BLOOM: A Multilingual AI Model that Can Generate Text in Any Language
This is the configuration class to store the configuration of a BloomModel. It is used to instantiate a Bloommodel according to the specified arguments, defining the model architecture. Instantiating a configuration with thedefaults will yield a similar configuration to the Bloom architecturebigscience/bloom.
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This model inherits from PreTrainedModel. Check the superclass documentation for the generic methods thelibrary implements for all its model (such as downloading or saving, resizing the input embeddings etc.)
In this post, we demonstrate how to deploy a BLOOM-176B model with custom inference code. We have hosted the model in a public S3 location for ease of use. In that case, you should use the parameter option.s3url. Set this to the URI of the Amazon S3 bucket that contains the model artifacts. The DJL container automatically downloads the model artifacts from the S3 bucket to the hosting instance using the highly optimized s5cmd. The model artifacts are downloaded into /tmp. SageMaker makes the mounted EBS volume specified by VolumeSizeInGB available under /tmp on the container. This same location is also used when mapping the SSD memory available for instances which support SSD.
We have provided the steps in this section in case you want to download the model to Amazon S3 and use it from there. The steps are provided in the Jupyter file on GitHub. The following screenshot shows a snapshot of the steps.
How to download and use BLOOM, the world's largest open multilingual language model
BLOOM: A 176 billion parameter language model for 46 natural languages and 13 programming languages
Download BLOOM from Hugging Face and explore its capabilities
BLOOM: The first transparently trained multilingual LLM with a Responsible AI License
What is BLOOM and how can it generate text in multiple languages and domains
BLOOM: A collaborative project of over 1000 researchers from 70+ countries and 250+ institutions
How to run BLOOM on a local machine or on a cloud provider with transformers and accelerate
BLOOM: A living family of models that will continue to improve and expand
How to instruct BLOOM to perform text tasks it hasn't been explicitly trained for
BLOOM: A seed for future research on large language models and their impacts
How to access the intermediary checkpoints and optimizer states of BLOOM training
BLOOM: A model trained on the Jean Zay supercomputer in France with a 3M compute grant
How to evaluate BLOOM on various benchmarks and datasets
BLOOM: A model that covers 46 languages from 14 language families and 13 programming languages from 6 paradigms
How to fine-tune BLOOM for specific tasks or domains
BLOOM: A model that uses the GPT-3 architecture with some modifications and optimizations
How to compress BLOOM into a more usable version with the same level of performance
BLOOM: A model that uses the OSCAR corpus as its main source of training data
How to contribute to the BigScience project and help improve BLOOM
BLOOM: A model that aims to democratize access to large language models and foster open science
How to use the inference API for large-scale use of BLOOM without dedicated hardware or engineering
BLOOM: A model that follows the best practices of Responsible AI and addresses foreseeable harms and limitations
How to study the internal operations and behavior of BLOOM using various tools and methods
BLOOM: A model that leverages the Hugging Face ecosystem for easy integration and deployment
How to compare BLOOM with other large language models such as GPT-3, GPT-J, or T0++
BLOOM: A model that can handle code generation, code completion, code summarization, and code documentation tasks
How to generate high-quality text in different languages and genres using BLOOM
BLOOM: A model that can produce coherent text that is hardly distinguishable from text written by humans
How to make BLOOM more instructable and controllable using natural language commands or prefixes
BLOOM: A model that can learn from its own generated text and improve over time
How to use BLOOM for creative writing, such as poetry, stories, lyrics, or jokes
BLOOM: A model that can mimic the style and tone of different authors, celebrities, or characters
How to use BLOOM for knowledge extraction, such as summarization, question answering, or fact checking
BLOOM: A model that can access a large amount of information from various sources and domains
How to use BLOOM for natural language understanding, such as sentiment analysis, classification, or parsing
BLOOM: A model that can capture the semantics and syntax of natural language at different levels of granularity
How to use BLOOM for natural language generation, such as dialogue, translation, or paraphrasing
BLOOM: A model that can produce fluent and diverse natural language outputs for various purposes and audiences
How to use BLOOM for multimodal tasks, such as image captioning, text-to-speech, or speech recognition
BLOOM: A model that can integrate different modalities of information and communication
The only thing where Bloom is not that good at is in downloading single pictures. You need to open them in the program's viewer, right click on them and select "Save Image". It's definitely a tool made to work with photos in batches.
Can any one tell me, how much ram, gpu ram, and disk space is required to run bloom locally. I have tried to run ir and it has downloaded 180gb of data and still its in download process. so if it finish what are chances to run it locally?I have rtx 3070
Having enough RAM to hold the entire model would reduce the execution time; however, you would still be cpu bounded. I did a quick test and, once a BLOOM block is in RAM, my CPU (i5 11gen) takes in average 0.45 sec to run a forward pass on a single bloom block. Therefore, assuming the 70 blocks are already in RAM, you could expect around 70*0.45 sec = 31.5 sec per token.
Bloom can be downloaded for free on Hugging Face and is said to be on par with GPT-3 for accuracy - and also toxicity. A key difference from GPT-3 is a stronger focus on languages away from the otherwise dominant English language.
You can also follow this guide to setup a serving system to serve smaller versions of OPT, such as OPT-66B, OPT-30B, etc.Pick an appropriate size from OPT weight downloading page based on your available resources.
Run the script first on a driver node. The driver node will download the weights to its local disk, but the script will fail later because worker nodes cannot access the weights.You can then manually copy all downloaded weights under path from the driver node to all worker nodes.
Organic effluent enrichment in water may selectively promote algal growth, resulting in water pollution and posing a threat to the aquatic ecosystem. Recent harmful algal blooms (HABs) incidents have highlighted information gaps that still exist, as well as the heightened need for early detection technology developments. Although previous research has demonstrated the importance of deep learning in the identification of algal genera, it is still a challenge to identify or to develop the best-suited convolution neural network (CNN) model for effective monitoring of bloom-forming algae. In the present study, efficiency of deep learning models (MobileNet V-2, Visual Geometry Group-16 (VGG-16), AlexNet, and ResNeXt-50) have been evaluated for the classification of 15 bloom-forming algae. To obtain a high level of accuracy, different convolution layers with adaptive moment estimation (Adam), root-mean-square propagation (RMSprop) as optimizers with softmax and rectified linear unit (ReLU) as activation factors have been used. The classification accuracies of 40, 96, 98, and 99% have been achieved for MobileNet V-2, VGG-16, AlexNet, and ResNeXt-50 model, respectively. We believe that the ResNeXt-50 has the potential to identify algae in a variety of situations with high accuracy and in real time, regardless of the underlying hardware. Such studies pave the path for future AI-based cleaner technologies associated with phycological studies for a sustainable future.
Recently, various studies have applied AI-based methods to characterize these bloom-forming algae by various neural networks but their accuracy and reliability are uncertain. Promdaen et al. (2014) exhibit a computerized acknowledgment framework using texture and shape features for the classification of 12 algal genera by sequential minimal optimization (SMO). The affirmation for the viability of the technique regarding 97.22% characterization exactness has been done by them. Li et al. (2017) exhibit a promising and proficient arrangement through the Mueller matrix image analysis framework dependent on the deep neural network for the grouping of morphology, shape, and external features based on comparative algal studies. For the characterization of the algal images, only a few studies were taken into account when examining algal bloom using CNN.
For strong performance on image classification, CNNs have made great achievements. The current research applied the most acceptable deep CNNs, including MobileNet version 2, Vgg16, AlexNet, and ResNeXt 50, and also examined the potential capacity of these models when applied to the dataset comprising of algal pictures. A proportional analysis of the performance of models is given for 15 bloom-forming algal genera. Usually, a CNN structure consists of multiple convolutionary architecture blocks and a layer that is completely connected. A convolutionary layer conducts operations of convolution over the performance using a set of filters or kernels to extract the characteristics of the preceding layers that are important for classification.
Present research target 15 genera of bloom-forming algae with a dataset comprised of 450 algal images as input data. These images were gathered from various open access web depositories (CRIS database, galerie.sinicearasy.cz) and past phycological examinations by phycologists in previous studies as mentioned in Yadav et al. (2020).
Toxins produced by HABs can be harmful to fish and other aquatic creatures. These toxins migrate up the food chain after being digested by small fish and shellfish, affecting larger animals such as sea lions, turtles, dolphins, birds, and manatees. However, the actual health risks presented by these toxins in water resources utilized for recreation and drinking water to the general public, pets, livestock, and wildlife are yet unknown but due to various natural and anthropogenic activities, global trends in the prevalence, toxicity, and risk posed by harmful algal blooms are commonly assumed to be on the rise. Rapid classification of HAB forming algae is a need of time because the health effects, HAB toxins can range from minor to severe, and in some cases, lethal, depending on the quantity of exposure and the type of algal toxins involved.
Using electron microscopes, morphological studies revealed differences in traits such as the flagellar apparatus, cell division mechanism, and organelle structure and function, all of which are significant in algal categorization. Standard microbiological techniques focused on isolation and identification, as well as molecular techniques, are needed to characterize the microalgal community. Li et al. (2017) used the convolutional neural networks (CNNs) for the classification of algae (with morphological resemblances) and achieve a 97% accuracy by Mueller matrix imaging system. With the advancement of artificial intelligence, a deep convolutional neural network (CNN) employing microscopic images of algae could substantially aid in detecting water quality and become a major solution for image categorization (Wang et al. 2020). The performances of the automated models have been deeply impacted by the comparative morphological appearance of various bloom-forming algae (Zhang et al. 2021). The accuracies of the models have been compromised when the algae have similar morphological features and seek a detailed analysis to resolve this miscalculation.