “And Hugging Face’s partnership with AWS meant that it was simple to deploy these models.” For every metric that Fetch measures, performance has improved since adopting Amazon SageMaker. The company has reduced latency for its slowest scans by 50 percent. “Our improved accuracy also creates confidence in our data among partners,” says Corzine. With more confidence, partners will increase their use of Fetch’s solution. “Being able to meaningfully improve accuracy on literally every data point using Amazon SageMaker is a huge benefit and propagates throughout our entire business,” says Corzine. Fetch can now extract more types of data from a receipt, and it has the flexibility to structure resulting insights according to the specific needs of brand partners. “Leaning into ML has unlocked the ability to extract exactly what our partners want from a receipt,” says Corzine. “Partners can make new types of offers because of our investment in ML, and that’s a huge additional benefit for them.” Users enjoy the updates too; Fetch has grown from 10 million to 18 million monthly active users since it released the new version. “Amazon SageMaker is a game changer for Fetch,” says Corzine. “We use almost every feature extensively. As new features come out, they are immediately valuable. It’s hard to imagine having done this project without the features of Amazon SageMaker.” For example, Fetch migrated from a custom shadow testing pipeline to Amazon SageMaker shadow testing—which validates the performance of new ML models against production models to prevent outages. Now, shadow testing is more direct because Fetch can directly compare performance with production traffic.
|
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] |
Spread Your Wings: Falcon 180B is here
|
philschmid
|
September 6, 2023
|
falcon-180b
|
nlp, community, research, LLM
|
https://huggingface.co/blog/falcon-180b
|
Hardware requirements We ran several tests on the hardware needed to run the model for different use cases. Those are not the minimum numbers, but the minimum numbers for the configurations we had access to. | | Type | Kind | Memory | Example | | ----------- | --------- | ---------------- | ------------------- | --------------- | | Falcon 180B | Training | Full fine-tuning | 5120GB | 8x 8x A100 80GB | | Falcon 180B | Training | LoRA with ZeRO-3 | 1280GB | 2x 8x A100 80GB | | Falcon 180B | Training | QLoRA | 160GB | 2x A100 80GB | | Falcon 180B | Inference | BF16/FP16 | 640GB | 8x A100 80GB | | Falcon 180B | Inference | GPTQ/int4 | 320GB | 8x A100 40GB |
|
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] |
Spread Your Wings: Falcon 180B is here
|
philschmid
|
September 6, 2023
|
falcon-180b
|
nlp, community, research, LLM
|
https://huggingface.co/blog/falcon-180b
|
8-bit and 4-bit with bitsandbytes The 8-bit and 4-bit quantized versions of Falcon 180B show almost no difference in evaluation with respect to the bfloat16 reference! This is very good news for inference, as you can confidently use a quantized version to reduce hardware requirements. Keep in mind, though, that 8-bit inference is much faster than running the model in 4-bit. To use quantization, you need to install the bitsandbytes library and simply enable the corresponding flag when loading the model: python model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, load_in_8bit=True, device_map="auto", )
|
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] |
Spread Your Wings: Falcon 180B is here
|
philschmid
|
September 6, 2023
|
falcon-180b
|
nlp, community, research, LLM
|
https://huggingface.co/blog/falcon-180b
|
bfloat16 This is how you’d use the base model in bfloat16. Falcon 180B is a big model, so please take into account the hardware requirements summarized in the table above. python from transformers import AutoTokenizer, AutoModelForCausalLM import transformers import torch model_id = "tiiuae/falcon-180B" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto", ) prompt = "My name is Pedro, I live in" inputs = tokenizer(prompt, return_tensors="pt").to("cuda") output = model.generate( input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], do_sample=True, temperature=0.6, top_p=0.9, max_new_tokens=50, ) output = output[0].to("cpu") print(tokenizer.decode(output) This could produce an output such as: My name is Pedro, I live in Portugal and I am 25 years old. I am a graphic designer, but I am also passionate about photography and video. I love to travel and I am always looking for new adventures. I love to meet new people and explore new places.
|
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] |
Spread Your Wings: Falcon 180B is here
|
philschmid
|
September 6, 2023
|
falcon-180b
|
nlp, community, research, LLM
|
https://huggingface.co/blog/falcon-180b
|
Chat Model As mentioned above, the version of the model fine-tuned to follow conversations used a very straightforward training template. We have to follow the same pattern in order to run chat-style inference. For reference, you can take a look at the format_prompt function in the Chat demo, which looks like this: python def format_prompt(message, history, system_prompt): prompt = "" if system_prompt: prompt += f"System: {system_prompt}\n" for user_prompt, bot_response in history: prompt += f"User: {user_prompt}\n" prompt += f"Falcon: {bot_response}\n" prompt += f"User: {message}\nFalcon:" return prompt As you can see, interactions from the user and responses by the model are preceded by User: and Falcon: separators. We concatenate them together to form a prompt containing the conversation's whole history. We can provide a system prompt to tweak the generation style.
|
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] |
Spread Your Wings: Falcon 180B is here
|
philschmid
|
September 6, 2023
|
falcon-180b
|
nlp, community, research, LLM
|
https://huggingface.co/blog/falcon-180b
|
How to use Falcon 180B? Falcon 180B is available in the Hugging Face ecosystem, starting with Transformers version 4.33.
|
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] |
Spread Your Wings: Falcon 180B is here
|
philschmid
|
September 6, 2023
|
falcon-180b
|
nlp, community, research, LLM
|
https://huggingface.co/blog/falcon-180b
|
Introduction Today, we're excited to welcome TII's Falcon 180B to HuggingFace! Falcon 180B sets a new state-of-the-art for open models. It is the largest openly available language model, with 180 billion parameters, and was trained on a massive 3.5 trillion tokens using TII's RefinedWeb dataset. This represents the longest single-epoch pretraining for an open model. You can find the model on the Hugging Face Hub (base and chat model) and interact with the model on the Falcon Chat Demo Space. In terms of capabilities, Falcon 180B achieves state-of-the-art results across natural language tasks. It tops the leaderboard for (pre-trained) open-access models and rivals proprietary models like PaLM-2. While difficult to rank definitively yet, it is considered on par with PaLM-2 Large, making Falcon 180B one of the most capable LLMs publicly known. In this blog post, we explore what makes Falcon 180B so good by looking at some evaluation results and show how you can use the model. * What is Falcon-180B? * How good is Falcon 180B? * How to use Falcon 180B? * Demo * Hardware requirements * Prompt format * Transformers * Additional Resources
|
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] |
Spread Your Wings: Falcon 180B is here
|
philschmid
|
September 6, 2023
|
falcon-180b
|
nlp, community, research, LLM
|
https://huggingface.co/blog/falcon-180b
|
Prompt format The base model has no prompt format. Remember that it’s not a conversational model or trained with instructions, so don’t expect it to generate conversational responses—the pretrained model is a great platform for further finetuning, but you probably shouldn’t driectly use it out of the box. The Chat model has a very simple conversation structure. bash System: Add an optional system prompt here User: This is the user input Falcon: This is what the model generates User: This might be a second turn input Falcon: and so on
|
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] |
Spread Your Wings: Falcon 180B is here
|
philschmid
|
September 6, 2023
|
falcon-180b
|
nlp, community, research, LLM
|
https://huggingface.co/blog/falcon-180b
|
What is Falcon-180B? Falcon 180B is a model released by TII that follows previous releases in the Falcon family. Architecture-wise, Falcon 180B is a scaled-up version of Falcon 40B and builds on its innovations such as multiquery attention for improved scalability. We recommend reviewing the initial blog post introducing Falcon to dive into the architecture. Falcon 180B was trained on 3.5 trillion tokens on up to 4096 GPUs simultaneously, using Amazon SageMaker for a total of ~7,000,000 GPU hours. This means Falcon 180B is 2.5 times larger than Llama 2 and was trained with 4x more compute. The dataset for Falcon 180B consists predominantly of web data from RefinedWeb (\~85%). In addition, it has been trained on a mix of curated data such as conversations, technical papers, and a small fraction of code (\~3%). This pretraining dataset is big enough that even 3.5 trillion tokens constitute less than an epoch. The released chat model is fine-tuned on chat and instruction datasets with a mix of several large-scale conversational datasets. ‼️ Commercial use: Falcon 180b can be commercially used but under very restrictive conditions, excluding any "hosting use". We recommend to check the license and consult your legal team if you are interested in using it for commercial purposes.
|
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] |
Spread Your Wings: Falcon 180B is here
|
philschmid
|
September 6, 2023
|
falcon-180b
|
nlp, community, research, LLM
|
https://huggingface.co/blog/falcon-180b
|
Acknowledgments Releasing such a model with support and evaluations in the ecosystem would not be possible without the contributions of many community members, including Clémentine and Eleuther Evaluation Harness for LLM evaluations; Loubna and BigCode for code evaluations; Nicolas for Inference support; Lysandre, Matt, Daniel, Amy, Joao, and Arthur for integrating Falcon into transformers. Thanks to Baptiste and Patrick for the open-source demo. Thanks to Thom, Lewis, TheBloke, Nouamane, Tim Dettmers for multiple contributions enabling this to get out. Finally, thanks to the HF Cluster for enabling running LLM evaluations as well as providing inference for a free, open-source demo of the model.
|
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] |
Spread Your Wings: Falcon 180B is here
|
philschmid
|
September 6, 2023
|
falcon-180b
|
nlp, community, research, LLM
|
https://huggingface.co/blog/falcon-180b
|
Demo You can easily try the Big Falcon Model (180 billion parameters!) in this Space or in the playground embedded below:
|
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] |
Spread Your Wings: Falcon 180B is here
|
philschmid
|
September 6, 2023
|
falcon-180b
|
nlp, community, research, LLM
|
https://huggingface.co/blog/falcon-180b
|
Additional Resources - Models - Demo - The Falcon has landed in the Hugging Face ecosystem - Official Announcement
|
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] |
Spread Your Wings: Falcon 180B is here
|
philschmid
|
September 6, 2023
|
falcon-180b
|
nlp, community, research, LLM
|
https://huggingface.co/blog/falcon-180b
|
How good is Falcon 180B? Falcon 180B is the best openly released LLM today, outperforming Llama 2 70B and OpenAI’s GPT-3.5 on MMLU, and is on par with Google's PaLM 2-Large on HellaSwag, LAMBADA, WebQuestions, Winogrande, PIQA, ARC, BoolQ, CB, COPA, RTE, WiC, WSC, ReCoRD. Falcon 180B typically sits somewhere between GPT 3.5 and GPT4 depending on the evaluation benchmark and further finetuning from the community will be very interesting to follow now that it's openly released. With 68.74 on the Hugging Face Leaderboard, Falcon 180B is the highest-scoring openly released pre-trained LLM, surpassing Meta’s LLaMA 2 (67.35). | Model | Size | Leaderboard score | Commercial use or license | Pretraining length | | ------- | ---- | ----------------- | ------------------------- | ------------------ | | Falcon | 180B | 68.74 | 🟠 | 3,500B | | Llama 2 | 70B | 67.35 | 🟠 | 2,000B | | LLaMA | 65B | 64.23 | 🔴 | 1,400B | | Falcon | 40B | 61.48 | 🟢 | 1,000B | | MPT | 30B | 56.15 | 🟢 | 1,000B | The quantized Falcon models preserve similar metrics across benchmarks. The results were similar when evaluating torch.float16, 8bit, and 4bit. See results in the Open LLM Leaderboard.
|
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] |
Spread Your Wings: Falcon 180B is here
|
philschmid
|
September 6, 2023
|
falcon-180b
|
nlp, community, research, LLM
|
https://huggingface.co/blog/falcon-180b
|
Transformers With the release of Transformers 4.33, you can use Falcon 180B and leverage all the tools in the HF ecosystem, such as: - training and inference scripts and examples - safe file format (safetensors) - integrations with tools such as bitsandbytes (4-bit quantization), PEFT (parameter efficient fine-tuning) and GPTQ - assisted generation (also known as “speculative decoding”) - RoPE scaling support for larger context lengths - rich and powerful generation parameters Use of the model requires you to accept its license and terms of use. Please, make sure you are logged into your Hugging Face account and ensure you have the latest version of transformers: bash pip install --upgrade transformers huggingface-cli login
|
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] |
Spread Your Wings: Falcon 180B is here
|
philschmid
|
September 6, 2023
|
falcon-180b
|
nlp, community, research, LLM
|
https://huggingface.co/blog/falcon-180b
|
# Spread Your Wings: Falcon 180B is here
|
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Efficient Controllable Generation for SDXL with T2I-Adapters
|
Adapter
|
September 8, 2023
|
t2i-sdxl-adapters
|
guide, collaboration, diffusers, diffusion
|
https://huggingface.co/blog/t2i-sdxl-adapters
|
Training T2I-Adapter-SDXL with diffusers We built our training script on this official example provided by diffusers. Most of the T2I-Adapter models we mention in this blog post were trained on 3M high-resolution image-text pairs from LAION-Aesthetics V2 with the following settings: - Training steps: 20000-35000 - Batch size: Data parallel with a single GPU batch size of 16 for a total batch size of 128. - Learning rate: Constant learning rate of 1e-5. - Mixed precision: fp16 We encourage the community to use our scripts to train custom and powerful T2I-Adapters, striking a competitive trade-off between speed, memory, and quality.
|
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] |
Efficient Controllable Generation for SDXL with T2I-Adapters
|
Adapter
|
September 8, 2023
|
t2i-sdxl-adapters
|
guide, collaboration, diffusers, diffusion
|
https://huggingface.co/blog/t2i-sdxl-adapters
|
More Results Below, we present results obtained from using different kinds of conditions. We also supplement the results with links to their corresponding pre-trained checkpoints. Their model cards contain more details on how they were trained, along with example usage.
|
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] |
Efficient Controllable Generation for SDXL with T2I-Adapters
|
Adapter
|
September 8, 2023
|
t2i-sdxl-adapters
|
guide, collaboration, diffusers, diffusion
|
https://huggingface.co/blog/t2i-sdxl-adapters
|
Try out the Demo You can easily try T2I-Adapter-SDXL in this Space or in the playground embedded below: You can also try out Doodly, built using the sketch model that turns your doodles into realistic images (with language supervision):
|
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] |
Efficient Controllable Generation for SDXL with T2I-Adapters
|
Adapter
|
September 8, 2023
|
t2i-sdxl-adapters
|
guide, collaboration, diffusers, diffusion
|
https://huggingface.co/blog/t2i-sdxl-adapters
|
Lineart Guided Model from TencentARC/t2i-adapter-lineart-sdxl-1.0
|
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Efficient Controllable Generation for SDXL with T2I-Adapters
|
Adapter
|
September 8, 2023
|
t2i-sdxl-adapters
|
guide, collaboration, diffusers, diffusion
|
https://huggingface.co/blog/t2i-sdxl-adapters
|
Using T2I-Adapter-SDXL in diffusers Here, we take the lineart condition as an example to demonstrate the usage of T2I-Adapter-SDXL. To get started, first install the required dependencies: bash pip install -U git+https://github.com/huggingface/diffusers.git pip install -U controlnet_aux==0.0.7 # for conditioning models and detectors pip install transformers accelerate The generation process of the T2I-Adapter-SDXL mainly consists of the following two steps: 1. Condition images are first prepared into the appropriate control image format. 2. The control image and prompt are passed to the StableDiffusionXLAdapterPipeline. Let's have a look at a simple example using the Lineart Adapter. We start by initializing the T2I-Adapter pipeline for SDXL and the lineart detector. python import torch from controlnet_aux.lineart import LineartDetector from diffusers import (AutoencoderKL, EulerAncestralDiscreteScheduler, StableDiffusionXLAdapterPipeline, T2IAdapter) from diffusers.utils import load_image, make_image_grid # load adapter adapter = T2IAdapter.from_pretrained( "TencentARC/t2i-adapter-lineart-sdxl-1.0", torch_dtype=torch.float16, varient="fp16" ).to("cuda") # load pipeline model_id = "stabilityai/stable-diffusion-xl-base-1.0" euler_a = EulerAncestralDiscreteScheduler.from_pretrained( model_id, subfolder="scheduler" ) vae = AutoencoderKL.from_pretrained( "madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16 ) pipe = StableDiffusionXLAdapterPipeline.from_pretrained( model_id, vae=vae, adapter=adapter, scheduler=euler_a, torch_dtype=torch.float16, variant="fp16", ).to("cuda") # load lineart detector line_detector = LineartDetector.from_pretrained("lllyasviel/Annotators").to("cuda") Then, load an image to detect lineart: python url = "https://huggingface.co/Adapter/t2iadapter/resolve/main/figs_SDXLV1.0/org_lin.jpg" image = load_image(url) image = line_detector(image, detect_resolution=384, image_resolution=1024) Then we generate: python prompt = "Ice dragon roar, 4k photo" negative_prompt = "anime, cartoon, graphic, text, painting, crayon, graphite, abstract, glitch, deformed, mutated, ugly, disfigured" gen_images = pipe( prompt=prompt, negative_prompt=negative_prompt, image=image, num_inference_steps=30, adapter_conditioning_scale=0.8, guidance_scale=7.5, ).images[0] gen_images.save("out_lin.png") There are two important arguments to understand that help you control the amount of conditioning. 1. adapter_conditioning_scale This argument controls how much influence the conditioning should have on the input. High values mean a higher conditioning effect and vice-versa. 2. adapter_conditioning_factor This argument controls how many initial generation steps should have the conditioning applied. The value should be set between 0-1 (default is 1). The value of adapter_conditioning_factor=1 means the adapter should be applied to all timesteps, while the adapter_conditioning_factor=0.5 means it will only applied for the first 50% of the steps. For more details, we welcome you to check the official documentation.
|
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] |
Efficient Controllable Generation for SDXL with T2I-Adapters
|
Adapter
|
September 8, 2023
|
t2i-sdxl-adapters
|
guide, collaboration, diffusers, diffusion
|
https://huggingface.co/blog/t2i-sdxl-adapters
|
OpenPose Guided Model from TencentARC/t2i-adapter-openpose-sdxl-1.0 --- Acknowledgements: Immense thanks to William Berman for helping us train the models and sharing his insights.
|
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] |
Efficient Controllable Generation for SDXL with T2I-Adapters
|
Adapter
|
September 8, 2023
|
t2i-sdxl-adapters
|
guide, collaboration, diffusers, diffusion
|
https://huggingface.co/blog/t2i-sdxl-adapters
|
Depth Guided Depth guided models from TencentARC/t2i-adapter-depth-midas-sdxl-1.0 and TencentARC/t2i-adapter-depth-zoe-sdxl-1.0 respectively
|
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] |
Efficient Controllable Generation for SDXL with T2I-Adapters
|
Adapter
|
September 8, 2023
|
t2i-sdxl-adapters
|
guide, collaboration, diffusers, diffusion
|
https://huggingface.co/blog/t2i-sdxl-adapters
|
# Efficient Controllable Generation for SDXL with T2I-Adapters T2I-Adapter is an efficient plug-and-play model that provides extra guidance to pre-trained text-to-image models while freezing the original large text-to-image models. T2I-Adapter aligns internal knowledge in T2I models with external control signals. We can train various adapters according to different conditions and achieve rich control and editing effects. As a contemporaneous work, ControlNet has a similar function and is widely used. However, it can be computationally expensive to run. This is because, during each denoising step of the reverse diffusion process, both the ControlNet and UNet need to be run. In addition, ControlNet emphasizes the importance of copying the UNet encoder as a control model, resulting in a larger parameter number. Thus, the generation is bottlenecked by the size of the ControlNet (the larger, the slower the process becomes). T2I-Adapters provide a competitive advantage to ControlNets in this matter. T2I-Adapters are smaller in size, and unlike ControlNets, T2I-Adapters are run just once for the entire course of the denoising process. | Model Type | Model Parameters | Storage (fp16) | | --- | --- | --- | | ControlNet-SDXL | 1251 M | 2.5 GB | | ControlLoRA (with rank 128) | 197.78 M (84.19% reduction) | 396 MB (84.53% reduction) | | T2I-Adapter-SDXL | 79 M (93.69% reduction) | 158 MB (94% reduction) | Over the past few weeks, the Diffusers team and the T2I-Adapter authors have been collaborating to bring the support of T2I-Adapters for Stable Diffusion XL (SDXL) in diffusers. In this blog post, we share our findings from training T2I-Adapters on SDXL from scratch, some appealing results, and, of course, the T2I-Adapter checkpoints on various conditionings (sketch, canny, lineart, depth, and openpose)! Compared to previous versions of T2I-Adapter (SD-1.4/1.5), T2I-Adapter-SDXL still uses the original recipe, driving 2.6B SDXL with a 79M Adapter! T2I-Adapter-SDXL maintains powerful control capabilities while inheriting the high-quality generation of SDXL!
|
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] |
Efficient Controllable Generation for SDXL with T2I-Adapters
|
Adapter
|
September 8, 2023
|
t2i-sdxl-adapters
|
guide, collaboration, diffusers, diffusion
|
https://huggingface.co/blog/t2i-sdxl-adapters
|
Canny Guided Model from TencentARC/t2i-adapter-canny-sdxl-1.0
|
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] |
Efficient Controllable Generation for SDXL with T2I-Adapters
|
Adapter
|
September 8, 2023
|
t2i-sdxl-adapters
|
guide, collaboration, diffusers, diffusion
|
https://huggingface.co/blog/t2i-sdxl-adapters
|
Sketch Guided Model from TencentARC/t2i-adapter-sketch-sdxl-1.0
|
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] |
SafeCoder vs. Closed-source Code Assistants
|
julsimon
|
September 11, 2023
|
safecoder-vs-closed-source-code-assistants
|
bigcode
|
https://huggingface.co/blog/safecoder-vs-closed-source-code-assistants
|
Security and privacy Security is always a top concern, all the more when source code is involved. Intellectual property and privacy must be protected at all costs. Whether you run on-premise or in the cloud, SafeCoder is under your complete administrative control. You can apply and monitor your security checks and maintain strong and consistent compliance across your IT platform. SafeCoder doesn't spy on any of your data. Your prompts and suggestions are yours and yours only. SafeCoder doesn't call home and send telemetry data to Hugging Face or anyone else. No one but you needs to know how and when you're using SafeCoder. SafeCoder doesn't even require an Internet connection. You can (and should) run it fully air-gapped. Closed-source services rely on the security of the underlying cloud. Whether this works or not for your compliance posture is your call. For enterprise users, prompts and suggestions are not stored (they are for individual users). However, we regret to point out that GitHub collects "user engagement data" with no possibility to opt-out. AWS does the same by default but lets you opt out.
|
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] |
SafeCoder vs. Closed-source Code Assistants
|
julsimon
|
September 11, 2023
|
safecoder-vs-closed-source-code-assistants
|
bigcode
|
https://huggingface.co/blog/safecoder-vs-closed-source-code-assistants
|
Transparency In line with the Chinchilla Scaling Law, SafeCoder is a compute-optimal model trained on 1 trillion (1,000 billion) code tokens. These tokens are extracted from The Stack, a 2.7 terabyte dataset built from permissively licensed open-source repositories. All efforts are made to honor opt-out requests, and we built a tool that lets repository owners check if their code is part of the dataset. In the spirit of transparency, our research paper discloses the model architecture, the training process, and detailed metrics. Unfortunately, closed-source services stick to vague information, such as "[the model was trained on] billions of lines of code." To the best of our knowledge, no metrics are available.
|
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] |
SafeCoder vs. Closed-source Code Assistants
|
julsimon
|
September 11, 2023
|
safecoder-vs-closed-source-code-assistants
|
bigcode
|
https://huggingface.co/blog/safecoder-vs-closed-source-code-assistants
|
Customization The StarCoder models have been specifically designed to be customizable, and we have already built different versions: * StarCoderBase: the original model trained on 80+ languages from The Stack. * StarCoder: StarCoderBase further trained on Python. * StarCoder+: StarCoderBase further trained on English web data for coding conversations. We also shared the fine-tuning code on GitHub. Every company has its preferred languages and coding guidelines, i.e., how to write inline documentation or unit tests, or do's and don'ts on security and performance. With SafeCoder, we can help you train models that learn the peculiarities of your software engineering process. Our team will help you prepare high-quality datasets and fine-tune StarCoder on your infrastructure. Your data will never be exposed to anyone. Unfortunately, closed-source services cannot be customized.
|
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] |
SafeCoder vs. Closed-source Code Assistants
|
julsimon
|
September 11, 2023
|
safecoder-vs-closed-source-code-assistants
|
bigcode
|
https://huggingface.co/blog/safecoder-vs-closed-source-code-assistants
|
Conclusion We're very excited about the future of SafeCoder, and so are our customers. No one should have to compromise on state-of-the-art code generation, transparency, customization, IT flexibility, security, and privacy. We believe SafeCoder delivers them all, and we'll keep working hard to make it even better. If you’re interested in SafeCoder for your company, please contact us. Our team will contact you shortly to learn more about your use case and discuss requirements. Thanks for reading!
|
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] |
SafeCoder vs. Closed-source Code Assistants
|
julsimon
|
September 11, 2023
|
safecoder-vs-closed-source-code-assistants
|
bigcode
|
https://huggingface.co/blog/safecoder-vs-closed-source-code-assistants
|
# SafeCoder vs. Closed-source Code Assistants For decades, software developers have designed methodologies, processes, and tools that help them improve code quality and increase productivity. For instance, agile, test-driven development, code reviews, and CI/CD are now staples in the software industry. In "How Google Tests Software" (Addison-Wesley, 2012), Google reports that fixing a bug during system tests - the final testing stage - is 1000x more expensive than fixing it at the unit testing stage. This puts much pressure on developers - the first link in the chain - to write quality code from the get-go. For all the hype surrounding generative AI, code generation seems a promising way to help developers deliver better code fast. Indeed, early studies show that managed services like GitHub Copilot or Amazon CodeWhisperer help developers be more productive. However, these services rely on closed-source models that can't be customized to your technical culture and processes. Hugging Face released SafeCoder a few weeks ago to fix this. SafeCoder is a code assistant solution built for the enterprise that gives you state-of-the-art models, transparency, customizability, IT flexibility, and privacy. In this post, we'll compare SafeCoder to closed-source services and highlight the benefits you can expect from our solution.
|
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] |
SafeCoder vs. Closed-source Code Assistants
|
julsimon
|
September 11, 2023
|
safecoder-vs-closed-source-code-assistants
|
bigcode
|
https://huggingface.co/blog/safecoder-vs-closed-source-code-assistants
|
IT flexibility SafeCoder relies on Docker containers for fine-tuning and deployment. It's easy to run on-premise or in the cloud on any container management service. In addition, SafeCoder includes our Optimum hardware acceleration libraries. Whether you work with CPU, GPU, or AI accelerators, Optimum will kick in automatically to help you save time and money on training and inference. Since you control the underlying hardware, you can also tune the cost-performance ratio of your infrastructure to your needs. Unfortunately, closed-source services are only available as managed services.
|
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-0.01933552697300911
] |
SafeCoder vs. Closed-source Code Assistants
|
julsimon
|
September 11, 2023
|
safecoder-vs-closed-source-code-assistants
|
bigcode
|
https://huggingface.co/blog/safecoder-vs-closed-source-code-assistants
|
State-of-the-art models SafeCoder is currently built on top of the StarCoder models, a family of open-source models designed and trained within the BigCode collaborative project. StarCoder is a 15.5 billion parameter model trained for code generation in over 80 programming languages. It uses innovative architectural concepts, like Multi-Query Attention (MQA), to improve throughput and reduce latency, a technique also present in the Falcon and adapted for LLaMa 2 models. StarCoder has an 8192-token context window, helping it take into account more of your code to generate new code. It can also do fill-in-the-middle, i.e., insert within your code, instead of just appending new code at the end. Lastly, like HuggingChat, SafeCoder will introduce new state-of-the-art models over time, giving you a seamless upgrade path. Unfortunately, closed-source code assistant services don't share information about the underlying models, their capabilities, and their training data.
|
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] |
Overview of natively supported quantization schemes in 🤗 Transformers
|
ybelkada
|
September 12, 2023
|
overview-quantization-transformers
|
llm, optimization, quantization, comparison, bitsandbytes, gptq
|
https://huggingface.co/blog/overview-quantization-transformers
|
use_cache Let's test use_cache to better understand the impact of caching the hidden state during the generation. The benchmark was run on an A100 with a prompt length of 30 and we generated exactly 30 tokens. The model we used was meta-llama/Llama-2-7b-hf. with use_cache=True with use_cache=False From the two benchmarks, we conclude that generation is faster when we use attention caching, as expected. Moreover, GPTQ is, in general, faster than bitsandbytes. For example, with batch_size=4 and use_cache=True, it is twice as fast! Therefore let’s use use_cache for the next benchmarks. Note that use_cache will consume more memory.
|
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] |
Overview of natively supported quantization schemes in 🤗 Transformers
|
ybelkada
|
September 12, 2023
|
overview-quantization-transformers
|
llm, optimization, quantization, comparison, bitsandbytes, gptq
|
https://huggingface.co/blog/overview-quantization-transformers
|
Generation length In the following benchmark, we will try different generation lengths to see their impact on the quantized model. It was run on a A100 and we used a prompt length of 30, and varied the number of generated tokens. The model we used was meta-llama/Llama-2-7b-hf. with 30 tokens generated: with 512 tokens generated: From the benchmark above, we can conclude that GPTQ is faster than bitsandbytes independently of the generation length.
|
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] |
Overview of natively supported quantization schemes in 🤗 Transformers
|
ybelkada
|
September 12, 2023
|
overview-quantization-transformers
|
llm, optimization, quantization, comparison, bitsandbytes, gptq
|
https://huggingface.co/blog/overview-quantization-transformers
|
Generate speed The following benchmarks measure the generation speed of the model during inference. The benchmarking script can be found here for reproducibility.
|
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] |
Overview of natively supported quantization schemes in 🤗 Transformers
|
ybelkada
|
September 12, 2023
|
overview-quantization-transformers
|
llm, optimization, quantization, comparison, bitsandbytes, gptq
|
https://huggingface.co/blog/overview-quantization-transformers
|
Conclusion and final words In this blogpost, we compared bitsandbytes and GPTQ quantization across multiple setups. We saw that bitsandbytes is better suited for fine-tuning while GPTQ is better for generation. From this observation, one way to get better merged models would be to: - (1) quantize the base model using bitsandbytes (zero-shot quantization) - (2) add and fine-tune the adapters - (3) merge the trained adapters on top of the base model or the dequantized model ! - (4) quantize the merged model using GPTQ and use it for deployment We hope that this overview will make it easier for everyone to use LLMs in their applications and usecases, and we are looking forward to seeing what you will build with it!
|
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