Getting started with Amazon SageMaker JumpStart
Overview
Amazon SageMaker JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. Explore how you can get started with built-in algorithms with pretrained models from model hubs, pretrained foundation models, and prebuilt solutions to solve common use cases. To get started, see documentation or example notebooks that you can quickly execute.
Total results: 630
- Popularity
- Featured First
- A-Z Model Name
- Z-A Model Name
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foundation model
Text GenerationFalcon 7B Instruct BF16
Hugging FaceFalcon-7B-Instruct is a 7B parameters causal decoder-only model built by TII based on Falcon-7B and finetuned on a mixture of chat/instruct datasets. It is made available under the Apache 2.0 license.Fine-tunable -
foundation model
Text GenerationFalcon 40B Instruct BF16
Hugging FaceFalcon-40B-Instruct is a 40B parameters causal decoder-only model built by TII based on Falcon-40B and finetuned on a mixture of Baize. It is made available under the Apache 2.0 license.Fine-tunable -
foundation model
Text GenerationBloomZ 7B1 FP16
Hugging FaceBloomZ 7b1 is an instruction-tuned model based on Bloom 7b1 and thus capable of performing various zero-shot natural language processing tasks, as well as the few-shot in-context learning tasks. With appropriate prompt, it can perform zero-shot NLP tasks such as text summarization, common sense reasoning, natural language inference, question and answering, sentence/sentiment classification, translation, and pronoun resolution.Fine-tunable -
foundation model
Text GenerationFalcon 7B BF16
Hugging FaceFalcon-7B is a 7B parameters causal decoder-only model built by TII and trained on 1,500B tokens of RefinedWeb enhanced with curated corpora. It is made available under the Apache 2.0 license.Fine-tunable -
foundation model
Text GenerationGPT NeoXT Chat Base 20B FP16
Hugging FaceAs part of OpenChatKit, GPT-NeoXT-Chat-Base-20B-v0.16 is a 20B parameter language model, fine-tuned from EleutherAI's GPT-NeoX with over 40 million instructions on 100% carbon negative compute.Deploy only -
foundation model
FeaturedText ClassificationMeta Llama Prompt Guard 86M
MetaPrompt Guard is a classifier model trained on a large corpus of attacks, capable of detecting both explicitly malicious prompts as well as data that contains injected inputs.Deploy only -
foundation model
Text GenerationFalcon 40B BF16
Hugging FaceFalcon-40B is a 40B parameters causal decoder-only model built by TII and trained on 1,000B tokens of RefinedWeb enhanced with curated corpora. It is made available under the Apache 2.0 license.Fine-tunable -
foundation model
Text GenerationFalcon2-11B
Hugging FaceFalcon2-11B is a 11B parameters causal decoder-only model built by TII and trained on over 5,000B tokens of RefinedWeb enhanced with curated corpora.Deploy only -
foundation model
FeaturedImage to TextEXAONE Atelier - Image to Text
LG AI ResearchEXAONE Atelier Image to Text model is a zero-shot image captioning model that is trained with 3.5 million images and text data, built upon LG AI Research's commercially licensed datasets.
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foundation model
FeaturedText GenerationLlama 3
MetaLlama 3 from Meta. Llama 3 family comes as 8B and 70B with pretrained and instruct versions. Available in us-east-1 (N. Virginia), us-east-2 (Ohio), us-west-2 (Oregon), eu-west-1 (Ireland) and ap-northeast-1 (Tokyo).
Deploy Only -
foundation model
FeaturedText GenerationLlama 2 70B
Meta70B variant of Llama 2 models. Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. Llama 2 is intended for commercial and research use in English. It comes in a range of parameter sizes—7 billion, 13 billion, and 70 billion—as well as pre-trained and fine-tuned variations.Fine-tunable -
foundation model
FeaturedText GenerationLlama 2 7B
Meta7B variant of Llama 2 models. Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. Llama 2 is intended for commercial and research use in English. It comes in a range of parameter sizes—7 billion, 13 billion, and 70 billion—as well as pre-trained and fine-tuned variations.Fine-tunable