Amazon
Models by this creator
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MistralLite
425
The MistralLite model is a fine-tuned version of the Mistral-7B-v0.1 language model, with enhanced capabilities for processing long contexts up to 32K tokens. By utilizing an adapted Rotary Embedding and sliding window during fine-tuning, MistralLite is able to perform significantly better on several long context retrieval and answering tasks, while keeping the simple model structure of the original model. MistralLite is similar to the Mistral-7B-Instruct-v0.1 model, with key differences in the maximum context length, Rotary Embedding adaptation, and sliding window size. Model inputs and outputs MistralLite is a text-to-text model that can be used for a variety of natural language processing tasks, such as long context line and topic retrieval, summarization, and question-answering. The model takes in text prompts as input and generates relevant text outputs. Inputs Text prompts**: MistralLite can process text prompts up to 32,000 tokens in length. Outputs Generated text**: MistralLite outputs relevant text based on the input prompt, which can be used for tasks like long context retrieval, summarization, and question-answering. Capabilities The key capability of MistralLite is its ability to effectively process and generate text for long contexts, up to 32,000 tokens. This is a significant improvement over the original Mistral-7B-Instruct-v0.1 model, which was limited to 8,000 token contexts. MistralLite's enhanced performance on long context tasks makes it well-suited for applications that require retrieving and answering questions based on lengthy input texts. What can I use it for? With its ability to process long contexts, MistralLite can be a valuable tool for a variety of applications, such as: Long context line and topic retrieval**: MistralLite can be used to quickly identify relevant lines or topics within lengthy documents or conversations. Summarization**: MistralLite can generate concise summaries of long input texts, making it easier for users to quickly understand the key points. Question-answering**: MistralLite can be used to answer questions based on long input passages, providing users with relevant information without requiring them to read through the entire text. Things to try One key aspect of MistralLite is its use of an adapted Rotary Embedding and sliding window during fine-tuning. This allows the model to better process long contexts without significantly increasing the model complexity. Developers may want to experiment with different hyperparameter settings for the Rotary Embedding and sliding window to further optimize MistralLite's performance on their specific use cases. Additionally, since MistralLite is built on top of the Mistral-7B-v0.1 model, users may want to explore ways to leverage the capabilities of the original Mistral model in conjunction with the enhancements made in MistralLite.
Updated 5/28/2024
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FalconLite
173
FalconLite is a quantized version of the Falcon 40B SFT OASST-TOP1 model, capable of processing long input sequences while consuming 4x less GPU memory. By utilizing 4-bit GPTQ quantization and adapted dynamic NTK RotaryEmbedding, FalconLite achieves a balance between latency, accuracy, and memory efficiency. With the ability to process 5x longer contexts than the original model, FalconLite is useful for applications such as topic retrieval, summarization, and question-answering. It can be deployed on a single AWS g5.12x instance with TGI 0.9.2, making it suitable for resource-constrained environments. Model inputs and outputs Inputs Text data**: FalconLite can process long input sequences up to 11K tokens. Outputs Text generation**: The model generates text in response to the input. Capabilities FalconLite can handle long input sequences, making it useful for applications like topic retrieval, summarization, and question-answering. Its ability to process 5x longer contexts than the original Falcon 40B model while consuming 4x less GPU memory demonstrates its efficiency and memory-friendliness. What can I use it for? FalconLite can be used in resource-constrained environments for applications that require high performance and the ability to handle long input sequences. This could include tasks like: Content summarization Question-answering Topic retrieval Generating responses to long prompts The model's efficiency and memory-friendly design make it suitable for deployment on a single AWS g5.12x instance, which can be beneficial for businesses or organizations with limited computing resources. Things to try One interesting aspect of FalconLite is its use of 4-bit GPTQ quantization and dynamic NTK RotaryEmbedding. These techniques allow the model to balance latency, accuracy, and memory efficiency, making it a versatile tool for a variety of natural language processing tasks. You could experiment with FalconLite by trying different prompts and evaluating its performance on tasks like question-answering or summarization. Additionally, you could explore how the model's quantization and specialized embedding techniques impact its behavior and outputs compared to other language models.
Updated 5/28/2024
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chronos-t5-large
77
The chronos-t5-large model is a time series forecasting model from Amazon that is based on the T5 architecture. Like other Chronos models, it transforms time series data into sequences of tokens using scaling and quantization, and then trains a language model on these tokens to learn patterns and generate future forecasts. The chronos-t5-large model has 710M parameters, making it the largest in the Chronos family, which also includes smaller variants like chronos-t5-tiny, chronos-t5-mini, and chronos-t5-base. Chronos models are similar to other text-to-text transformer models like CodeT5-large and the original T5-large in their use of a unified text-to-text format and encoder-decoder architecture. However, Chronos is specifically designed and trained for time series forecasting tasks, while CodeT5 and T5 are more general-purpose language models. Model inputs and outputs Inputs Time series data**: The Chronos-T5 models accept sequences of numerical time series values as input, which are then transformed into token sequences for modeling. Outputs Probabilistic forecasts**: The models generate future trajectories of the time series by autoregressively sampling tokens from the trained language model. This results in a predictive distribution over future values rather than a single point forecast. Capabilities The chronos-t5-large model and other Chronos variants have demonstrated strong performance on a variety of time series forecasting tasks, including datasets covering domains like finance, energy, and weather. By leveraging the large-scale T5 architecture, the models are able to capture complex patterns in the training data and generalize well to new time series. Additionally, the probabilistic nature of the outputs allows the models to capture uncertainty, which can be valuable in real-world forecasting applications. What can I use it for? The chronos-t5-large model and other Chronos variants can be used for a wide range of time series forecasting use cases, such as: Financial forecasting**: Predicting stock prices, exchange rates, or other financial time series Energy demand forecasting**: Forecasting electricity or fuel consumption for grid operators or energy companies Demand planning**: Forecasting product demand to optimize inventory and supply chain management Weather and climate forecasting**: Predicting weather patterns, temperature, precipitation, and other climate-related variables To use the Chronos models, you can follow the example provided in the companion repository, which demonstrates how to load the model, preprocess your data, and generate forecasts. Things to try One key capability of the Chronos models is their ability to handle a wide range of time series data, from financial metrics to weather measurements. Try experimenting with different types of time series data to see how the model performs. You can also explore the impact of different preprocessing steps, such as scaling, quantization, and time series transformation, on the model's forecasting accuracy. Another interesting aspect of the Chronos models is their probabilistic nature, which allows them to capture uncertainty in their forecasts. Try analyzing the predicted probability distributions and how they change based on the input data or model configuration. This information can be valuable for decision-making in real-world applications.
Updated 6/13/2024
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LightGPT
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LightGPT is a language model based on the GPT-J 6B model, which was instruction fine-tuned on the high-quality, Apache-2.0 licensed OIG-small-chip2 instruction dataset. It was developed by contributors at AWS. Compared to similar models like instruct-gpt-j-fp16 and mpt-30b-instruct, LightGPT was trained on a smaller but high-quality dataset, allowing for a more focused and specialized model. Model inputs and outputs LightGPT is a text-to-text model that can be used for a variety of natural language tasks. It takes in an instruction prompt and generates a relevant response. Inputs Instruction prompts**: LightGPT expects input prompts to be formatted with an instruction template, starting with "Below is an instruction that describes a task. Write a response that appropriately completes the request." followed by the specific instruction. Outputs Generated text**: LightGPT will generate a relevant response to complete the provided instruction. The output is open-ended and can vary in length depending on the complexity of the task. Capabilities LightGPT demonstrates strong performance on a wide range of instruction-following tasks, from answering questions to generating creative content. For example, when prompted to "Write a poem about cats", the model produced a thoughtful, multi-paragraph poem highlighting the unique characteristics of cats. What can I use it for? Given its strong performance on instructional tasks, LightGPT could be useful for a variety of applications that require natural language understanding and generation, such as: Content creation**: Generating engaging and informative articles, stories, or other text-based content based on provided guidelines. Customer service**: Handling basic customer inquiries and requests through a conversational interface. Task assistance**: Helping users complete various tasks by providing step-by-step guidance and relevant information. You can deploy LightGPT to Amazon SageMaker following the deployment instructions provided. Things to try One interesting aspect of LightGPT is its ability to handle complex, multi-part instructions. For example, you could prompt the model with a task like "Write a 5 paragraph essay on the benefits of renewable energy, including an introduction, three body paragraphs, and a conclusion." The model would then generate a cohesive, structured response addressing all elements of the instruction.
Updated 5/19/2024
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chronos-t5-tiny
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chronos-t5-tiny is a family of pretrained time series forecasting models developed by Amazon based on the language model architecture of T5. These models transform a time series into a sequence of tokens using scaling and quantization, and then train a language model on these tokens using a cross-entropy loss. During inference, the model can autoregressively sample future trajectories to generate probabilistic forecasts. The chronos-t5-tiny model in particular has 8M parameters and is based on the t5-efficient-tiny architecture. This smaller model size allows for fast inference on a single GPU or even a laptop, while still achieving strong forecasting performance. Compared to similar time series models like granite-timeseries-ttm-v1 from IBM and chronos-hermes-13b, the chronos-t5-tiny model has a more compact architecture focused specifically on time series forecasting. It also benefits from being part of the broader Chronos family of models, which have been trained on a large corpus of time series data. Model inputs and outputs Inputs Time series data**: The model takes in a time series, which is transformed into a sequence of tokens through scaling and quantization. Outputs Probabilistic forecasts**: The model outputs probabilistic forecasts, where it autoregressively samples multiple future trajectories given the historical context. Capabilities The chronos-t5-tiny model is capable of producing accurate probabilistic forecasts for a variety of time series datasets, including those related to electricity demand, weather, and solar/wind power generation. It achieves state-of-the-art zero-shot forecasting performance, and can be further fine-tuned on a small amount of target data to improve accuracy. The compact size and fast inference speed of the model make it well-suited for real-world applications where resource constraints are a concern. What can I use it for? The chronos-t5-tiny model can be used for a wide range of time series forecasting applications, such as: Forecasting energy consumption or generation for smart grid and renewable energy applications Predicting demand for products or services to improve inventory management and supply chain optimization Forecasting financial time series like stock prices or cryptocurrency values Predicting weather patterns and conditions for weather-sensitive industries The model's ability to provide probabilistic forecasts can also be useful for risk assessment and decision-making in these types of applications. Things to try One interesting aspect of the chronos-t5-tiny model is its use of a language model architecture for time series forecasting. This allows the model to leverage the powerful capabilities of transformers, such as capturing long-range dependencies and contextual information, which can be valuable for accurate forecasting. Researchers and practitioners may want to explore how this architecture compares to more traditional time series models, and investigate ways to further improve the model's performance through novel training techniques or architectural modifications. Additionally, the compact size of the chronos-t5-tiny model opens up opportunities for deploying the model in resource-constrained environments, such as edge devices or mobile applications. Exploring efficient deployment strategies and benchmarking the model's performance in these real-world scenarios could lead to impactful applications of this technology.
Updated 9/16/2024
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FalconLite2
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FalconLite2 is a fine-tuned and quantized version of the Falcon 40B language model. It can process up to 24,000 token input sequences, leveraging 4-bit GPTQ quantization and adapted RotaryEmbedding. This allows FalconLite2 to consume 4 times less GPU memory than the original Falcon 40B model while still processing 10 times longer contexts. FalconLite2 evolves from the previous FalconLite model, with improvements to its fine-tuning and quantization. Model Inputs and Outputs Inputs Long input sequences up to 24,000 tokens Outputs Processed and generated text based on the input Capabilities FalconLite2 is capable of handling much longer input contexts than the original Falcon 40B model. This makes it well-suited for applications that require processing and understanding of long-form content, such as topic retrieval, summarization, and question-answering. What can I use it for? The ability to process longer input sequences with lower memory usage makes FalconLite2 a good choice for deployment in resource-constrained environments, such as on a single AWS g5.12x instance. It can be used for applications that need to work with long-form content, like content search and retrieval, summarization of lengthy documents, and question-answering on complex topics. Things to Try You can experiment with FalconLite2 on tasks that require understanding and generating text from long input sequences, such as extracting key information from lengthy reports or articles, generating comprehensive summaries of complex topics, or building question-answering systems that can handle in-depth queries. The model's improved efficiency compared to the original Falcon 40B makes it an interesting option to explore for these types of applications.
Updated 9/6/2024