hfModelId large_stringlengths 11 68 | smModelId large_stringlengths 15 86 ⌀ | serverlessCustomizationSupport bool 2
classes | serverfulTrainingSupport bool 2
classes | deployAvailability bool 1
class | trainingAvailability bool 2
classes | hasMultipleVariants bool 2
classes | endpointCode large_stringlengths 380 1.25k ⌀ | endpointLinks large_stringclasses 11
values | training_hfCode large_stringclasses 43
values | training_hfLinks large_stringclasses 3
values | deployRedirectionLink large_stringlengths 153 272 ⌀ | customizeRedirectionLink large_stringlengths 206 271 ⌀ | dpoCode large_stringclasses 28
values | dpoLabel large_stringclasses 1
value | dpoLinks large_stringclasses 1
value | rlaifCode large_stringclasses 30
values | rlaifLabel large_stringclasses 1
value | rlaifLinks large_stringclasses 1
value | rlvrCode large_stringclasses 30
values | rlvrLabel large_stringclasses 1
value | rlvrLinks large_stringclasses 1
value | sftCode large_stringclasses 30
values | sftLabel large_stringclasses 1
value | sftLinks large_stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
google/gemma-4-31B-it | huggingface-vlm-gemma-4-31b-it | true | false | true | true | false | # Deploy a JumpStart model using the SageMaker Python SDK v3.
from sagemaker.serve.model_builder import ModelBuilder
from sagemaker.core.jumpstart.configs import JumpStartConfig
from sagemaker.core.training.configs import Compute
model_builder = ModelBuilder.from_jumpstart_config(
jumpstart_config=JumpStartConfig... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=google%2Fgemma-4-31b-it&smModelId=huggingface-vlm-gemma-4-31b-it&hasVariants=false&page=deploy | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=google%2Fgemma-4-31b-it&smModelId=huggingface-vlm-gemma-4-31b-it&supportServerless=true&supportServerful=false&hasVariants=false&page=customize | # Fine-tune with Direct Preference Optimization (DPO) using the SageMaker Python SDK v3.
from sagemaker.train.dpo_trainer import DPOTrainer
from sagemaker.train.common import TrainingType
from sagemaker.ai_registry.dataset import DataSet
# Dataset format: JSONL with {"prompt": [...messages...], "chosen": "...", "reje... | Direct Preference Optimization (DPO) | [] | # Fine-tune with RL from AI Feedback (RLAIF) using the SageMaker Python SDK v3.
from sagemaker.train.rlaif_trainer import RLAIFTrainer
from sagemaker.train.common import TrainingType
from sagemaker.ai_registry.dataset import DataSet
# Dataset format: JSONL with {"prompt": [...messages...], "reward_model": {"style": "... | RL from AI Feedback (RLAIF) | [] | # Fine-tune with RL with Verifiable Rewards (RLVR) using the SageMaker Python SDK v3.
from sagemaker.train.rlvr_trainer import RLVRTrainer
from sagemaker.train.common import TrainingType
from sagemaker.ai_registry.dataset import DataSet
# Dataset format: JSONL with {"prompt": [...messages...], "reward_model": {"groun... | RL with Verifiable Rewards (RLVR) | [] | # Fine-tune with Supervised Fine-Tuning (SFT) using the SageMaker Python SDK v3.
from sagemaker.train.sft_trainer import SFTTrainer
from sagemaker.train.common import TrainingType
from sagemaker.ai_registry.dataset import DataSet
# Dataset format: JSONL with {"prompt": "...", "completion": "..."} per line
# Download ... | Supervised Fine-Tuning (SFT) | [] |
google/gemma-4-E2B-it | huggingface-vlm-gemma-4-e2b-instruct | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model_id = "huggingface-vlm-gemma-4-e2b-instruct"
model = JumpStartModel(model_id=model_id)
pay... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=google%2Fgemma-4-e2b-it&smModelId=huggingface-vlm-gemma-4-e2b-instruct&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
google/gemma-4-E4B-it | huggingface-vlm-gemma-4-e4b-it | true | false | true | true | false | # Deploy a JumpStart model using the SageMaker Python SDK v3.
from sagemaker.serve.model_builder import ModelBuilder
from sagemaker.core.jumpstart.configs import JumpStartConfig
from sagemaker.core.training.configs import Compute
model_builder = ModelBuilder.from_jumpstart_config(
jumpstart_config=JumpStartConfig... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=google%2Fgemma-4-e4b-it&smModelId=huggingface-vlm-gemma-4-e4b-it&hasVariants=false&page=deploy | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=google%2Fgemma-4-e4b-it&smModelId=huggingface-vlm-gemma-4-e4b-it&supportServerless=true&supportServerful=false&hasVariants=false&page=customize | # Fine-tune with Direct Preference Optimization (DPO) using the SageMaker Python SDK v3.
from sagemaker.train.dpo_trainer import DPOTrainer
from sagemaker.train.common import TrainingType
from sagemaker.ai_registry.dataset import DataSet
# Dataset format: JSONL with {"prompt": [...messages...], "chosen": "...", "reje... | Direct Preference Optimization (DPO) | [] | # Fine-tune with RL from AI Feedback (RLAIF) using the SageMaker Python SDK v3.
from sagemaker.train.rlaif_trainer import RLAIFTrainer
from sagemaker.train.common import TrainingType
from sagemaker.ai_registry.dataset import DataSet
# Dataset format: JSONL with {"prompt": [...messages...], "reward_model": {"style": "... | RL from AI Feedback (RLAIF) | [] | # Fine-tune with RL with Verifiable Rewards (RLVR) using the SageMaker Python SDK v3.
from sagemaker.train.rlvr_trainer import RLVRTrainer
from sagemaker.train.common import TrainingType
from sagemaker.ai_registry.dataset import DataSet
# Dataset format: JSONL with {"prompt": [...messages...], "reward_model": {"groun... | RL with Verifiable Rewards (RLVR) | [] | # Fine-tune with Supervised Fine-Tuning (SFT) using the SageMaker Python SDK v3.
from sagemaker.train.sft_trainer import SFTTrainer
from sagemaker.train.common import TrainingType
from sagemaker.ai_registry.dataset import DataSet
# Dataset format: JSONL with {"prompt": "...", "completion": "..."} per line
# Download ... | Supervised Fine-Tuning (SFT) | [] |
google/gemma-7b | huggingface-llm-gemma-7b | false | true | true | true | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="huggingface-llm-gemma-7b")
example_payloads = model.retrieve_al... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | # Fine-tune a gated Hugging Face Hub model using the SageMaker Python SDK v3.
# This model requires a Hugging Face token to download weights.
# See https://github.com/huggingface/transformers/tree/v4.56.2/examples/pytorch
import boto3
from sagemaker.train.model_trainer import ModelTrainer
from sagemaker.core.training.... | [] | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=google%2Fgemma-7b&smModelId=huggingface-llm-gemma-7b&hasVariants=false&page=deploy | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=google%2Fgemma-7b&smModelId=huggingface-llm-gemma-7b&supportServerless=false&supportServerful=true&hasVariants=false&page=customize | null | null | null | null | null | null | null | null | null | null | null | null |
google/gemma-7b-it | huggingface-llm-gemma-7b-instruct | false | true | true | true | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="huggingface-llm-gemma-7b-instruct")
example_payloads = model.re... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | # Fine-tune a gated Hugging Face Hub model using the SageMaker Python SDK v3.
# This model requires a Hugging Face token to download weights.
# See https://github.com/huggingface/transformers/tree/v4.56.2/examples/pytorch
import boto3
from sagemaker.train.model_trainer import ModelTrainer
from sagemaker.core.training.... | [] | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=google%2Fgemma-7b-it&smModelId=huggingface-llm-gemma-7b-instruct&hasVariants=false&page=deploy | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=google%2Fgemma-7b-it&smModelId=huggingface-llm-gemma-7b-instruct&supportServerless=false&supportServerful=true&hasVariants=false&page=customize | null | null | null | null | null | null | null | null | null | null | null | null |
gradientai/Llama-3-8B-Instruct-262k | huggingface-llm-gradientai-llama-3-8B-instruct-262k | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="huggingface-llm-gradientai-llama-3-8B-instruct-262k")
example_p... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=gradientai%2Fllama-3-8b-instruct-262k&smModelId=huggingface-llm-gradientai-llama-3-8B-instruct-262k&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
gradientai/Llama-3-8B-Instruct-Gradient-1048k | huggingface-llm-llama-3-8b-instruct-gradient | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="huggingface-llm-llama-3-8b-instruct-gradient")
example_payloads... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=gradientai%2Fllama-3-8b-instruct-gradient-1048k&smModelId=huggingface-llm-llama-3-8b-instruct-gradient&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
ibm-granite/granite-speech-4.1-2b | huggingface-asr-ibm-granite-granite-speech-4-1-2b | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="huggingface-asr-ibm-granite-granite-speech-4-1-2b")
example_pay... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=ibm-granite%2Fgranite-speech-4.1-2b&smModelId=huggingface-asr-ibm-granite-granite-speech-4-1-2b&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
ibm-research/qcpg-sentences | huggingface-text2text-qcpg-sentences | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="huggingface-text2text-qcpg-sentences")
example_payloads = model... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=ibm-research%2Fqcpg-sentences&smModelId=huggingface-text2text-qcpg-sentences&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
intfloat/e5-base | huggingface-sentencesimilarity-e5-base | false | true | true | true | false | from sagemaker.jumpstart.model import JumpStartModel
import json
model_id = "huggingface-sentencesimilarity-e5-base"
endpoint_input = json.dumps(["How cute your dog is!", "Your dog is so cute."])
model = JumpStartModel(model_id=model_id)
predictor = model.deploy()
response = predictor.predict(endpoint_input)
print(f"... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=intfloat%2Fe5-base&smModelId=huggingface-sentencesimilarity-e5-base&hasVariants=false&page=deploy | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=intfloat%2Fe5-base&smModelId=huggingface-sentencesimilarity-e5-base&supportServerless=false&supportServerful=true&hasVariants=false&page=customize | null | null | null | null | null | null | null | null | null | null | null | null |
intfloat/e5-base-v2 | huggingface-sentencesimilarity-e5-base-v2 | false | true | true | true | false | from sagemaker.jumpstart.model import JumpStartModel
import json
model_id = "huggingface-sentencesimilarity-e5-base-v2"
endpoint_input = json.dumps(["How cute your dog is!", "Your dog is so cute."])
model = JumpStartModel(model_id=model_id)
predictor = model.deploy()
response = predictor.predict(endpoint_input)
print... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=intfloat%2Fe5-base-v2&smModelId=huggingface-sentencesimilarity-e5-base-v2&hasVariants=false&page=deploy | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=intfloat%2Fe5-base-v2&smModelId=huggingface-sentencesimilarity-e5-base-v2&supportServerless=false&supportServerful=true&hasVariants=false&page=customize | null | null | null | null | null | null | null | null | null | null | null | null |
intfloat/e5-large-v2 | huggingface-sentencesimilarity-e5-large-v2 | false | true | true | true | false | from sagemaker.jumpstart.model import JumpStartModel
import json
model_id = "huggingface-sentencesimilarity-e5-large-v2"
endpoint_input = json.dumps(["How cute your dog is!", "Your dog is so cute."])
model = JumpStartModel(model_id=model_id)
predictor = model.deploy()
response = predictor.predict(endpoint_input)
prin... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=intfloat%2Fe5-large-v2&smModelId=huggingface-sentencesimilarity-e5-large-v2&hasVariants=false&page=deploy | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=intfloat%2Fe5-large-v2&smModelId=huggingface-sentencesimilarity-e5-large-v2&supportServerless=false&supportServerful=true&hasVariants=false&page=customize | null | null | null | null | null | null | null | null | null | null | null | null |
intfloat/multilingual-e5-base | huggingface-sentencesimilarity-multilingual-e5-base | false | true | true | true | false | from sagemaker.jumpstart.model import JumpStartModel
import json
model_id = "huggingface-sentencesimilarity-multilingual-e5-base"
endpoint_input = json.dumps(["How cute your dog is!", "Your dog is so cute."])
model = JumpStartModel(model_id=model_id)
predictor = model.deploy()
response = predictor.predict(endpoint_in... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=intfloat%2Fmultilingual-e5-base&smModelId=huggingface-sentencesimilarity-multilingual-e5-base&hasVariants=false&page=deploy | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=intfloat%2Fmultilingual-e5-base&smModelId=huggingface-sentencesimilarity-multilingual-e5-base&supportServerless=false&supportServerful=true&hasVariants=false&page=customize | null | null | null | null | null | null | null | null | null | null | null | null |
intfloat/multilingual-e5-large | huggingface-sentencesimilarity-multilingual-e5-large | false | true | true | true | false | from sagemaker.jumpstart.model import JumpStartModel
import json
model_id = "huggingface-sentencesimilarity-multilingual-e5-large"
endpoint_input = json.dumps(["How cute your dog is!", "Your dog is so cute."])
model = JumpStartModel(model_id=model_id)
predictor = model.deploy()
response = predictor.predict(endpoint_i... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=intfloat%2Fmultilingual-e5-large&smModelId=huggingface-sentencesimilarity-multilingual-e5-large&hasVariants=false&page=deploy | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=intfloat%2Fmultilingual-e5-large&smModelId=huggingface-sentencesimilarity-multilingual-e5-large&supportServerless=false&supportServerful=true&hasVariants=false&page=customize | null | null | null | null | null | null | null | null | null | null | null | null |
kakaocorp/kanana-2-30b-a3b-instruct-2601 | huggingface-reasoning-kanana-2-30b-a3b-instruct-2601 | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="huggingface-reasoning-kanana-2-30b-a3b-instruct-2601")
example_... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=kakaocorp%2Fkanana-2-30b-a3b-instruct-2601&smModelId=huggingface-reasoning-kanana-2-30b-a3b-instruct-2601&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
kogai/laneformer-2b-it | huggingface-llm-kogai-laneformer-2b-it | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="huggingface-llm-kogai-laneformer-2b-it")
example_payloads = mod... | [{"label": "Large Language Model (LLM): Run Inference in Amazon SageMaker JumpStart", "href": "https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-falcon.ipynb"}, {"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/... | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=kogai%2Flaneformer-2b-it&smModelId=huggingface-llm-kogai-laneformer-2b-it&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
lighteternal/nli-xlm-r-greek | huggingface-zstc-lighteternal-nli-xlm-r-greek | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model_id = "huggingface-zstc-lighteternal-nli-xlm-r-greek"
endpoint_input = {'sequences': 'one d... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=lighteternal%2Fnli-xlm-r-greek&smModelId=huggingface-zstc-lighteternal-nli-xlm-r-greek&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
lightonai/LightOnOCR-2-1B | huggingface-vlm-lightonai-lightonocr-2-1b | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model_id = "huggingface-vlm-lightonai-lightonocr-2-1b"
model = JumpStartModel(model_id=model_id... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=lightonai%2Flightonocr-2-1b&smModelId=huggingface-vlm-lightonai-lightonocr-2-1b&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
meta-llama/Llama-2-13b-chat-hf | meta-textgeneration-llama-2-13b-f | false | true | true | true | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="meta-textgeneration-llama-2-13b-f")
example_payloads = model.re... | [{"label": "Large Language Model (LLM): Run Inference in Amazon SageMaker JumpStart", "href": "https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-chatbot.ipynb"}, {"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com... | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-2-13b-chat-hf&smModelId=meta-textgeneration-llama-2-13b-f&hasVariants=false&page=deploy | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-2-13b-chat-hf&smModelId=meta-textgeneration-llama-2-13b-f&supportServerless=false&supportServerful=true&hasVariants=false&page=customize | null | null | null | null | null | null | null | null | null | null | null | null |
meta-llama/Llama-2-13b-hf | meta-textgeneration-llama-2-13b | false | true | true | true | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="meta-textgeneration-llama-2-13b")
example_payloads = model.retr... | [{"label": "Large Language Model (LLM): Run Inference in Amazon SageMaker JumpStart", "href": "https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart-foundation-models/llama-2-text-completion.ipynb"}, {"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com... | # Fine-tune a gated Hugging Face Hub model using the SageMaker Python SDK v3.
# This model requires a Hugging Face token to download weights.
# See https://github.com/huggingface/transformers/tree/v4.56.2/examples/pytorch
import boto3
from sagemaker.train.model_trainer import ModelTrainer
from sagemaker.core.training.... | [] | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-2-13b-hf&smModelId=meta-textgeneration-llama-2-13b&hasVariants=false&page=deploy | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-2-13b-hf&smModelId=meta-textgeneration-llama-2-13b&supportServerless=false&supportServerful=true&hasVariants=false&page=customize | null | null | null | null | null | null | null | null | null | null | null | null |
meta-llama/Llama-2-70b-chat-hf | meta-textgenerationneuron-llama-2-70b-f | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="meta-textgeneration-llama-2-70b-f")
example_payloads = model.re... | [{"href": "https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-chatbot.ipynb", "label": "Large Language Model (LLM): Run Inference in Amazon SageMaker JumpStart"}, {"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon... | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-2-70b-chat-hf&smModelId=meta-textgenerationneuron-llama-2-70b-f&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
meta-llama/Llama-2-70b-hf | meta-textgenerationneuron-llama-2-70b | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="meta-textgeneration-llama-2-70b")
example_payloads = model.retr... | [{"label": "Large Language Model (LLM): Run Inference in Amazon SageMaker JumpStart", "href": "https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart-foundation-models/llama-2-text-completion.ipynb"}, {"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com... | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-2-70b-hf&smModelId=meta-textgenerationneuron-llama-2-70b&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
meta-llama/Llama-2-7b-chat-hf | meta-textgeneration-llama-2-7b-f | false | true | true | true | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="meta-textgeneration-llama-2-7b-f")
example_payloads = model.ret... | [{"label": "Large Language Model (LLM): Run Inference in Amazon SageMaker JumpStart", "href": "https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-chatbot.ipynb"}, {"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com... | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-2-7b-chat-hf&smModelId=meta-textgeneration-llama-2-7b-f&hasVariants=false&page=deploy | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-2-7b-chat-hf&smModelId=meta-textgeneration-llama-2-7b-f&supportServerless=false&supportServerful=true&hasVariants=false&page=customize | null | null | null | null | null | null | null | null | null | null | null | null |
meta-llama/Llama-2-7b-hf | meta-textgeneration-llama-2-7b | false | true | true | true | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="meta-textgeneration-llama-2-7b")
example_payloads = model.retri... | [{"label": "Large Language Model (LLM): Run Inference in Amazon SageMaker JumpStart", "href": "https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart-foundation-models/llama-2-text-completion.ipynb"}, {"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com... | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-2-7b-hf&smModelId=meta-textgeneration-llama-2-7b&hasVariants=false&page=deploy | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-2-7b-hf&smModelId=meta-textgeneration-llama-2-7b&supportServerless=false&supportServerful=true&hasVariants=false&page=customize | null | null | null | null | null | null | null | null | null | null | null | null |
meta-llama/Llama-3.1-405B-FP8 | meta-textgeneration-llama-3-1-405b-fp8 | false | true | true | true | false | # Deploy a gated JumpStart model using the SageMaker Python SDK v3.
# This model requires accepting the end-user license agreement (EULA).
from sagemaker.serve.model_builder import ModelBuilder
from sagemaker.core.jumpstart.configs import JumpStartConfig
from sagemaker.core.training.configs import Compute
# You must ... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | # Fine-tune a gated Hugging Face Hub model using the SageMaker Python SDK v3.
# This model requires a Hugging Face token to download weights.
# See https://github.com/huggingface/transformers/tree/v4.56.2/examples/pytorch
import boto3
from sagemaker.train.model_trainer import ModelTrainer
from sagemaker.core.training.... | [] | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-3.1-405b-fp8&smModelId=meta-textgeneration-llama-3-1-405b-fp8&hasVariants=false&page=deploy | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-3.1-405b-fp8&smModelId=meta-textgeneration-llama-3-1-405b-fp8&supportServerless=false&supportServerful=true&hasVariants=false&page=customize | null | null | null | null | null | null | null | null | null | null | null | null |
meta-llama/Llama-3.1-405B-Instruct-FP8 | meta-textgeneration-llama-3-1-405b-instruct-fp8 | false | true | true | true | false | # Deploy a gated JumpStart model using the SageMaker Python SDK v3.
# This model requires accepting the end-user license agreement (EULA).
from sagemaker.serve.model_builder import ModelBuilder
from sagemaker.core.jumpstart.configs import JumpStartConfig
from sagemaker.core.training.configs import Compute
# You must ... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | # Fine-tune a gated Hugging Face Hub model using the SageMaker Python SDK v3.
# This model requires a Hugging Face token to download weights.
# See https://github.com/huggingface/transformers/tree/v4.56.2/examples/pytorch
import boto3
from sagemaker.train.model_trainer import ModelTrainer
from sagemaker.core.training.... | [] | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-3.1-405b-instruct-fp8&smModelId=meta-textgeneration-llama-3-1-405b-instruct-fp8&hasVariants=false&page=deploy | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-3.1-405b-instruct-fp8&smModelId=meta-textgeneration-llama-3-1-405b-instruct-fp8&supportServerless=false&supportServerful=true&hasVariants=false&page=customize | null | null | null | null | null | null | null | null | null | null | null | null |
meta-llama/Llama-3.1-70B | meta-textgeneration-llama-3-1-70b | false | true | true | true | false | # Deploy a gated JumpStart model using the SageMaker Python SDK v3.
# This model requires accepting the end-user license agreement (EULA).
from sagemaker.serve.model_builder import ModelBuilder
from sagemaker.core.jumpstart.configs import JumpStartConfig
from sagemaker.core.training.configs import Compute
# You must ... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | # Fine-tune a gated Hugging Face Hub model using the SageMaker Python SDK v3.
# This model requires a Hugging Face token to download weights.
# See https://github.com/huggingface/transformers/tree/v4.56.2/examples/pytorch
import boto3
from sagemaker.train.model_trainer import ModelTrainer
from sagemaker.core.training.... | [] | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-3.1-70b&smModelId=meta-textgeneration-llama-3-1-70b&hasVariants=false&page=deploy | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-3.1-70b&smModelId=meta-textgeneration-llama-3-1-70b&supportServerless=false&supportServerful=true&hasVariants=false&page=customize | null | null | null | null | null | null | null | null | null | null | null | null |
meta-llama/Llama-3.1-70B-Instruct | meta-textgeneration-llama-3-1-70b-instruct | false | true | true | true | false | # Deploy a gated JumpStart model using the SageMaker Python SDK v3.
# This model requires accepting the end-user license agreement (EULA).
from sagemaker.serve.model_builder import ModelBuilder
from sagemaker.core.jumpstart.configs import JumpStartConfig
from sagemaker.core.training.configs import Compute
# You must ... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | # Fine-tune a gated Hugging Face Hub model using the SageMaker Python SDK v3.
# This model requires a Hugging Face token to download weights.
# See https://github.com/huggingface/transformers/tree/v4.56.2/examples/pytorch
import boto3
from sagemaker.train.model_trainer import ModelTrainer
from sagemaker.core.training.... | [] | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-3.1-70b-instruct&smModelId=meta-textgeneration-llama-3-1-70b-instruct&hasVariants=false&page=deploy | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-3.1-70b-instruct&smModelId=meta-textgeneration-llama-3-1-70b-instruct&supportServerless=false&supportServerful=true&hasVariants=false&page=customize | null | null | null | null | null | null | null | null | null | null | null | null |
meta-llama/Llama-3.1-8B | meta-textgeneration-llama-3-1-8b | false | true | true | true | false | # Deploy a gated JumpStart model using the SageMaker Python SDK v3.
# This model requires accepting the end-user license agreement (EULA).
from sagemaker.serve.model_builder import ModelBuilder
from sagemaker.core.jumpstart.configs import JumpStartConfig
from sagemaker.core.training.configs import Compute
# You must ... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | # Fine-tune a gated Hugging Face Hub model using the SageMaker Python SDK v3.
# This model requires a Hugging Face token to download weights.
# See https://github.com/huggingface/transformers/tree/v4.56.2/examples/pytorch
import boto3
from sagemaker.train.model_trainer import ModelTrainer
from sagemaker.core.training.... | [] | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-3.1-8b&smModelId=meta-textgeneration-llama-3-1-8b&hasVariants=false&page=deploy | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-3.1-8b&smModelId=meta-textgeneration-llama-3-1-8b&supportServerless=false&supportServerful=true&hasVariants=false&page=customize | null | null | null | null | null | null | null | null | null | null | null | null |
meta-llama/Llama-3.1-8B-Instruct | meta-textgeneration-llama-3-1-8b-instruct | true | true | true | true | true | # Deploy a gated JumpStart model using the SageMaker Python SDK v3.
# This model requires accepting the end-user license agreement (EULA).
from sagemaker.serve.model_builder import ModelBuilder
from sagemaker.core.jumpstart.configs import JumpStartConfig
from sagemaker.core.training.configs import Compute
# You must ... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | # Fine-tune a gated Hugging Face Hub model using the SageMaker Python SDK v3.
# This model requires a Hugging Face token to download weights.
# See https://github.com/huggingface/transformers/tree/v4.56.2/examples/pytorch
import boto3
from sagemaker.train.model_trainer import ModelTrainer
from sagemaker.core.training.... | [] | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-3.1-8b-instruct&smModelId=meta-textgeneration-llama-3-1-8b-instruct&hasVariants=true&page=deploy | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-3.1-8b-instruct&smModelId=meta-textgeneration-llama-3-1-8b-instruct&supportServerless=true&supportServerful=true&hasVariants=true&page=customize | # Fine-tune with Direct Preference Optimization (DPO) using the SageMaker Python SDK v3.
from sagemaker.train.dpo_trainer import DPOTrainer
from sagemaker.train.common import TrainingType
from sagemaker.ai_registry.dataset import DataSet
# Dataset format: JSONL with {"prompt": [...messages...], "chosen": "...", "reje... | Direct Preference Optimization (DPO) | [] | # Fine-tune with RL from AI Feedback (RLAIF) using the SageMaker Python SDK v3.
from sagemaker.train.rlaif_trainer import RLAIFTrainer
from sagemaker.train.common import TrainingType
from sagemaker.ai_registry.dataset import DataSet
# Dataset format: JSONL with {"prompt": [...messages...], "reward_model": {"style": "... | RL from AI Feedback (RLAIF) | [] | # Fine-tune with RL with Verifiable Rewards (RLVR) using the SageMaker Python SDK v3.
from sagemaker.train.rlvr_trainer import RLVRTrainer
from sagemaker.train.common import TrainingType
from sagemaker.ai_registry.dataset import DataSet
# Dataset format: JSONL with {"prompt": [...messages...], "reward_model": {"groun... | RL with Verifiable Rewards (RLVR) | [] | # Fine-tune with Supervised Fine-Tuning (SFT) using the SageMaker Python SDK v3.
from sagemaker.train.sft_trainer import SFTTrainer
from sagemaker.train.common import TrainingType
from sagemaker.ai_registry.dataset import DataSet
# Dataset format: JSONL with {"prompt": "...", "completion": "..."} per line
# Download ... | Supervised Fine-Tuning (SFT) | [] |
meta-llama/Llama-3.2-11B-Vision | meta-vlm-llama-3-2-11b-vision | false | true | true | true | false | # Deploy a gated JumpStart model using the SageMaker Python SDK v3.
# This model requires accepting the end-user license agreement (EULA).
from sagemaker.serve.model_builder import ModelBuilder
from sagemaker.core.jumpstart.configs import JumpStartConfig
from sagemaker.core.training.configs import Compute
# You must ... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-3.2-11b-vision&smModelId=meta-vlm-llama-3-2-11b-vision&hasVariants=false&page=deploy | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-3.2-11b-vision&smModelId=meta-vlm-llama-3-2-11b-vision&supportServerless=false&supportServerful=true&hasVariants=false&page=customize | null | null | null | null | null | null | null | null | null | null | null | null |
meta-llama/Llama-3.2-11B-Vision-Instruct | meta-vlm-llama-3-2-11b-vision-instruct | false | true | true | true | false | # Deploy a gated JumpStart model using the SageMaker Python SDK v3.
# This model requires accepting the end-user license agreement (EULA).
from sagemaker.serve.model_builder import ModelBuilder
from sagemaker.core.jumpstart.configs import JumpStartConfig
from sagemaker.core.training.configs import Compute
# You must ... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-3.2-11b-vision-instruct&smModelId=meta-vlm-llama-3-2-11b-vision-instruct&hasVariants=false&page=deploy | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-3.2-11b-vision-instruct&smModelId=meta-vlm-llama-3-2-11b-vision-instruct&supportServerless=false&supportServerful=true&hasVariants=false&page=customize | null | null | null | null | null | null | null | null | null | null | null | null |
meta-llama/Llama-3.2-1B | meta-textgenerationneuron-llama-3-2-1b | false | false | true | false | false | null | null | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-3.2-1b&smModelId=meta-textgenerationneuron-llama-3-2-1b&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
meta-llama/Llama-3.2-1B-Instruct | meta-textgeneration-llama-3-2-1b-instruct | true | true | true | true | true | # Deploy a gated JumpStart model using the SageMaker Python SDK v3.
# This model requires accepting the end-user license agreement (EULA).
from sagemaker.serve.model_builder import ModelBuilder
from sagemaker.core.jumpstart.configs import JumpStartConfig
from sagemaker.core.training.configs import Compute
# You must ... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | # Fine-tune a gated Hugging Face Hub model using the SageMaker Python SDK v3.
# This model requires a Hugging Face token to download weights.
# See https://github.com/huggingface/transformers/tree/v4.56.2/examples/pytorch
import boto3
from sagemaker.train.model_trainer import ModelTrainer
from sagemaker.core.training.... | [] | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-3.2-1b-instruct&smModelId=meta-textgeneration-llama-3-2-1b-instruct&hasVariants=true&page=deploy | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-3.2-1b-instruct&smModelId=meta-textgeneration-llama-3-2-1b-instruct&supportServerless=true&supportServerful=true&hasVariants=true&page=customize | # Fine-tune with Direct Preference Optimization (DPO) using the SageMaker Python SDK v3.
from sagemaker.train.dpo_trainer import DPOTrainer
from sagemaker.train.common import TrainingType
from sagemaker.ai_registry.dataset import DataSet
# Dataset format: JSONL with {"prompt": [...messages...], "chosen": "...", "reje... | Direct Preference Optimization (DPO) | [] | # Fine-tune with RL from AI Feedback (RLAIF) using the SageMaker Python SDK v3.
from sagemaker.train.rlaif_trainer import RLAIFTrainer
from sagemaker.train.common import TrainingType
from sagemaker.ai_registry.dataset import DataSet
# Dataset format: JSONL with {"prompt": [...messages...], "reward_model": {"style": "... | RL from AI Feedback (RLAIF) | [] | # Fine-tune with RL with Verifiable Rewards (RLVR) using the SageMaker Python SDK v3.
from sagemaker.train.rlvr_trainer import RLVRTrainer
from sagemaker.train.common import TrainingType
from sagemaker.ai_registry.dataset import DataSet
# Dataset format: JSONL with {"prompt": [...messages...], "reward_model": {"groun... | RL with Verifiable Rewards (RLVR) | [] | # Fine-tune with Supervised Fine-Tuning (SFT) using the SageMaker Python SDK v3.
from sagemaker.train.sft_trainer import SFTTrainer
from sagemaker.train.common import TrainingType
from sagemaker.ai_registry.dataset import DataSet
# Dataset format: JSONL with {"prompt": "...", "completion": "..."} per line
# Download ... | Supervised Fine-Tuning (SFT) | [] |
meta-llama/Llama-3.2-3B | meta-textgenerationneuron-llama-3-2-3b | false | false | true | false | false | null | null | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-3.2-3b&smModelId=meta-textgenerationneuron-llama-3-2-3b&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
meta-llama/Llama-3.2-3B-Instruct | meta-textgeneration-llama-3-2-3b-instruct | true | true | true | true | true | # Deploy a gated JumpStart model using the SageMaker Python SDK v3.
# This model requires accepting the end-user license agreement (EULA).
from sagemaker.serve.model_builder import ModelBuilder
from sagemaker.core.jumpstart.configs import JumpStartConfig
from sagemaker.core.training.configs import Compute
# You must ... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | # Fine-tune a gated Hugging Face Hub model using the SageMaker Python SDK v3.
# This model requires a Hugging Face token to download weights.
# See https://github.com/huggingface/transformers/tree/v4.56.2/examples/pytorch
import boto3
from sagemaker.train.model_trainer import ModelTrainer
from sagemaker.core.training.... | [] | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-3.2-3b-instruct&smModelId=meta-textgeneration-llama-3-2-3b-instruct&hasVariants=true&page=deploy | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-3.2-3b-instruct&smModelId=meta-textgeneration-llama-3-2-3b-instruct&supportServerless=true&supportServerful=true&hasVariants=true&page=customize | # Fine-tune with Direct Preference Optimization (DPO) using the SageMaker Python SDK v3.
from sagemaker.train.dpo_trainer import DPOTrainer
from sagemaker.train.common import TrainingType
from sagemaker.ai_registry.dataset import DataSet
# Dataset format: JSONL with {"prompt": [...messages...], "chosen": "...", "reje... | Direct Preference Optimization (DPO) | [] | # Fine-tune with RL from AI Feedback (RLAIF) using the SageMaker Python SDK v3.
from sagemaker.train.rlaif_trainer import RLAIFTrainer
from sagemaker.train.common import TrainingType
from sagemaker.ai_registry.dataset import DataSet
# Dataset format: JSONL with {"prompt": [...messages...], "reward_model": {"style": "... | RL from AI Feedback (RLAIF) | [] | # Fine-tune with RL with Verifiable Rewards (RLVR) using the SageMaker Python SDK v3.
from sagemaker.train.rlvr_trainer import RLVRTrainer
from sagemaker.train.common import TrainingType
from sagemaker.ai_registry.dataset import DataSet
# Dataset format: JSONL with {"prompt": [...messages...], "reward_model": {"groun... | RL with Verifiable Rewards (RLVR) | [] | # Fine-tune with Supervised Fine-Tuning (SFT) using the SageMaker Python SDK v3.
from sagemaker.train.sft_trainer import SFTTrainer
from sagemaker.train.common import TrainingType
from sagemaker.ai_registry.dataset import DataSet
# Dataset format: JSONL with {"prompt": "...", "completion": "..."} per line
# Download ... | Supervised Fine-Tuning (SFT) | [] |
meta-llama/Llama-3.2-90B-Vision | meta-vlm-llama-3-2-90b-vision | false | true | true | true | false | # Deploy a gated JumpStart model using the SageMaker Python SDK v3.
# This model requires accepting the end-user license agreement (EULA).
from sagemaker.serve.model_builder import ModelBuilder
from sagemaker.core.jumpstart.configs import JumpStartConfig
from sagemaker.core.training.configs import Compute
# You must ... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-3.2-90b-vision&smModelId=meta-vlm-llama-3-2-90b-vision&hasVariants=false&page=deploy | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-3.2-90b-vision&smModelId=meta-vlm-llama-3-2-90b-vision&supportServerless=false&supportServerful=true&hasVariants=false&page=customize | null | null | null | null | null | null | null | null | null | null | null | null |
meta-llama/Llama-4-Maverick-17B-128E-Instruct | meta-vlm-llama-4-maverick-17b-128e-instruct | false | false | true | false | false | # Deploy a gated JumpStart model using the SageMaker Python SDK v3.
# This model requires accepting the end-user license agreement (EULA).
from sagemaker.serve.model_builder import ModelBuilder
from sagemaker.core.jumpstart.configs import JumpStartConfig
from sagemaker.core.training.configs import Compute
# You must ... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-4-maverick-17b-128e-instruct&smModelId=meta-vlm-llama-4-maverick-17b-128e-instruct&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 | meta-vlm-llama-4-maverick-17b-128e-instruct-fp8 | false | false | true | false | false | # Deploy a gated JumpStart model using the SageMaker Python SDK v3.
# This model requires accepting the end-user license agreement (EULA).
from sagemaker.serve.model_builder import ModelBuilder
from sagemaker.core.jumpstart.configs import JumpStartConfig
from sagemaker.core.training.configs import Compute
# You must ... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-4-maverick-17b-128e-instruct-fp8&smModelId=meta-vlm-llama-4-maverick-17b-128e-instruct-fp8&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
meta-llama/Llama-Guard-3-11B-Vision | meta-vlm-llama-guard-3-11b-vision | false | false | true | false | false | # Deploy a gated JumpStart model using the SageMaker Python SDK v3.
# This model requires accepting the end-user license agreement (EULA).
from sagemaker.serve.model_builder import ModelBuilder
from sagemaker.core.jumpstart.configs import JumpStartConfig
from sagemaker.core.training.configs import Compute
# You must ... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-guard-3-11b-vision&smModelId=meta-vlm-llama-guard-3-11b-vision&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
meta-llama/Llama-Guard-3-1B | meta-textgeneration-llama-guard-3-1b | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="meta-textgeneration-llama-guard-3-1b")
example_payloads = model... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-guard-3-1b&smModelId=meta-textgeneration-llama-guard-3-1b&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
meta-llama/Llama-Guard-3-8B | meta-textgenerationneuron-llama-guard-3-8b | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="meta-textgeneration-llama-guard-3-8b")
example_payloads = model... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllama-guard-3-8b&smModelId=meta-textgenerationneuron-llama-guard-3-8b&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
meta-llama/LlamaGuard-7b | meta-textgeneration-llama-guard-7b | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="meta-textgeneration-llama-guard-7b")
example_payloads = model.r... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fllamaguard-7b&smModelId=meta-textgeneration-llama-guard-7b&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
meta-llama/Meta-Llama-3-70B | meta-textgenerationneuron-llama-3-70b | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="meta-textgeneration-llama-3-70b")
example_payloads = model.retr... | [{"label": "Large Language Model (LLM): Run Inference in Amazon SageMaker JumpStart", "href": "https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart-foundation-models/llama-2-text-completion.ipynb"}, {"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com... | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fmeta-llama-3-70b&smModelId=meta-textgenerationneuron-llama-3-70b&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
meta-llama/Meta-Llama-3-70B-Instruct | meta-textgenerationneuron-llama-3-70b-instruct | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="meta-textgeneration-llama-3-70b-instruct")
example_payloads = m... | [{"label": "Large Language Model (LLM): Run Inference in Amazon SageMaker JumpStart", "href": "https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-chatbot.ipynb"}, {"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com... | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fmeta-llama-3-70b-instruct&smModelId=meta-textgenerationneuron-llama-3-70b-instruct&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
meta-llama/Meta-Llama-3-8B | meta-textgenerationneuron-llama-3-8b | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="meta-textgeneration-llama-3-8b")
example_payloads = model.retri... | [{"label": "Large Language Model (LLM): Run Inference in Amazon SageMaker JumpStart", "href": "https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart-foundation-models/llama-2-text-completion.ipynb"}, {"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com... | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fmeta-llama-3-8b&smModelId=meta-textgenerationneuron-llama-3-8b&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
meta-llama/Meta-Llama-3-8B-Instruct | meta-textgenerationneuron-llama-3-8b-instruct | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="meta-textgeneration-llama-3-8b-instruct")
example_payloads = mo... | [{"label": "Large Language Model (LLM): Run Inference in Amazon SageMaker JumpStart", "href": "https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-chatbot.ipynb"}, {"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com... | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fmeta-llama-3-8b-instruct&smModelId=meta-textgenerationneuron-llama-3-8b-instruct&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
meta-llama/Prompt-Guard-86M | meta-tc-llama-prompt-guard-86m | false | false | true | false | false | # Deploy a gated JumpStart model using the SageMaker Python SDK v3.
# This model requires accepting the end-user license agreement (EULA).
from sagemaker.serve.model_builder import ModelBuilder
from sagemaker.core.jumpstart.configs import JumpStartConfig
from sagemaker.core.training.configs import Compute
# You must ... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-llama%2Fprompt-guard-86m&smModelId=meta-tc-llama-prompt-guard-86m&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
meta-models/Muse-Glimmer-30B | huggingface-vlm-muse-glimmer-30b | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model_id = "huggingface-vlm-muse-glimmer-30b"
model = JumpStartModel(model_id=model_id)
payload... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=meta-models%2Fmuse-glimmer-30b&smModelId=huggingface-vlm-muse-glimmer-30b&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
microsoft/Phi-3-mini-128k-instruct | huggingface-llm-phi-3-mini-128k-instruct | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="huggingface-llm-phi-3-mini-128k-instruct")
example_payloads = m... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=microsoft%2Fphi-3-mini-128k-instruct&smModelId=huggingface-llm-phi-3-mini-128k-instruct&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
microsoft/Phi-3-mini-4k-instruct | huggingface-llm-phi-3-mini-4k-instruct | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="huggingface-llm-phi-3-mini-4k-instruct")
example_payloads = mod... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=microsoft%2Fphi-3-mini-4k-instruct&smModelId=huggingface-llm-phi-3-mini-4k-instruct&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
microsoft/Phi-3.5-mini-instruct | huggingface-llm-phi-3-5-mini-instruct | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="huggingface-llm-phi-3-5-mini-instruct")
example_payloads = mode... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=microsoft%2Fphi-3.5-mini-instruct&smModelId=huggingface-llm-phi-3-5-mini-instruct&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
microsoft/Phi-4-mini-instruct | huggingface-llm-phi-4-mini-instruct | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="huggingface-llm-phi-4-mini-instruct")
example_payloads = model.... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=microsoft%2Fphi-4-mini-instruct&smModelId=huggingface-llm-phi-4-mini-instruct&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
microsoft/phi-2 | huggingface-llm-phi-2 | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="huggingface-llm-phi-2")
example_payloads = model.retrieve_all_e... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=microsoft%2Fphi-2&smModelId=huggingface-llm-phi-2&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
microsoft/table-transformer-detection | huggingface-od-microsoft-table-transformer-detection | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="huggingface-od-microsoft-table-transformer-detection")
example_... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=microsoft%2Ftable-transformer-detection&smModelId=huggingface-od-microsoft-table-transformer-detection&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
mistralai/Ministral-3-14B-Instruct-2512 | null | false | null | true | false | null | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="huggingface-llm-ministral-3-14b-instruct-2512")
example_payload... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
mistralai/Ministral-3-14B-Instruct-2512-BF16 | huggingface-vlm-ministral-3-14b-instruct-2512-bf16 | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model_id = "huggingface-vlm-ministral-3-14b-instruct-2512-bf16"
model = JumpStartModel(model_id... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=mistralai%2Fministral-3-14b-instruct-2512-bf16&smModelId=huggingface-vlm-ministral-3-14b-instruct-2512-bf16&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
mistralai/Ministral-3-3B-Instruct-2512 | huggingface-llm-ministral-3-3b-instruct-2512 | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="huggingface-llm-ministral-3-3b-instruct-2512")
example_payloads... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=mistralai%2Fministral-3-3b-instruct-2512&smModelId=huggingface-llm-ministral-3-3b-instruct-2512&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
mistralai/Ministral-3-3B-Instruct-2512-BF16 | huggingface-vlm-ministral-3-3b-instruct-2512-bf16 | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model_id = "huggingface-vlm-ministral-3-3b-instruct-2512-bf16"
model = JumpStartModel(model_id=... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=mistralai%2Fministral-3-3b-instruct-2512-bf16&smModelId=huggingface-vlm-ministral-3-3b-instruct-2512-bf16&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
mistralai/Ministral-3-8B-Instruct-2512 | huggingface-llm-ministral-3-8b-instruct-2512 | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="huggingface-llm-ministral-3-8b-instruct-2512")
example_payloads... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=mistralai%2Fministral-3-8b-instruct-2512&smModelId=huggingface-llm-ministral-3-8b-instruct-2512&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
mistralai/Ministral-3-8B-Instruct-2512-BF16 | huggingface-vlm-ministral-3-8b-instruct-2512-bf16 | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model_id = "huggingface-vlm-ministral-3-8b-instruct-2512-bf16"
model = JumpStartModel(model_id=... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=mistralai%2Fministral-3-8b-instruct-2512-bf16&smModelId=huggingface-vlm-ministral-3-8b-instruct-2512-bf16&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
mistralai/Mistral-7B-Instruct-v0.3 | huggingface-llm-mistral-7b-instruct-v3 | false | false | true | false | false | # Deploy a JumpStart model using the SageMaker Python SDK v3.
from sagemaker.serve.model_builder import ModelBuilder
from sagemaker.core.jumpstart.configs import JumpStartConfig
from sagemaker.core.training.configs import Compute
model_builder = ModelBuilder.from_jumpstart_config(
jumpstart_config=JumpStartConfig... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=mistralai%2Fmistral-7b-instruct-v0.3&smModelId=huggingface-llm-mistral-7b-instruct-v3&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
mistralai/Mistral-Nemo-Base-2407 | huggingface-llm-mistral-nemo-base-2407 | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="huggingface-llm-mistral-nemo-base-2407")
example_payloads = mod... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=mistralai%2Fmistral-nemo-base-2407&smModelId=huggingface-llm-mistral-nemo-base-2407&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
mistralai/Mistral-Nemo-Instruct-2407 | huggingface-llm-mistral-nemo-instruct-2407 | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="huggingface-llm-mistral-nemo-instruct-2407")
example_payloads =... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=mistralai%2Fmistral-nemo-instruct-2407&smModelId=huggingface-llm-mistral-nemo-instruct-2407&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
mistralai/Mistral-Small-3.1-24B-Base-2503 | huggingface-vlm-mistral-small-3-1-24b-base-2503 | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model_id = "huggingface-vlm-mistral-small-3-1-24b-base-2503"
model = JumpStartModel(model_id=mo... | [] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=mistralai%2Fmistral-small-3.1-24b-base-2503&smModelId=huggingface-vlm-mistral-small-3-1-24b-base-2503&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
mistralai/Mistral-Small-3.2-24B-Instruct-2506 | huggingface-vlm-mistral-small-3-2-24b-instruct-2506 | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model_id = "huggingface-vlm-mistral-small-3-2-24b-instruct-2506"
model = JumpStartModel(model_i... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=mistralai%2Fmistral-small-3.2-24b-instruct-2506&smModelId=huggingface-vlm-mistral-small-3-2-24b-instruct-2506&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
mistralai/Mixtral-8x7B-v0.1 | huggingface-llm-mixtral-8x7b | false | true | true | true | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="huggingface-llm-mixtral-8x7b")
example_payloads = model.retriev... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | # Fine-tune a Hugging Face Hub model using the SageMaker Python SDK v3.
# See https://github.com/huggingface/transformers/tree/v4.56.2/examples/pytorch
import boto3
from sagemaker.train.model_trainer import ModelTrainer
from sagemaker.core.training.configs import Compute, SourceCode
from sagemaker.core import image_ur... | [] | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=mistralai%2Fmixtral-8x7b-v0.1&smModelId=huggingface-llm-mixtral-8x7b&hasVariants=false&page=deploy | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=mistralai%2Fmixtral-8x7b-v0.1&smModelId=huggingface-llm-mixtral-8x7b&supportServerless=false&supportServerful=true&hasVariants=false&page=customize | null | null | null | null | null | null | null | null | null | null | null | null |
mistralai/Pixtral-12B-2409 | huggingface-vlm-mistral-pixtral-12b-2409 | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model_id = "huggingface-vlm-mistral-pixtral-12b-2409"
model = JumpStartModel(model_id=model_id)... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=mistralai%2Fpixtral-12b-2409&smModelId=huggingface-vlm-mistral-pixtral-12b-2409&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
mistralai/Voxtral-Mini-4B-Realtime-2602 | huggingface-asr-voxtral-mini-4b-realtime-2602 | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="huggingface-asr-voxtral-mini-4b-realtime-2602")
example_payload... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=mistralai%2Fvoxtral-mini-4b-realtime-2602&smModelId=huggingface-asr-voxtral-mini-4b-realtime-2602&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
moonshotai/Kimi-K2.5 | huggingface-llm-kimi-k2-5 | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="huggingface-llm-kimi-k2-5")
example_payloads = model.retrieve_a... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=moonshotai%2Fkimi-k2.5&smModelId=huggingface-llm-kimi-k2-5&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
mrm8488/bert-small2bert-small-finetuned-cnn_daily_mail-summarization | huggingface-summarization-bert-small2bert-small-finetuned-cnn-daily-mail-summarization | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model_id = "huggingface-summarization-bert-small2bert-cnn-dailymail-summ"
endpoint_input = "The ... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=mrm8488%2Fbert-small2bert-small-finetuned-cnn_daily_mail-summarization&smModelId=huggingface-summarization-bert-small2bert-small-finetuned-cnn-daily-mail-summarization&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
naclbit/trinart_stable_diffusion_v2 | huggingface-txt2img-naclbit-trinart-stable-diffusion-v2 | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="huggingface-txt2img-naclbit-trinart-stable-diffusion-v2")
examp... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=naclbit%2Ftrinart_stable_diffusion_v2&smModelId=huggingface-txt2img-naclbit-trinart-stable-diffusion-v2&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
nvidia/Cosmos3-Edge | huggingface-txt2vid-cosmos3-edge | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
# This model accepts multipart/form-data payloads, which are sent directly through the SageMaker Runtime client.
import boto3
from sagemaker.jumpstart.... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=nvidia%2Fcosmos3-edge&smModelId=huggingface-txt2vid-cosmos3-edge&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
nvidia/Cosmos3-Nano | huggingface-txt2vid-cosmos3-nano | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
# This model accepts multipart/form-data payloads, which are sent directly through the SageMaker Runtime client.
import boto3
from sagemaker.jumpstart.... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=nvidia%2Fcosmos3-nano&smModelId=huggingface-txt2vid-cosmos3-nano&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
nvidia/Cosmos3-Super | huggingface-txt2vid-cosmos3-super | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
# This model accepts multipart/form-data payloads, which are sent directly through the SageMaker Runtime client.
import boto3
from sagemaker.jumpstart.... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=nvidia%2Fcosmos3-super&smModelId=huggingface-txt2vid-cosmos3-super&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
nvidia/Gemma-4-31B-IT-NVFP4 | huggingface-llm-gemma-4-31b-it-nvfp4 | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="huggingface-llm-gemma-4-31b-it-nvfp4")
example_payloads = model... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=nvidia%2Fgemma-4-31b-it-nvfp4&smModelId=huggingface-llm-gemma-4-31b-it-nvfp4&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
nvidia/Llama3-ChatQA-1.5-8B | huggingface-llm-nvidia-llama3-chatqa-1-5-8B | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="huggingface-llm-nvidia-llama3-chatqa-1-5-8B")
example_payloads ... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=nvidia%2Fllama3-chatqa-1.5-8b&smModelId=huggingface-llm-nvidia-llama3-chatqa-1-5-8B&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
nvidia/LocateAnything-3B | huggingface-od-nvidia-locateanything-3b | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="huggingface-od-nvidia-locateanything-3b")
example_payloads = mo... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=nvidia%2Flocateanything-3b&smModelId=huggingface-od-nvidia-locateanything-3b&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 | huggingface-reasoning-nvidia-nemotron-3-nano-30b-a3b-bf16 | true | false | true | true | false | # Deploy a JumpStart model using the SageMaker Python SDK v3.
from sagemaker.serve.model_builder import ModelBuilder
from sagemaker.core.jumpstart.configs import JumpStartConfig
from sagemaker.core.training.configs import Compute
model_builder = ModelBuilder.from_jumpstart_config(
jumpstart_config=JumpStartConfig... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=nvidia%2Fnvidia-nemotron-3-nano-30b-a3b-bf16&smModelId=huggingface-reasoning-nvidia-nemotron-3-nano-30b-a3b-bf16&hasVariants=false&page=deploy | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=nvidia%2Fnvidia-nemotron-3-nano-30b-a3b-bf16&smModelId=huggingface-reasoning-nvidia-nemotron-3-nano-30b-a3b-bf16&supportServerless=true&supportServerful=false&hasVariants=false&page=customize | null | null | null | # Fine-tune with RL from AI Feedback (RLAIF) using the SageMaker Python SDK v3.
from sagemaker.train.rlaif_trainer import RLAIFTrainer
from sagemaker.train.common import TrainingType
from sagemaker.ai_registry.dataset import DataSet
# Dataset format: JSONL with {"prompt": [...messages...], "reward_model": {"style": "... | RL from AI Feedback (RLAIF) | [] | # Fine-tune with RL with Verifiable Rewards (RLVR) using the SageMaker Python SDK v3.
from sagemaker.train.rlvr_trainer import RLVRTrainer
from sagemaker.train.common import TrainingType
from sagemaker.ai_registry.dataset import DataSet
# Dataset format: JSONL with {"prompt": [...messages...], "reward_model": {"groun... | RL with Verifiable Rewards (RLVR) | [] | # Fine-tune with Supervised Fine-Tuning (SFT) using the SageMaker Python SDK v3.
from sagemaker.train.sft_trainer import SFTTrainer
from sagemaker.train.common import TrainingType
from sagemaker.ai_registry.dataset import DataSet
# Dataset format: JSONL with {"prompt": "...", "completion": "..."} per line
# Download ... | Supervised Fine-Tuning (SFT) | [] |
nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-BF16 | huggingface-llm-nvidia-nemotron-3-super-120b-a12b-bf16 | true | false | true | true | false | # Deploy a JumpStart model using the SageMaker Python SDK v3.
from sagemaker.serve.model_builder import ModelBuilder
from sagemaker.core.jumpstart.configs import JumpStartConfig
from sagemaker.core.training.configs import Compute
model_builder = ModelBuilder.from_jumpstart_config(
jumpstart_config=JumpStartConfig... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=nvidia%2Fnvidia-nemotron-3-super-120b-a12b-bf16&smModelId=huggingface-llm-nvidia-nemotron-3-super-120b-a12b-bf16&hasVariants=false&page=deploy | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=nvidia%2Fnvidia-nemotron-3-super-120b-a12b-bf16&smModelId=huggingface-llm-nvidia-nemotron-3-super-120b-a12b-bf16&supportServerless=true&supportServerful=false&hasVariants=false&page=customize | null | null | null | # Fine-tune with RL from AI Feedback (RLAIF) using the SageMaker Python SDK v3.
from sagemaker.train.rlaif_trainer import RLAIFTrainer
from sagemaker.train.common import TrainingType
from sagemaker.ai_registry.dataset import DataSet
# Dataset format: JSONL with {"prompt": [...messages...], "reward_model": {"style": "... | RL from AI Feedback (RLAIF) | [] | # Fine-tune with RL with Verifiable Rewards (RLVR) using the SageMaker Python SDK v3.
from sagemaker.train.rlvr_trainer import RLVRTrainer
from sagemaker.train.common import TrainingType
from sagemaker.ai_registry.dataset import DataSet
# Dataset format: JSONL with {"prompt": [...messages...], "reward_model": {"groun... | RL with Verifiable Rewards (RLVR) | [] | # Fine-tune with Supervised Fine-Tuning (SFT) using the SageMaker Python SDK v3.
from sagemaker.train.sft_trainer import SFTTrainer
from sagemaker.train.common import TrainingType
from sagemaker.ai_registry.dataset import DataSet
# Dataset format: JSONL with {"prompt": "...", "completion": "..."} per line
# Download ... | Supervised Fine-Tuning (SFT) | [] |
nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8 | huggingface-reasoning-nvidia-nemotron-3-super-120b-a12b-fp8 | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="huggingface-reasoning-nvidia-nemotron-3-super-120b-a12b-fp8")
e... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=nvidia%2Fnvidia-nemotron-3-super-120b-a12b-fp8&smModelId=huggingface-reasoning-nvidia-nemotron-3-super-120b-a12b-fp8&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-NVFP4 | huggingface-reasoning-nvidia-nemotron-3-ultra-550b-a55b-nvfp4 | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="huggingface-reasoning-nvidia-nemotron-3-ultra-550b-a55b-nvfp4")... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=nvidia%2Fnvidia-nemotron-3-ultra-550b-a55b-nvfp4&smModelId=huggingface-reasoning-nvidia-nemotron-3-ultra-550b-a55b-nvfp4&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16 | huggingface-reasoning-nemotron-3-5-lightning-30b-a3b-bf16 | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="huggingface-reasoning-nemotron-3-5-lightning-30b-a3b-bf16")
exa... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=nvidia%2Fnvidia-nemotron-3.5-lightning-30b-a3b-bf16&smModelId=huggingface-reasoning-nemotron-3-5-lightning-30b-a3b-bf16&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4 | huggingface-reasoning-nemotron-3-5-lightning-30b-a3b-nvfp4 | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="huggingface-reasoning-nemotron-3-5-lightning-30b-a3b-nvfp4")
ex... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=nvidia%2Fnvidia-nemotron-3.5-lightning-30b-a3b-nvfp4&smModelId=huggingface-reasoning-nemotron-3-5-lightning-30b-a3b-nvfp4&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
nvidia/NVIDIA-Nemotron-Nano-12B-v2 | huggingface-reasoning-nvidia-nemotron-nano-12b-v2 | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="huggingface-reasoning-nvidia-nemotron-nano-12b-v2")
example_pay... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=nvidia%2Fnvidia-nemotron-nano-12b-v2&smModelId=huggingface-reasoning-nvidia-nemotron-nano-12b-v2&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-FP8 | huggingface-vlm-nvidia-nemotron3-nano-omni-30ba3b-reasoning-fp8 | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model_id = "huggingface-vlm-nvidia-nemotron3-nano-omni-30ba3b-reasoning-fp8"
model = JumpStartM... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=nvidia%2Fnemotron-3-nano-omni-30b-a3b-reasoning-fp8&smModelId=huggingface-vlm-nvidia-nemotron3-nano-omni-30ba3b-reasoning-fp8&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
nvidia/Qwen3.6-35B-A3B-NVFP4 | huggingface-llm-qwen3-6-35b-a3b-nvfp4 | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="huggingface-llm-qwen3-6-35b-a3b-nvfp4")
example_payloads = mode... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=nvidia%2Fqwen3.6-35b-a3b-nvfp4&smModelId=huggingface-llm-qwen3-6-35b-a3b-nvfp4&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
openai-community/gpt2-xl | huggingface-textgeneration1-gpt-2-xl | false | true | true | true | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="huggingface-textgeneration1-gpt-2-xl")
example_payloads = model... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | # Fine-tune a Hugging Face Hub model using the SageMaker Python SDK v3.
# See https://github.com/huggingface/transformers/tree/v4.56.2/examples/pytorch
import boto3
from sagemaker.train.model_trainer import ModelTrainer
from sagemaker.core.training.configs import Compute, SourceCode
from sagemaker.core import image_ur... | [{"href": "https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart-foundation-models/domain-adaption-finetuning-gpt-j-6b.ipynb", "label": "Large Language Model (LLM): Run Domain Adaptation fine-tuning in Amazon SageMaker JumpStart"}, {"href": "https://aws.amazon.com/sagema... | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=openai-community%2Fgpt2-xl&smModelId=huggingface-textgeneration1-gpt-2-xl&hasVariants=false&page=deploy | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=openai-community%2Fgpt2-xl&smModelId=huggingface-textgeneration1-gpt-2-xl&supportServerless=false&supportServerful=true&hasVariants=false&page=customize | null | null | null | null | null | null | null | null | null | null | null | null |
openai-community/roberta-base-openai-detector | huggingface-eqa-roberta-base-openai-detector | false | true | true | true | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="huggingface-eqa-roberta-base-openai-detector")
example_payloads... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | # Fine-tune a Hugging Face Hub model using the SageMaker Python SDK v3.
# See https://github.com/huggingface/transformers/tree/v4.56.2/examples/pytorch
import boto3
from sagemaker.train.model_trainer import ModelTrainer
from sagemaker.core.training.configs import Compute, SourceCode
from sagemaker.core import image_ur... | [] | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=openai-community%2Froberta-base-openai-detector&smModelId=huggingface-eqa-roberta-base-openai-detector&hasVariants=false&page=deploy | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=openai-community%2Froberta-base-openai-detector&smModelId=huggingface-eqa-roberta-base-openai-detector&supportServerless=false&supportServerful=true&hasVariants=false&page=customize | null | null | null | null | null | null | null | null | null | null | null | null |
openai/gpt-oss-120b | openai-reasoning-gpt-oss-120b | true | false | true | true | false | # Deploy a JumpStart model using the SageMaker Python SDK v3.
from sagemaker.serve.model_builder import ModelBuilder
from sagemaker.core.jumpstart.configs import JumpStartConfig
from sagemaker.core.training.configs import Compute
model_builder = ModelBuilder.from_jumpstart_config(
jumpstart_config=JumpStartConfig... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=openai%2Fgpt-oss-120b&smModelId=openai-reasoning-gpt-oss-120b&hasVariants=false&page=deploy | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=openai%2Fgpt-oss-120b&smModelId=openai-reasoning-gpt-oss-120b&supportServerless=true&supportServerful=false&hasVariants=false&page=customize | # Fine-tune with Direct Preference Optimization (DPO) using the SageMaker Python SDK v3.
from sagemaker.train.dpo_trainer import DPOTrainer
from sagemaker.train.common import TrainingType
from sagemaker.ai_registry.dataset import DataSet
# Dataset format: JSONL with {"prompt": [...messages...], "chosen": "...", "reje... | Direct Preference Optimization (DPO) | [] | # Fine-tune with RL from AI Feedback (RLAIF) using the SageMaker Python SDK v3.
from sagemaker.train.rlaif_trainer import RLAIFTrainer
from sagemaker.train.common import TrainingType
from sagemaker.ai_registry.dataset import DataSet
# Dataset format: JSONL with {"prompt": [...messages...], "reward_model": {"style": "... | RL from AI Feedback (RLAIF) | [] | # Fine-tune with RL with Verifiable Rewards (RLVR) using the SageMaker Python SDK v3.
from sagemaker.train.rlvr_trainer import RLVRTrainer
from sagemaker.train.common import TrainingType
from sagemaker.ai_registry.dataset import DataSet
# Dataset format: JSONL with {"prompt": [...messages...], "reward_model": {"groun... | RL with Verifiable Rewards (RLVR) | [] | # Fine-tune with Supervised Fine-Tuning (SFT) using the SageMaker Python SDK v3.
from sagemaker.train.sft_trainer import SFTTrainer
from sagemaker.train.common import TrainingType
from sagemaker.ai_registry.dataset import DataSet
# Dataset format: JSONL with {"prompt": "...", "completion": "..."} per line
# Download ... | Supervised Fine-Tuning (SFT) | [] |
openai/gpt-oss-20b | openai-reasoning-gpt-oss-20b | true | false | true | true | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="openai-reasoning-gpt-oss-20b")
example_payloads = model.retriev... | [{"href": "https://aws.amazon.com/sagemaker/jumpstart", "label": "Amazon SageMaker JumpStart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=openai%2Fgpt-oss-20b&smModelId=openai-reasoning-gpt-oss-20b&hasVariants=false&page=deploy | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=openai%2Fgpt-oss-20b&smModelId=openai-reasoning-gpt-oss-20b&supportServerless=true&supportServerful=false&hasVariants=false&page=customize | # Fine-tune with Direct Preference Optimization (DPO) using the SageMaker Python SDK v3.
from sagemaker.train.dpo_trainer import DPOTrainer
from sagemaker.train.common import TrainingType
from sagemaker.ai_registry.dataset import DataSet
# Dataset format: JSONL with {"prompt": [...messages...], "chosen": "...", "reje... | Direct Preference Optimization (DPO) | [] | # Fine-tune with RL from AI Feedback (RLAIF) using the SageMaker Python SDK v3.
from sagemaker.train.rlaif_trainer import RLAIFTrainer
from sagemaker.train.common import TrainingType
from sagemaker.ai_registry.dataset import DataSet
# Dataset format: JSONL with {"prompt": [...messages...], "reward_model": {"style": "... | RL from AI Feedback (RLAIF) | [] | # Fine-tune with RL with Verifiable Rewards (RLVR) using the SageMaker Python SDK v3.
from sagemaker.train.rlvr_trainer import RLVRTrainer
from sagemaker.train.common import TrainingType
from sagemaker.ai_registry.dataset import DataSet
# Dataset format: JSONL with {"prompt": [...messages...], "reward_model": {"groun... | RL with Verifiable Rewards (RLVR) | [] | # Fine-tune with Supervised Fine-Tuning (SFT) using the SageMaker Python SDK v3.
from sagemaker.train.sft_trainer import SFTTrainer
from sagemaker.train.common import TrainingType
from sagemaker.ai_registry.dataset import DataSet
# Dataset format: JSONL with {"prompt": "...", "completion": "..."} per line
# Download ... | Supervised Fine-Tuning (SFT) | [] |
openai/privacy-filter | openai-tokenclassification-privacy-filter | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from sagemaker.jumpstart.model import JumpStartModel
model = JumpStartModel(model_id="openai-tokenclassification-privacy-filter")
example_payloads = ... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=openai%2Fprivacy-filter&smModelId=openai-tokenclassification-privacy-filter&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
openai/whisper-base | huggingface-asr-whisper-base | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from scipy.io.wavfile import read
import json
import boto3
from sagemaker.jumpstart import utils
from sagemaker.jumpstart.model import JumpStartModel
... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=openai%2Fwhisper-base&smModelId=huggingface-asr-whisper-base&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
openai/whisper-large | huggingface-asr-whisper-large | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from scipy.io.wavfile import read
import json
import boto3
from sagemaker.jumpstart import utils
from sagemaker.jumpstart.model import JumpStartModel
... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=openai%2Fwhisper-large&smModelId=huggingface-asr-whisper-large&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
openai/whisper-large-v2 | huggingface-asr-whisper-large-v2 | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from scipy.io.wavfile import read
import json
import boto3
from sagemaker.jumpstart import utils
from sagemaker.jumpstart.model import JumpStartModel
... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=openai%2Fwhisper-large-v2&smModelId=huggingface-asr-whisper-large-v2&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
openai/whisper-large-v3 | huggingface-asr-whisper-large-v3 | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from scipy.io.wavfile import read
import json
import boto3
from sagemaker.jumpstart import utils
from sagemaker.jumpstart.model import JumpStartModel
... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=openai%2Fwhisper-large-v3&smModelId=huggingface-asr-whisper-large-v3&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
openai/whisper-large-v3-turbo | huggingface-asr-whisper-large-v3-turbo | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from scipy.io.wavfile import read
import json
import boto3
from sagemaker.jumpstart import utils
from sagemaker.jumpstart.model import JumpStartModel
... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=openai%2Fwhisper-large-v3-turbo&smModelId=huggingface-asr-whisper-large-v3-turbo&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
openai/whisper-medium | huggingface-asr-whisper-medium | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from scipy.io.wavfile import read
import json
import boto3
from sagemaker.jumpstart import utils
from sagemaker.jumpstart.model import JumpStartModel
... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=openai%2Fwhisper-medium&smModelId=huggingface-asr-whisper-medium&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
openai/whisper-small | huggingface-asr-whisper-small | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from scipy.io.wavfile import read
import json
import boto3
from sagemaker.jumpstart import utils
from sagemaker.jumpstart.model import JumpStartModel
... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=openai%2Fwhisper-small&smModelId=huggingface-asr-whisper-small&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
openai/whisper-tiny | huggingface-asr-whisper-tiny | false | false | true | false | false | # SageMaker JumpStart provides APIs as part of SageMaker SDK that allow you to deploy and fine-tune models in network isolation using scripts that SageMaker maintains.
from scipy.io.wavfile import read
import json
import boto3
from sagemaker.jumpstart import utils
from sagemaker.jumpstart.model import JumpStartModel
... | [{"label": "Amazon SageMaker JumpStart", "href": "https://aws.amazon.com/sagemaker/jumpstart"}] | null | null | https://console.aws.amazon.com/sagemaker/home#/launch?source=hf&extModelId=openai%2Fwhisper-tiny&smModelId=huggingface-asr-whisper-tiny&hasVariants=false&page=deploy | null | null | null | null | null | null | null | null | null | null | null | null | null |
Subsets and Splits
Serverless Deployable Models
Filters for models that support all three deployment configurations (deployable, serverful training, and serverless customization) to identify models suitable for flexible deployment scenarios.
Amazon SageMaker Model Organizations
Provides a list of unique organizations from Hugging Face model IDs, which helps identify different model creators but offers limited analytical value beyond basic categorization.
Neuron Redirections
Filters training data for entries related to neuron models, providing basic categorical insight but offering limited analytical value beyond simple identification.