Apple
Models by this creator
🔎
OpenELM
1.3K
OpenELM is an open-source family of efficient language models developed by Apple. They use a layer-wise scaling strategy to allocate parameters efficiently within each transformer layer, leading to enhanced accuracy. The OpenELM models range from 270M to 3B parameters and are available in both base and instruction-tuned versions. The models were pretrained on a large corpus of around 1.8 trillion tokens, including datasets like RefinedWeb, PILE, RedPajama, and Dolma v1.6. Compared to similar models like openchat_3.5, OpenELM offers improved efficiency and performance. Model inputs and outputs Inputs Text**: The OpenELM models accept text as input and can be used for a variety of natural language processing tasks. Outputs Text**: The models generate human-readable text as output, making them suitable for tasks like language generation, question answering, and dialogue. Capabilities OpenELM has shown strong performance on a range of benchmarks, including MMLU, HumanEval, MATH, and GSM8k. The instruction-tuned versions are particularly adept at following prompts and generating helpful, coherent responses. What can I use it for? The OpenELM models can be used as a foundation for building various natural language applications, such as: Language generation**: Use the models to generate human-like text for creative writing, content creation, or chatbots. Question answering**: Fine-tune the models to answer questions on a wide range of topics. Dialogue systems**: Leverage the instruction-tuned versions to build conversational AI assistants. Things to try One interesting aspect of OpenELM is its use of layer-wise scaling to optimize parameter allocation. This approach could lead to insights about efficient model design and potentially inspire new architectures or training techniques. Additionally, the open-source nature of the models presents opportunities for the community to further fine-tune and adapt them for specialized use cases, contributing to the broader progress of language models.
Updated 5/28/2024
🏅
OpenELM-3B-Instruct
274
OpenELM-3B-Instruct is a 3 billion parameter language model developed by Apple. It is part of the OpenELM family of efficient language models that use a layer-wise scaling strategy to enhance accuracy. The model was pretrained on a large corpus of data, including RefinedWeb, deduplicated PILE, a subset of RedPajama, and a subset of Dolma v1.6, totaling approximately 1.8 trillion tokens. Similar models in the OpenELM family include OpenELM-270M, OpenELM-450M, OpenELM-1_1B, and their respective instruction-tuned versions. These models aim to provide efficient and high-performing language models for a variety of natural language processing tasks. Model Inputs and Outputs Inputs Text input for the model to generate output Outputs Generated text output Capabilities The OpenELM-3B-Instruct model demonstrates strong performance across a range of benchmark tasks, including ARC-c, ARC-e, BoolQ, HellaSwag, PIQA, SciQ, and WinoGrande. It outperforms many existing 3 billion parameter models, setting a new standard for efficiency and accuracy in this size range. What Can I Use It For? The OpenELM-3B-Instruct model can be used for a variety of natural language generation tasks, such as text summarization, language translation, and content creation. Its strong performance on benchmarks suggests it could be a valuable tool for researchers and developers working on advanced language models and natural language processing applications. Things to Try One interesting aspect of the OpenELM-3B-Instruct model is its support for lookup token speculative generation, which can help speed up inference. Developers could experiment with this feature to optimize the model's performance for their specific use cases. Additionally, the model's ability to be used with an assistive model for model-wise speculative generation could be an interesting area to explore, allowing for potential improvements in the model's helpfulness and safety.
Updated 5/28/2024
⚙️
OpenELM-3B
104
OpenELM is a family of open-source efficient language models created by Apple. The models use a layer-wise scaling strategy to efficiently allocate parameters within each layer of the transformer model, leading to enhanced accuracy. OpenELM models are available in a range of sizes from 270M to 3B parameters, with both pretrained and instruction-tuned versions. Model Inputs and Outputs OpenELM models take in natural language prompts as input and generate coherent, contextual text as output. The models can be used for a variety of language tasks such as text generation, summarization, and question answering. Inputs Natural language prompts Outputs Coherent, contextual text generated in response to the input prompt Capabilities OpenELM models exhibit strong performance on a range of language tasks, including question answering, common sense reasoning, and language understanding. The models show competitive results compared to other large language models, with the larger 3B parameter version outperforming on many benchmarks. What Can I Use it For? The OpenELM models can be used for a wide variety of natural language processing applications, such as: Content generation**: Generate coherent and contextual text for tasks like story writing, article summarization, and dialogue response. Language understanding**: Use the models for tasks like text classification, question answering, and relation extraction. Conversational AI**: Integrate the models into chatbots and virtual assistants to enable more natural and engaging interactions. Things to Try One interesting aspect of the OpenELM models is the use of the layer-wise scaling strategy, which allows the models to allocate parameters more efficiently across layers. This could enable interesting explorations into model compression and efficient inference on resource-constrained devices. Additionally, the availability of both pretrained and instruction-tuned versions of the models opens up possibilities for prompt engineering and few-shot learning experiments. Developers can explore how the models respond to different prompts and fine-tune them for specific use cases.
Updated 5/23/2024
🎲
OpenELM-270M-Instruct
99
The OpenELM-270M-Instruct is a 270M parameter open-source efficient language model developed by Apple. It uses a layer-wise scaling strategy to efficiently allocate parameters within each layer of the transformer model, leading to enhanced accuracy. OpenELM models are pretrained using the CoreNet library and released in several sizes with both pretrained and instruction-tuned variants. The OpenELM-270M-Instruct model is part of a family of OpenELM models that also includes the OpenELM-3B-Instruct, a larger 3B parameter version. These models are designed for text-to-text generation tasks and have shown strong performance on a variety of benchmarks like ARC-c, HellaSwag, and WinoGrande. Model inputs and outputs Inputs Text prompts to be used for text generation Outputs Conditional text generation based on the input prompts Capabilities The OpenELM-270M-Instruct model is capable of generating high-quality and coherent text across a range of domains. It has shown strong performance on tasks like question answering, common sense reasoning, and open-ended text generation. Compared to similar sized models, the OpenELM-270M-Instruct has demonstrated improved accuracy and efficiency. What can I use it for? The OpenELM-270M-Instruct model can be used for a variety of natural language processing applications, such as: Chatbots and conversational assistants Content generation (e.g. stories, articles, product descriptions) Question answering and knowledge retrieval Text summarization and simplification As an open-source model, developers can fine-tune the OpenELM-270M-Instruct for their specific use cases or incorporate it into larger language models or applications. Things to try One interesting aspect of the OpenELM-270M-Instruct is its use of layer-wise scaling to efficiently allocate parameters. This allows the model to achieve strong performance while being more compact than models of similar size. Developers can experiment with different ways of leveraging this efficiency, such as deploying the model on low-resource devices or incorporating it into ensemble models. Another area to explore is the instruction tuning process used to create the OpenELM-Instruct variants. Analyzing the impact of this fine-tuning on the model's capabilities and safety could provide insights for developing more robust and versatile language models.
Updated 5/28/2024
🤿
AIM
86
AIM is a collection of vision models pre-trained with an autoregressive generative objective, introduced by researchers at Apple. The models demonstrate that autoregressive pre-training of image features can exhibit similar scaling properties to large language models. Key findings include the ability to scale the model capacity to billions of parameters and effectively leverage large uncurated image datasets. Similar models include OpenELM, a family of efficient open-source language models developed by Apple. Like AIM, OpenELM utilizes a layer-wise scaling strategy to allocate parameters efficiently within the transformer architecture. Model Inputs and Outputs AIM takes images as input and generates a set of logits as output, which can be used for various downstream tasks such as image classification. The model uses the val_transforms function from the aim.torch.data module to preprocess the input images. Inputs Images, preprocessed using the val_transforms function Outputs Logits, representing the model's predictions Additional output, such as intermediate representations, depending on the specific use case Capabilities AIM demonstrates the ability to effectively leverage large-scale image datasets for pre-training, resulting in strong performance across a variety of computer vision benchmarks. The model's autoregressive nature allows it to capture rich visual features that can be useful for tasks like image classification, generation, and understanding. What Can I Use It For? The AIM models can be used for a range of computer vision applications, including image classification, generation, and understanding. Potential use cases include: Image Classification**: Fine-tune the AIM model on a labeled dataset to perform image classification tasks. Image Generation**: Use the autoregressive nature of AIM to generate novel images conditioned on text or other inputs. Transfer Learning**: Leverage the pre-trained visual representations of AIM as a feature extractor for other computer vision tasks. Things to Try One interesting aspect of AIM is its ability to scale to very large model sizes, up to billions of parameters. Experiment with different model sizes and compare the performance on your specific task to explore the scaling properties of the model. Additionally, try combining AIM with other techniques, such as few-shot learning or adversarial training, to further enhance its capabilities.
Updated 5/28/2024
🌿
coreml-stable-diffusion-2-base
77
The coreml-stable-diffusion-2-base model is a text-to-image generation model developed by Apple. It is a version of the Stable Diffusion v2 model that has been converted for use on Apple Silicon hardware. This model is capable of generating high-quality images from text prompts and can be used with the diffusers library. The model was trained on a filtered subset of the large-scale LAION-5B dataset, with a focus on images with high aesthetic quality and the removal of explicit pornographic content. It uses a Latent Diffusion Model architecture that combines an autoencoder with a diffusion model, along with a fixed, pretrained text encoder (OpenCLIP-ViT/H). There are four variants of the Core ML weights available, with different attention mechanisms and compilation targets. Users can choose the version that best fits their needs, whether that's Swift-based inference or Python-based inference, and the "original" or "split_einsum" attention mechanisms. Model inputs and outputs Inputs Text prompt**: A natural language description of the desired image. Outputs Generated image**: The model outputs a high-quality image that corresponds to the input text prompt. Capabilities The coreml-stable-diffusion-2-base model is capable of generating a wide variety of images from text prompts, including scenes, objects, and abstract concepts. It can produce photorealistic images, as well as more stylized or imaginative compositions. The model performs well on a range of prompts, though it may struggle with more complex or compositional tasks. What can I use it for? The coreml-stable-diffusion-2-base model is intended for research purposes only. Possible applications include: Safe deployment of generative models**: Researching techniques to safely deploy models that have the potential to generate harmful content. Understanding model biases**: Probing the limitations and biases of the model to improve future iterations. Creative applications**: Generating artwork, designs, and other creative content. Educational tools**: Developing interactive educational or creative applications. Generative model research**: Furthering the state of the art in text-to-image generation. The model should not be used to create content that is harmful, offensive, or in violation of copyrights. Things to try One interesting aspect of the coreml-stable-diffusion-2-base model is the availability of different attention mechanisms and compilation targets. Users can experiment with the "original" and "split_einsum" attention variants to see how they perform on their specific use cases and hardware setups. Additionally, the model's ability to generate high-quality images at 512x512 resolution makes it a compelling tool for creative applications and research.
Updated 5/28/2024
➖
OpenELM-270M
60
The OpenELM-270M model is part of the OpenELM family of open-source efficient language models developed by researchers at Apple. OpenELM uses a layer-wise scaling strategy to efficiently allocate parameters within each layer of the transformer model, leading to enhanced accuracy. The models were pretrained on a large dataset containing RefinedWeb, deduplicated PILE, and subsets of RedPajama and Dolma v1.6, totaling around 1.8 trillion tokens. OpenELM models are available in different sizes, including 270M, 450M, 1.1B, and 3B parameters, with both pretrained and instruction-tuned versions. Model inputs and outputs Inputs Text prompt**: The model takes in a text prompt as input, which can be used to generate continued text. Outputs Continued text**: The model outputs a continuation of the provided text prompt, generating coherent and contextually relevant text. Capabilities The OpenELM-270M model demonstrates strong performance across a variety of benchmark tasks, including common sense reasoning, reading comprehension, and natural language understanding. It achieves high scores on datasets like ARC, BoolQ, HellaSwag, PIQA, SciQ, and WinoGrande. Additionally, the instruction-tuned OpenELM-270M-Instruct model shows further improvements in several of these areas. What can I use it for? The OpenELM models can be used for a wide range of natural language processing tasks, such as text generation, question answering, and language understanding. Developers and researchers can leverage these efficient models to build applications that require language-based capabilities, while benefiting from the open-source nature and transparency of the project. As with any large language model, it is important to carefully evaluate the model's performance and potential biases for your specific use case. Things to try One interesting aspect of the OpenELM models is the ability to leverage different techniques to improve inference speed, such as lookup token speculative generation and model-wise speculative generation with an assistive model. Developers can experiment with these strategies to find the right balance between performance and efficiency for their particular applications.
Updated 6/4/2024
🤯
coreml-stable-diffusion-xl-base
58
The coreml-stable-diffusion-xl-base model is a text-to-image generation model developed by Apple. It is based on the Stable Diffusion XL (SDXL) model, which consists of an ensemble of experts pipeline for latent diffusion. The base model generates initial noisy latents, which are then further processed with a refinement model to produce the final denoised image. Alternatively, the base model can be used on its own in a two-stage pipeline to first generate latents and then apply a specialized high-resolution model for the final image. Model inputs and outputs The coreml-stable-diffusion-xl-base model takes text prompts as input and generates corresponding images as output. The text prompts can describe a wide variety of scenes, objects, and concepts, which the model then translates into visual form. Inputs Text prompt**: A natural language description of the desired image, such as "a photo of an astronaut riding a horse on mars". Outputs Generated image**: The model outputs a corresponding image based on the input text prompt. Capabilities The coreml-stable-diffusion-xl-base model is capable of generating high-quality, photorealistic images from text prompts. It can create a wide range of scenes, objects, and concepts, and performs significantly better than previous versions of Stable Diffusion. The model can also be used in a two-stage pipeline with a specialized high-resolution refinement model to further improve image quality. What can I use it for? The coreml-stable-diffusion-xl-base model is intended for research purposes, such as the generation of artworks, applications in educational or creative tools, and probing the limitations and biases of generative models. The model should not be used to create content that is harmful, offensive, or misrepresents people or events. Things to try Experiment with different text prompts to see the variety of images the model can generate. Try combining the base model with the stable-diffusion-xl-refiner-1.0 model to see if the additional refinement step improves the image quality. Explore the model's capabilities and limitations, and consider how it could be applied in creative or educational contexts.
Updated 5/28/2024
🎲
coreml-stable-diffusion-v1-5
53
The coreml-stable-diffusion-v1-5 model is a version of the Stable Diffusion v1-5 model that has been converted to Core ML format for use on Apple Silicon hardware. It was developed by Hugging Face using Apple's repository, which has an ASCL license. The Stable Diffusion v1-5 model is a latent text-to-image diffusion model capable of generating photo-realistic images from text prompts. This model was initialized with the weights of the Stable-Diffusion-v1-2 checkpoint and subsequently fine-tuned to improve classifier-free guidance sampling. There are four variants of the Core ML weights available, including different attention implementations and compilation options for Swift and Python inference. Model inputs and outputs Inputs Text prompt**: The text prompt describing the desired image to be generated. Outputs Generated image**: The photo-realistic image generated based on the input text prompt. Capabilities The coreml-stable-diffusion-v1-5 model is capable of generating a wide variety of photo-realistic images from text prompts, ranging from landscapes and scenes to intricate illustrations and creative concepts. Like other Stable Diffusion models, it excels at rendering detailed, imaginative imagery, but may struggle with tasks involving more complex compositionality or generating legible text. What can I use it for? The coreml-stable-diffusion-v1-5 model is intended for research purposes, such as exploring the capabilities and limitations of generative models, generating artworks and creative content, and developing educational or creative tools. However, the model should not be used to intentionally create or disseminate images that could be harmful, disturbing, or offensive, or to impersonate individuals without their consent. Things to try One interesting aspect of the coreml-stable-diffusion-v1-5 model is the availability of different attention implementations and compilation options, which can affect the performance and memory usage of the model on Apple Silicon hardware. Developers may want to experiment with these variants to find the best balance of speed and efficiency for their specific use cases.
Updated 5/28/2024