# Use Custom model import to import a customized open-source model into Amazon Bedrock
You can create a custom model in Amazon Bedrock by using the Amazon Bedrock Custom Model Import feature to import Foundation Models that you have customized in other environments, such as Amazon SageMaker AI. For example, you might have a model that you have created in Amazon SageMaker AI that has proprietary model weights. You can now import that model into Amazon Bedrock and then leverage Amazon Bedrock features to make inference calls to the model.
You can use a model that you import with on demand throughput. Use the [InvokeModel](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_InvokeModel.html) or [InvokeModelWithResponseStream](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_InvokeModelWithResponseStream.html) operations to make inference calls to the model. For more information, see [Submit a single prompt with InvokeModel](inference-invoke.md).
Custom model import is supported in the following regions:
+ eu-central-1
+ us-east-1
+ us-east-2
+ us-west-2
**Note**
Make sure that your import and use of the models in Amazon Bedrock complies with the terms or licenses applicable to the models.
You can't use Custom Model Import with the following Amazon Bedrock features.
Batch inference
CloudFormation
With Custom Model Import you can create a custom model that supports the following patterns.
+ **Fine-tuned model** — You can customize the model weights using proprietary data, but retain the configuration of the base model.
+ **Adaptation** You can customize the model to your domain for use cases where the model doesn't generalize well. Domain adaptation modifies a model to generalize for a target domain and deal with discrepancies across domains, such as a financial industry wanting to create a model which generalizes well on pricing. Another example is language adaptation. For example you could customize a model to generate responses in Portuguese or Tamil. Most often, this involves changes to the vocabulary of the model that you are using.
+ **Pretrained from scratch** — In addition to customizing the weights and vocabulary of the model, you can also change model configuration parameters such as the number of attention heads, hidden layers, or context length.
For information regarding pricing for custom model import, select the Custom Model Import tab in the Model pricing details section of [Amazon Bedrock pricing](https://aws.amazon.com/bedrock/pricing).
**Topics**
+ [Supported architectures](#model-customization-import-model-architecture)
+ [Import a model source from Amazon S3](#model-customization-import-model-source)
+ [Prerequisites for importing custom model](custom-model-import-prereq.md)
+ [Submit a model import job](model-customization-import-model-job.md)
+ [Invoke your imported model](invoke-imported-model.md)
+ [Handling Custom Chat Templates and Tokenizers](custom-chat-templates-tokenizers.md)
+ [Calculate the cost of running a custom model](import-model-calculate-cost.md)
+ [Code samples for custom model import](custom-model-import-code-samples.md)
## Supported architectures
The model you import must be in one of the following architectures.
+ **Mistral** — A decoder-only Transformer based architecture with Sliding Window Attention (SWA) and options for Grouped Query Attention (GQA). For more information, see [https://huggingface.co/docs/transformers/en/model_doc/mistral](https://huggingface.co/docs/transformers/en/model_doc/mistral) in the Hugging Face documentation.
+ **Mixtral** — A decoder-only transformer model with sparse Mixture of Experts (MoE) models. For more information, see [Mixtral](https://huggingface.co/docs/transformers/en/model_doc/mixtral) in the Hugging Face documentation.
+ **Flan ** — An enhanced version of the T5 architecture, an encoder-decoder based transformer model. For more information, see [https://huggingface.co/docs/transformers/model_doc/flan-t5](https://huggingface.co/docs/transformers/model_doc/flan-t5) in the Hugging Face documentation.
+ **Llama 2, Llama3, Llama3.1, Llama3.2, Llama 3.3, and Mllama** — An improved version of Llama with Grouped Query Attention (GQA). For more information, see [https://huggingface.co/blog/llama2](https://huggingface.co/blog/llama2), [https://huggingface.co/blog/llama3 ](https://huggingface.co/blog/llama3 ), [https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct#model-information](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct#model-information), [https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct](https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct), [https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct), and [https://huggingface.co/docs/transformers/main/en/model_doc/mllama](https://huggingface.co/docs/transformers/main/en/model_doc/mllama) in the Hugging Face documentation.
+ **GPTBigCode** — An optimized version of GPT-2 with Multi-Query action. For more information, see [GPTBigCode](https://huggingface.co/docs/transformers/en/model_doc/gpt_bigcode) in the Hugging Face documentation.
+ **Qwen2, Qwen2.5, Qwen2-VL, Qwen2.5-VL, Qwen3** — An LLM family with comprehensive multimodal perception and high speed vision encoding. Any model using the Qwen2, Qwen2-VL, and Qwen2.5-VL architectures can be imported. For Qwen3 architecture, only Qwen3ForCausalLM and Qwen3MoeForCausalLM are supported. Converse is also not supported for Qwen3 models. For more information, see [Qwen2](https://huggingface.co/collections/Qwen/qwen2-6659360b33528ced941e557f), [Qwen2.5](https://huggingface.co/collections/Qwen/qwen25-66e81a666513e518adb90d9e), [Qwen2-VL](https://huggingface.co/collections/Qwen/qwen2-vl-66cee7455501d7126940800d), [Qwen2.5-VL](https://huggingface.co/collections/Qwen/qwen25-vl-6795ffac22b334a837c0f9a5), and [Qwen3](https://huggingface.co/docs/transformers/en/model_doc/qwen3) in the Hugging Face documentation.
+ **GPT-OSS** — OpenAI based GPT-OSS architecture. Amazon Bedrock supports both 20B and 120B based custom models. GPT-OSS model architecture is only supported in the US East (N. Virginia) region.
**Note**
The size of the imported model weights must be less than 100GB for multimodal models and 200GB for text models.
The maximum positional embeddings or the maximum context length supported by the model should be less than 128K.
Amazon Bedrock supports transformer version 4.51.3. Ensure that you are using transformer version 4.51.3 when you fine tune your model.
Custom Model Import does not support embedding models.
## Import a model source from Amazon S3
You import a model into Amazon Bedrock by creating a model import job in the Amazon Bedrock console or API. In the job you specify the Amazon S3 URI for the source of the model files. During model training, the import job automatically detects your model's architecture.
You need to supply the model files in the Hugging Face weights format. You can create the files by using the Hugging Face transformer library. To create model files for a Llama model, see [convert\$1llama\$1weights\$1to\$1hf.py](https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py). To create the files for a Mistral AI model, see [convert\$1mistral\$1weights\$1to\$1hf.py](https://github.com/huggingface/transformers/blob/main/src/transformers/models/mistral/convert_mistral_weights_to_hf.py).
To import the model from Amazon S3, you minimally need the following files that the Hugging Face transformer library creates.
+ **.safetensor** — the model weights in *Safetensor* format. Safetensors is a format created by Hugging Face that stores a model weights as tensors. You must store the tensors for your model in a file with the extension `.safetensors`. For more information, see [Safetensors](https://huggingface.co/docs/safetensors/en/index). For information about converting model weights to Safetensor format, see [Convert weights to safetensors](https://huggingface.co/docs/safetensors/en/convert-weights).
+ **config.json** — For examples, see [LlamaConfig](https://huggingface.co/docs/transformers/model_doc/llama2#transformers.LlamaConfig) and [MistralConfig](https://huggingface.co/docs/transformers/model_doc/mistral#transformers.MistralConfig).
**Note**
Amazon Bedrock overrides llama3 `rope_scaling` value with the following values:
`original_max_position_embeddings=8192`
`high_freq_factor=4`
`low_freq_factor=1`
`factor=8`
+ **tokenizer\$1config.json** For an example, see [LlamaTokenizer](https://huggingface.co/docs/transformers/model_doc/llama2#transformers.LlamaTokenizer).
+ **tokenizer.json **
+ **tokenizer.model**
### Supported tokenizers
Amazon Bedrock Custom Model Import supports the following tokenizers. You can use these tokenizers with any model.
+ T5Tokenizer
+ T5TokenizerFast
+ LlamaTokenizer
+ LlamaTokenizerFast
+ CodeLlamaTokenizer
+ CodeLlamaTokenizerFast
+ GPT2Tokenizer
+ GPT2TokenizerFast
+ GPTNeoXTokenizer
+ GPTNeoXTokenizerFast
+ PreTrainedTokenizer
+ PreTrainedTokenizerFast
+ Qwen2Tokenizer
+ Qwen2TokenizerFast
# Prerequisites for importing custom model
Before you can start a custom model import job, you need to fulfill the following prerequisites:
1. If you are importing your model from Amazon S3 bucket, prepare your model files in the Hugging Face weights format. For more information see, [Import a model source from Amazon S3](model-customization-import-model.md#model-customization-import-model-source).
1. If you are using cross-account Amazon S3 or KMS keys, make sure to grant access to Amazon S3 bucket or the KMS key. For more information, see [Cross-account access to Amazon S3 bucket for custom model import jobs](cross-account-access-cmi.md).
1. (Optional) Create a custom AWS Identity and Access Management (IAM) [service role](https://docs.aws.amazon.com/IAM/latest/UserGuide/id_roles_terms-and-concepts.html#iam-term-service-role) with the proper permissions by following the instructions at [Create a service role for importing pre-trained models](model-import-iam-role.md) to set up the role. You can skip this prerequisite if you plan to use the AWS Management Console to automatically create a service role for you.
1. (Optional) Set up extra security configurations.
+ You can encrypt input and output data, import jobs, or inference requests made to imported models. For more information see [Encryption of custom model import](https://docs.aws.amazon.com//bedrock/latest/userguide/encryption-import-model.html).
+ You can create a virtual private cloud (VPC) to protect your customization jobs. For more information, see [(Optional) Protect custom model import jobs using a VPC](vpc-custom-model-import.md).
# (Optional) Protect custom model import jobs using a VPC
When you run a custom model import job, the job accesses your Amazon S3 bucket to download the input data and to upload job metrics. To control access to your data, we recommend that you use a virtual private cloud (VPC) with [Amazon VPC](https://docs.aws.amazon.com/vpc/latest/userguide/what-is-amazon-vpc.html). You can further protect your data by configuring your VPC so that your data isn't available over the internet and instead creating a VPC interface endpoint with [AWS PrivateLink](https://docs.aws.amazon.com/vpc/latest/privatelink/what-is-privatelink.html) to establish a private connection to your data. For more information about how Amazon VPC and AWS PrivateLink integrate with Amazon Bedrock, see [Protect your data using Amazon VPC and AWS PrivateLink](usingVPC.md).
Carry out the following steps to configure and use a VPC for importing your custom models.
**Topics**
+ [Set up a VPC](#create-vpc-cmi)
+ [Create an Amazon S3 VPC Endpoint](#train-vpc-s3-cmi)
+ [(Optional) Use IAM policies to restrict access to your S3 files](#train-vpc-policy-cmi)
+ [Attach VPC permissions to a custom model import role.](#vpc-data-access-role-cmi)
+ [Add the VPC configuration when submitting a model import job](#vpc-config-cmi)
## Set up a VPC
You can use a [default VPC](https://docs.aws.amazon.com/vpc/latest/userguide/default-vpc.html) for your model import data or create a new VPC by following the guidance at [Get started with Amazon VPC](https://docs.aws.amazon.com/vpc/latest/userguide/vpc-getting-started.html) and [Create a VPC](https://docs.aws.amazon.com/vpc/latest/userguide/create-vpc.html).
When you create your VPC, we recommend that you use the default DNS settings for your endpoint route table, so that standard Amazon S3 URLs (for example, `http://s3-aws-region.amazonaws.com/model-bucket`) resolve.
## Create an Amazon S3 VPC Endpoint
If you configure your VPC with no internet access, you need to create an [Amazon S3 VPC endpoint](https://docs.aws.amazon.com/AmazonS3/latest/userguide/privatelink-interface-endpoints.html) to allow your model import jobs to access the S3 buckets that store your training and validation data and that will store the model artifacts.
Create the S3 VPC endpoint by following the steps at [Create a gateway endpoint for Amazon S3](https://docs.aws.amazon.com/vpc/latest/privatelink/vpc-endpoints-s3.html#create-gateway-endpoint-s3).
**Note**
If you don't use the default DNS settings for your VPC, you need to ensure that the URLs for the locations of the data in your training jobs resolve by configuring the endpoint route tables. For information about VPC endpoint route tables, see [Routing for Gateway endpoints](https://docs.aws.amazon.com/AmazonVPC/latest/UserGuide/vpce-gateway.html#vpc-endpoints-routing).
## (Optional) Use IAM policies to restrict access to your S3 files
You can use [resource-based policies](https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies_identity-vs-resource.html) to more tightly control access to your S3 files. You can the following type of resource-based policy.
+ **Endpoint policies** – Endpoint policies restrict access through the VPC endpoint. The default endpoint policy allows full access to Amazon S3 for any user or service in your VPC. While creating or after you create the endpoint, you can optionally attach a resource-based policy to the endpoint to add restrictions, such as only allowing the endpoint to access a specific bucket or only allowing a specific IAM role to access the endpoint. For examples, see [Edit the VPC endpoint policy](https://docs.aws.amazon.com/vpc/latest/privatelink/vpc-endpoints-s3.html#edit-vpc-endpoint-policy-s3).
The following is an example policy you can attach to your VPC endpoint to only allow it to access the bucket containing your model weights.
------
#### [ JSON ]
****
```
{
"Version":"2012-10-17",
"Statement": [
{
"Sid": "RestrictAccessToModelWeightsBucket",
"Effect": "Allow",
"Principal": "*",
"Action": [
"s3:GetObject",
"s3:ListBucket"
],
"Resource": [
"arn:aws:s3:::model-weights-bucket",
"arn:aws:s3:::model-weights-bucket/*"
]
}
]
}
```
------
## Attach VPC permissions to a custom model import role.
After you finish setting up your VPC and endpoint, you need to attach the following permissions to your [model import IAM role](model-import-iam-role.md). Modify this policy to allow access to only the VPC resources that your job needs. Replace the *subnet-ids* and *security-group-id* with the values from your VPC.
------
#### [ JSON ]
****
```
{
"Version":"2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"ec2:DescribeNetworkInterfaces",
"ec2:DescribeVpcs",
"ec2:DescribeDhcpOptions",
"ec2:DescribeSubnets",
"ec2:DescribeSecurityGroups"
],
"Resource": "*"
},
{
"Effect": "Allow",
"Action": [
"ec2:CreateNetworkInterface"
],
"Resource": [
"arn:aws:ec2:us-east-1:123456789012:network-interface/*"
],
"Condition": {
"StringEquals": {
"aws:RequestTag/BedrockManaged": [
"true"
]
},
"ArnEquals": {
"aws:RequestTag/BedrockModelImportJobArn": [
"arn:aws:bedrock:us-east-1:123456789012:model-import-job/*"
]
}
}
},
{
"Effect": "Allow",
"Action": [
"ec2:CreateNetworkInterface"
],
"Resource": [
"arn:aws:ec2:us-east-1:123456789012:subnet/subnet-id",
"arn:aws:ec2:us-east-1:123456789012:subnet/subnet-id2",
"arn:aws:ec2:us-east-1:123456789012:security-group/security-group-id"
]
},
{
"Effect": "Allow",
"Action": [
"ec2:CreateNetworkInterfacePermission",
"ec2:DeleteNetworkInterface",
"ec2:DeleteNetworkInterfacePermission"
],
"Resource": "*",
"Condition": {
"ArnEquals": {
"ec2:Subnet": [
"arn:aws:ec2:us-east-1:123456789012:subnet/subnet-id",
"arn:aws:ec2:us-east-1:123456789012:subnet/subnet-id2"
],
"ec2:ResourceTag/BedrockModelImportJobArn": [
"arn:aws:bedrock:us-east-1:123456789012:model-import-job/*"
]
},
"StringEquals": {
"ec2:ResourceTag/BedrockManaged": "true"
}
}
},
{
"Effect": "Allow",
"Action": [
"ec2:CreateTags"
],
"Resource": "arn:aws:ec2:us-east-1:123456789012:network-interface/*",
"Condition": {
"StringEquals": {
"ec2:CreateAction": [
"CreateNetworkInterface"
]
},
"ForAllValues:StringEquals": {
"aws:TagKeys": [
"BedrockManaged",
"BedrockModelImportJobArn"
]
}
}
}
]
}
```
------
## Add the VPC configuration when submitting a model import job
After you configure the VPC and the required roles and permissions as described in the previous sections, you can create a model import job that uses this VPC.
When you specify the VPC subnets and security groups for a job, Amazon Bedrock creates *elastic network interfaces* (ENIs) that are associated with your security groups in one of the subnets. ENIs allow the Amazon Bedrock job to connect to resources in your VPC. For information about ENIs, see [Elastic Network Interfaces](https://docs.aws.amazon.com/vpc/latest/userguide/VPC_ElasticNetworkInterfaces.html) in the *Amazon VPC User Guide*. Amazon Bedrock tags ENIs that it creates with `BedrockManaged` and `BedrockModelImportJobArn` tags.
We recommend that you provide at least one subnet in each Availability Zone.
You can use security groups to establish rules for controlling Amazon Bedrock access to your VPC resources.
You can configure the VPC to use in either the console or through the API. Choose the tab for your preferred method, and then follow the steps:
------
#### [ Console ]
For the Amazon Bedrock console, you specify VPC subnets and security groups in the optional **VPC settings** section when you create the model import job. For more information about configuring model import jobs, see [Submit a model import job](model-customization-import-model-job.md).
------
#### [ API ]
When you submit a [CreateModelCustomizationJob](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_CreateModelCustomizationJob.html) request, you can include a `VpcConfig` as a request parameter to specify the VPC subnets and security groups to use, as in the following example.
```
"VpcConfig": {
"SecurityGroupIds": [
"sg-0123456789abcdef0"
],
"Subnets": [
"subnet-0123456789abcdef0",
"subnet-0123456789abcdef1",
"subnet-0123456789abcdef2"
]
}
```
------
# Submit a model import job
You import a model into Amazon Bedrock by submitting a model import job in the Amazon Bedrock console, using the API, using the AWS CLI or using AWS SDK. In the job you specify the Amazon S3 URI for the source of the model files. Alternatively, if you've created the model in Amazon SageMaker AI, you can specify the SageMaker AI model. During model import, the import job automatically detects your model's architecture. The model import job can take several minutes. During the job, Amazon Bedrock validates that the model that is being imported is using a compatible the model architecture.
The following procedure shows you how to create a custom model by importing a model that you have already customized. Select the tab corresponding to your method of choice and follow the steps.
------
#### [ Console ]
To submit a model import job in the console, complete the following steps.
1. If you are importing your model files from Amazon S3, convert the model to the Hugging Face format.
1. If your model is a Mistral AI model, use [convert\$1mistral\$1weights\$1to\$1hf.py](https://github.com/huggingface/transformers/blob/main/src/transformers/models/mistral/convert_mistral_weights_to_hf.py).
1. If your model is a Llama model, see [convert\$1llama\$1weights\$1to\$1hf.py](https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py).
1. Upload the model files to an Amazon S3 bucket in your AWS account. For more information, see [Upload an object to your bucket](https://docs.aws.amazon.com/AmazonS3/latest/userguide/uploading-an-object-bucket.html).
1. If you are using cross-account Amazon S3 or KMS keys to import your custom model, give Amazon Bedrock access to your AWS account's Amazon S3 or KMS key. For more information, see [Cross-account access to Amazon S3 bucket for custom model import jobs](cross-account-access-cmi.md).
1. In the Amazon Bedrock console, choose **Imported models** under **Foundation models** from the left navigation pane.
1. Choose the **Models** tab.
1. Choose **Import model**.
1. In the **Imported** tab, choose **Import model** to open the **Import model** page.
1. In the **Model details** section, do the following:
1. In **Model name** enter a name for the model.
1. (Optional) To associate [tags](tagging.md) with the model, expand the **Tags** section and select **Add new tag**.
1. In the **Import job name** section, do the following:
1. In **Job name** enter a name for the model import job.
1. (Optional) To associate [tags](tagging.md) with the custom model, expand the **Tags** section and select **Add new tag**.
1. In **Model import settings**, select the import options you want to use.
+ Select **Amazon S3 bucket** or ** Amazon SageMaker AI model** to specify the import source.
+ If you are importing your model files from an Amazon S3 bucket, enter the Amazon S3 location in **S3 location**. Optionally, you can choose **Browse S3** to choose the file location.
+ If you are importing your model from Amazon SageMaker AI, choose **Amazon SageMaker AI model** and then choose the SageMaker AI model that you want to import in **SageMaker AI models**.
1. Enter **VPC settings** (optional) to choose a VPC configuration to access your Amazon Amazon S3 data source located in your VPC. You can create and manage a VPC, subnets, and security groups in Amazon VPC. For more information on Amazon VPC, see [(Optional) Protect custom model import jobs using a VPC](vpc-custom-model-import.md).
1. Select **Encryption**, to encrypt your data by default with an AWS key that is owned and managed by you. You can also choose a different key if you select **Customize encryption settings (advanced).**.
1. In the **Service access** section, select one of the following:
+ **Create and use a new service role** – Enter a name for the service role.
+ **Use an existing service role** – Select a service role from the drop-down list. To see the permissions that your existing service role needs, choose **View permission details**.
For more information on setting up a service role with the appropriate permissions, see [Create a service role for importing pre-trained models](model-import-iam-role.md).
**Note**
if you are using cross-account Amazon S3 or KMS keys, edit the service role policy and replace the account id specified for `aws:ResourceAccount` with the AWS account id of the bucket owner.
1. Choose **Import**.
1. On the **Custom models** page, choose **Imported**.
1. In the **Jobs** section, check the status of the import job. The model name you chose identifies the model import job. The job is complete if the value of **Status** for the model is **Complete**.
1. Get the model ID for your model by doing the following.
1. On the **Imported models** page, choose the **Models** tab.
1. Copy the ARN for the model that you want to use from the **ARN** column.
1. Use your model for inference calls. For more information, see [Submit a single prompt with InvokeModel](inference-invoke.md). You can use the model with on demand throughput.
You can also use your model in the Amazon Bedrock text [playground](playgrounds.md).
------
#### [ API ]
**Request**
Send a [CreateModelImportJob](https://docs.aws.amazon.com//bedrock/latest/APIReference/API_CreateModelImportJob.html) (see link for request and response format and field details) request with an [Amazon Bedrock control plane endpoint](https://docs.aws.amazon.com/general/latest/gr/bedrock.html#br-cp) to submit a custom model import job. Minimally, you must provide the following fields.
+ `roleArn` – The ARN of the service role with permissions to import models. Amazon Bedrock can automatically create a role with the appropriate permissions if you use the console, or you can create a custom role by following the steps at [Create a service role for importing pre-trained models](model-import-iam-role.md).
**Note**
If you include a `vpcConfig` field, make sure that the role has the proper permissions to access the VPC. For an example, see [Attach VPC permissions to a custom model import role.](vpc-custom-model-import.md#vpc-data-access-role-cmi)
+ `importedModelName` – The name to give the newly imported model.
+ `jobName` – The name to give the import job.
+ `modelDataSource` – The data source for the imported model.
To prevent the request from completing more than once, include a `clientRequestToken`.
You can include the following optional fields for extra configurations.
+ `jobTags` and/or `importedModelTags` – Associate [tags](tagging.md) with the import job or the imported model.
+ `importedModelKmsKeyId` – Include a [Encryption of custom model import](https://docs.aws.amazon.com//bedrock/latest/userguide/encryption-import-model.html) KMS key to encrypt your imported model.
+ `vpcConfig` – Include the vpc configuration to [(Optional) Protect custom model import jobs using a VPC](vpc-custom-model-import.md).
**Response**
The response returns a `jobArn` for the import job that you use to identify the import job in other operations.
The import job will take a while to complete. You can check the current status by calling the [GetModelImportJob](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_GetModelImportJob.html) operation and checking the `Status` field in the response. You can list the current import jobs with the [ListModelImportJobs](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_ListModelImportJobs.html).
To get a list of models that you have imported, call [ListImportedModels](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_ListImportedModels.html). To get information about a specific imported model, call [GetImportedModel](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_GetImportedModel.html).
To delete an imported model, call [DeleteImportedModel](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_DeleteImportedModel.html).
------
# Invoke your imported model
The model import job can take several minutes to import your model after you send [CreateModelImportJob](https://docs.aws.amazon.com//bedrock/latest/APIReference/API_CreateModelImportJob.html) request. You can check the status of your import job in the console or by calling the [GetModelImportJob](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_GetModelImportJob.html) operation and checking the `Status` field in the response. The import job is complete if the Status for the model is **Complete**.
After your imported model is available in Amazon Bedrock, you can use the model with on demand throughput by sending [InvokeModel](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_InvokeModel.html) or [InvokeModelWithResponseStream](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_InvokeModelWithResponseStream.html) requests to make inference calls to the model. For more information, see [Submit a single prompt with InvokeModel](inference-invoke.md).
To interface with your imported model using the messages format, you can call the [Converse](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_Converse.html) or [ConverseStream](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_ConverseStream.html) operations. For more information, see [Using the Converse API](conversation-inference-call.md).
**Note**
Converse API is not supported for Qwen2.5, Qwen2-VL, Qwen2.5-VL, and GPT-OSS models.
## Enhanced API Support: Multiple API Formats
Starting November 17, 2025, Amazon Bedrock Custom Model Import supports comprehensive OpenAI-compatible API formats, providing flexibility in how you integrate and deploy your custom models. All models imported after November 11, 2025, will automatically benefit from these enhanced capabilities with no additional configuration required.
Custom Model Import now supports three API formats:
+ **BedrockCompletion (Text)** - Compatible with current Bedrock workflows
+ **OpenAICompletion (Text)** - OpenAI Completions Schema compatibility
+ **OpenAIChatCompletion (Text and Images)** - Full conversational Schema compatibility
These enhanced capabilities include structured outputs for enforcing JSON schemas and patterns, enhanced vision support with multi-image processing, log probabilities for model confidence insights, and tool calling capabilities for GPT-OSS models.
For detailed API reference documentation, see the official OpenAI documentation:
+ Completion: [OpenAI Completions API](https://platform.openai.com/docs/api-reference/completions)
+ ChatCompletion: [OpenAI Chat API](https://platform.openai.com/docs/api-reference/chat)
### API Format Examples
The following examples demonstrate how to use each of the four supported API formats with your imported models.
------
#### [ BedrockCompletion ]
**BedrockCompletion** format is compatible with current Bedrock workflows and supports text-based inference requests.
Example request:
```
import json
import boto3
client = boto3.client('bedrock-runtime', region_name='us-east-1')
payload = {
"prompt": "How is the rainbow formed?",
"max_gen_len": 100,
"temperature": 0.5
}
response = client.invoke_model(
modelId='your-model-arn',
body=json.dumps(payload),
accept='application/json',
contentType='application/json'
)
response_body = json.loads(response['body'].read())
```
Example response:
```
{
"generation": " – A scientific explanation\nA rainbow is a beautiful natural phenomenon that occurs when sunlight passes through water droplets in the air. It is formed through a process called refraction, which is the bending of light as it passes from one medium to another.\nHere's a step-by-step explanation of how a rainbow is formed:\n1. Sunlight enters the Earth's atmosphere: The first step in forming a rainbow is for sunlight to enter the Earth's atmosphere. This sunlight is made up of a spectrum of",
"prompt_token_count": 7,
"generation_token_count": 100,
"stop_reason": "length",
"logprobs": null
}
```
BedrockCompletion supports structured outputs using `response_format` parameter with `json_object` and `json_schema` types.
------
#### [ OpenAICompletion ]
**OpenAICompletion** format provides OpenAI Completions Schema compatibility. To use this format, include the `max_tokens` parameter instead of `max_gen_len`.
Example request:
```
import json
import boto3
client = boto3.client('bedrock-runtime', region_name='us-east-1')
payload = {
"prompt": "How is the rainbow formed?",
"max_tokens": 100,
"temperature": 0.5
}
response = client.invoke_model(
modelId='your-model-arn',
body=json.dumps(payload),
accept='application/json',
contentType='application/json'
)
response_body = json.loads(response['body'].read())
```
Example response:
```
{
"id": "cmpl-b09d5810bd64428f8a853be71c31f912",
"object": "text_completion",
"created": 1763166682,
"choices": [
{
"index": 0,
"text": " The formation of a rainbow is a complex process that involves the interaction of sunlight with water droplets in the air. Here's a simplified explanation: 1. Sunlight enters the Earth's atmosphere and is refracted, or bent, as it passes through the air. 2. When sunlight encounters a water droplet, such as a cloud, mist, or fog, it is refracted again and split into its individual colors, a process known as dispersion. 3. The refracted and",
"finish_reason": "length"
}
],
"usage": {
"prompt_tokens": 7,
"total_tokens": 107,
"completion_tokens": 100
}
}
```
OpenAICompletion supports full structured outputs capabilities including `json`, `regex`, `choice`, and `grammar` constraints using the `structured_outputs` parameter.
------
#### [ OpenAIChatCompletion ]
**OpenAIChatCompletion** format provides full conversational Schema compatibility and supports both text and image inputs.
Example request:
```
import json
import boto3
client = boto3.client('bedrock-runtime', region_name='us-east-1')
payload = {
"messages": [
{
"role": "user",
"content": "How is the rainbow formed?"
}
],
"max_tokens": 100,
"temperature": 0.5
}
response = client.invoke_model(
modelId='your-model-arn',
body=json.dumps(payload),
accept='application/json',
contentType='application/json'
)
response_body = json.loads(response['body'].read())
```
Example response:
```
{
"id": "chatcmpl-1d84ce1d3d61418e8c6d1973f87173db",
"object": "chat.completion",
"created": 1763166683,
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "A rainbow is a beautiful natural phenomenon that occurs when sunlight passes through water droplets in the air. The process of forming a rainbow involves several steps:\n\n1. **Sunlight**: The first requirement for a rainbow is sunlight. The sun should be shining brightly, but not directly overhead.\n2. **Water droplets**: The second requirement is water droplets in the air..."
},
"finish_reason": "length"
}
],
"usage": {
"prompt_tokens": 41,
"completion_tokens": 100,
"total_tokens": 141
}
}
```
OpenAIChatCompletion supports structured outputs using both `response_format` and `structured_outputs` parameters. For vision capabilities, include images in the content array with base64-encoded image data.
**Note**
To use ChatCompletion format, the chat template needs to be part of the `tokenizer_config.json`. Custom Model Import will not apply any default chat templates to the request.
------
# Advanced API features for imported models
This page provides detailed examples of advanced features available for models imported after November 11, 2025. These capabilities include structured outputs for controlled generation, enhanced vision support for multi-image processing, log probabilities for confidence insights, and tool calling for GPT-OSS models.
## Structured Outputs
Structured outputs enable controlled generation following specific formats, schemas, or patterns. This feature ensures that the model's response adheres to predefined constraints, making it ideal for applications requiring consistent data formats, API integrations, or automated processing pipelines.
Structured outputs on Custom Model Import are supported via two parameters:
+ `response_format` - Supports `json_object` and `json_schema` types
+ `structured_outputs` - Supports `json`, `regex`, `choice`, and `grammar` types
**Note**
When using structured outputs on Custom Model Import, customers should expect performance trade-offs due to constraint validation during generation. Simple constraints like `choice` and `json_object` have minimal impact, while complex constraints like `json_schema` and `grammar` can significantly increase latency and reduce throughput. For optimal performance, use simpler constraint types when possible and keep schemas flat rather than deeply nested.
The following examples demonstrate structured outputs support across different API formats. The Pydantic model definition is:
```
from pydantic import BaseModel
from enum import Enum
class CarType(str, Enum):
sedan = "sedan"
suv = "SUV"
truck = "Truck"
coupe = "Coupe"
class CarDescription(BaseModel):
brand: str
model: str
car_type: CarType
```
------
#### [ BedrockCompletion ]
BedrockCompletion supports structured outputs using the `response_format` parameter with `json_object` and `json_schema` types only.
**Example: JSON Schema**
```
payload = {
"prompt": "Generate a JSON with the brand, model and car_type of the most iconic car from the 90's",
"response_format": {
"type": "json_schema",
"json_schema": CarDescription.model_json_schema()
}
}
response = client.invoke_model(
modelId='your-model-arn',
body=json.dumps(payload),
accept='application/json',
contentType='application/json'
)
response_body = json.loads(response['body'].read())
```
Example response:
```
{
"generation": "{\n \"brand\": \"Ferrari\",\n \"model\": \"F40\",\n \"car_type\": \"SUV\"\n }",
"prompt_token_count": 22,
"generation_token_count": 30,
"stop_reason": "stop",
"logprobs": null
}
```
------
#### [ OpenAICompletion ]
OpenAICompletion supports both `response_format` (json\$1object, json\$1schema) and `structured_outputs` (json, regex, choice, grammar) parameters. Use `max_tokens` instead of `max_gen_len` to route requests to OpenAICompletion.
**Example: Structured Outputs - Choice**
```
payload = {
"prompt": "Classify the sentiment of this sentence. Amazon Bedrock CMI is Amazing!",
"max_tokens": 10,
"structured_outputs": {
"choice": ["positive", "negative"]
}
}
response = client.invoke_model(
modelId='your-model-arn',
body=json.dumps(payload),
accept='application/json',
contentType='application/json'
)
response_body = json.loads(response['body'].read())
```
Example response:
```
{
"id": "cmpl-01f94c4652d24870bbb4d5418a01c384",
"object": "text_completion",
"choices": [
{
"index": 0,
"text": "positive",
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 17,
"completion_tokens": 4
}
}
```
------
#### [ OpenAIChatCompletion ]
OpenAIChatCompletion supports both `response_format` (json\$1object, json\$1schema) and `structured_outputs` (json, regex, choice, grammar) parameters.
**Example: Response Format - JSON Schema**
```
payload = {
"messages": [
{"role": "user", "content": "Generate a JSON with the brand, model and car_type of the most iconic car from the 90's"}
],
"max_tokens": 100,
"response_format": {
"type": "json_schema",
"json_schema": {
"name": "car-description",
"schema": CarDescription.model_json_schema()
}
}
}
response = client.invoke_model(
modelId='your-model-arn',
body=json.dumps(payload),
accept='application/json',
contentType='application/json'
)
response_body = json.loads(response['body'].read())
```
Example response:
```
{
"id": "chatcmpl-cae5a43b0a924b8eb434510cbf978a19",
"object": "chat.completion",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "{\"brand\": \"Dodge\", \"model\": \"Viper\", \"car_type\": \"Coupe\"}"
},
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 56,
"completion_tokens": 23
}
}
```
------
## Vision Support
Vision capabilities enable processing of images alongside text inputs, with enhanced multi-image support for complex visual analysis tasks. Custom Model Import now supports up to 3 images per request, enhanced from previous single-image limitation.
**Supported API:** OpenAIChatCompletion only. All models imported after November 11, 2025 will default to this API for vision capabilities.
**Image Requirements:**
+ Base64 encoding required - Image URLs will cause request failures
+ Maximum 3 images per request
+ High resolution images significantly increase processing time and memory usage
**Warning**
High resolution images significantly increase processing time, memory usage, and may cause timeout errors. Multiple high-resolution images compound performance impact exponentially. For optimal performance, resize images appropriately and use lower detail levels when possible.
------
#### [ OpenAIChatCompletion ]
**Example: Multi-Image Processing**
```
import json
import boto3
import base64
client = boto3.client('bedrock-runtime', region_name='us-east-1')
# Load and encode images
with open('/path/to/car_image_1.jpg', 'rb') as f:
image_data_1 = base64.b64encode(f.read()).decode('utf-8')
with open('/path/to/car_image_2.jpg', 'rb') as f:
image_data_2 = base64.b64encode(f.read()).decode('utf-8')
payload = {
"messages": [
{
"role": "system",
"content": "You are a helpful assistant that can analyze images."
},
{
"role": "user",
"content": [
{
"type": "text",
"text": "Spot the difference between the two images?"
},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{image_data_1}"
}
},
{
"type": "image_url",
"image_url": {
"url": f"data:image/jpeg;base64,{image_data_2}"
}
}
]
}
],
"max_tokens": 300,
"temperature": 0.5
}
response = client.invoke_model(
modelId='your-model-arn',
body=json.dumps(payload),
accept='application/json',
contentType='application/json'
)
response_body = json.loads(response['body'].read())
```
Example response:
```
{
"id": "chatcmpl-ccae8a67e62f4014a9ffcbedfff96f44",
"object": "chat.completion",
"created": 1763167018,
"model": "667387627229-g6vkuhd609s4",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "There are no differences between the two images provided. They appear to be identical.",
"refusal": null,
"annotations": null,
"audio": null,
"function_call": null,
"tool_calls": [],
"reasoning_content": null
},
"logprobs": null,
"finish_reason": "stop",
"stop_reason": null,
"token_ids": null
}
],
"service_tier": null,
"system_fingerprint": null,
"usage": {
"prompt_tokens": 2795,
"total_tokens": 2812,
"completion_tokens": 17,
"prompt_tokens_details": null
},
"prompt_logprobs": null,
"prompt_token_ids": null,
"kv_transfer_params": null
}
```
------
## Log Probabilities
Log probabilities represent the likelihood of each token in a sequence, calculated as log(p) where p is the probability of a token at any position given its previous token in the context. Since log probs are additive, sequence probability equals the sum of individual token log probs, making them useful for ranking generations by average per-token scores. Custom Model Import will always return the raw logprob values for requested tokens.
Key applications include classification tasks where log probs enable custom confidence thresholds, retrieval Q&A systems that use confidence scores to reduce hallucinations, autocomplete suggestions based on token likelihood, and perplexity calculations for comparing model performance across prompts. Log probs also provide token-level analysis capabilities, allowing developers to examine alternative tokens the model considered.
**Note**
Logprobs are not cached. For a request requiring prompt logprobs, the system will ignore the prefix cache and recompute the prefill of full prompt to generate the logprobs. This presents an obvious performance tradeoff when using logprobs.
Log probability support varies by API format:
+ BedrockCompletion - Output tokens only
+ OpenAICompletion - Prompt and output tokens
+ OpenAIChatCompletion - Prompt and output tokens
------
#### [ BedrockCompletion ]
BedrockCompletion only supports output token logprobs. This will return top 1 logprob for each output token.
```
payload = {
"prompt": "How is the rainbow formed?",
"max_gen_len": 10,
"temperature": 0.5,
"return_logprobs": True
}
response = client.invoke_model(
modelId='your-model-arn',
body=json.dumps(payload),
accept='application/json',
contentType='application/json'
)
response_body = json.loads(response['body'].read())
```
Example response (truncated):
```
{
"generation": " A rainbow is formed when sunlight passes through water dro",
"prompt_token_count": 7,
"generation_token_count": 10,
"stop_reason": "length",
"logprobs": [
{
"362": -2.1413702964782715
},
{
"48713": -0.8180374503135681
},
{
"374": -0.09657637774944305
},
...
]
}
```
------
#### [ OpenAIChatCompletion ]
OpenAIChatCompletion supports both prompt and output token logprobs. You can set `top_logprobs=N` and `prompt_logprobs=N` where N is an integer representing log probabilities for the N most likely tokens at each position.
```
payload = {
"messages": [
{
"role": "user",
"content": "How is the rainbow formed?"
}
],
"max_tokens": 10,
"temperature": 0.5,
"logprobs": True,
"top_logprobs": 1,
"prompt_logprobs": 1
}
response = client.invoke_model(
modelId='your-model-arn',
body=json.dumps(payload),
accept='application/json',
contentType='application/json'
)
response_body = json.loads(response['body'].read())
```
Example response (truncated):
```
{
"id": "chatcmpl-xxx",
"object": "chat.completion",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "A rainbow is formed..."
},
"logprobs": {
"content": [
{
"token": "A",
"logprob": -0.07903262227773666,
"bytes": [65],
"top_logprobs": [
{
"token": "A",
"logprob": -0.07903262227773666,
"bytes": [65]
}
]
},
{
"token": " rainbow",
"logprob": -0.20187227427959442,
"bytes": [32, 114, 97, 105, 110, 98, 111, 119],
"top_logprobs": [...]
},
...
]
},
"finish_reason": "length"
}
],
"usage": {
"prompt_tokens": 41,
"completion_tokens": 10,
"total_tokens": 51
}
}
```
------
You'll need the model ARN to make inference calls to your newly imported model. After the successful completion of the import job and after your imported model is active, you can get the model ARN of your imported model in the console or by sending a [ListImportedModels](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_ListImportedModels.html) request.
When you invoke your imported model using `InvokeModel` or `InvokeModelWithStream`, your request is served within 5 minutes or you might get `ModelNotReadyException`. To understand the ModelNotReadyException, follow the steps in this next section for handling ModelNotreadyException.
## Frequently Asked Questions
**Q: What API format should I use?**
A: For maximum compatibility with various SDKs, we recommend using OpenAICompletion or OpenAIChatCompletion formats as they provide OpenAI-compatible schemas that are widely supported across different tools and libraries.
**Q: Does GPT-OSS on Amazon Bedrock Custom Model Import support the Converse API?**
A: No. GPT-OSS based custom model import models do not support the Converse API or ConverseStream API. You must use the [InvokeModel](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_InvokeModel.html) API with OpenAI-compatible schemas when working with GPT-OSS based custom models.
**Q: What models support tool calling?**
A: GPT-OSS based custom models support tool calling capabilities. Tool calling enables function calling for complex workflows.
**Q: What about models imported before November 11, 2025?**
A: Models imported before November 11, 2025, continue to work as is with their existing API formats and capabilities.
**Q: What about `generation_config.json` for OpenAI-based models?**
A: It is critical that you include the correct `generation_config.json` file when importing OpenAI-based models such as GPT-OSS. You must use the updated configuration file (updated August 13, 2024) available at [https://huggingface.co/openai/gpt-oss-20b/blob/main/generation\$1config.json](https://huggingface.co/openai/gpt-oss-20b/blob/main/generation_config.json). The updated configuration includes three end-of-sequence token IDs (`[200002, 199999, 200012]`), whereas older versions only included two tokens (`[200002, 199999]`). Using an outdated `generation_config.json` file will cause runtime errors during model invocation. This file is essential for proper model behavior and must be included with your OpenAI-based model imports.
## Handling ModelNotReadyException
Amazon Bedrock Custom Model Import optimizes the hardware utilization by removing the models that are not active. If you try to invoke a model that has been removed, you'll get a `ModelNotReadyException`. After the model is removed and you invoke the model for the first time, Custom Model Import starts to restore the model. The restoration time depends on the on-demand fleet size and the model size.
If your `InvokeModel` or `InvokeModelWithStream` request returns `ModelNotReadyException`, follow the steps to handle the exception.
1.
**Configure retries**
By default, the request is automatically retried with exponential backoff. You can configure the maximum number of retries.
The following example shows how to configure the retry. Replace *\$1\$1region-name\$1*, *\$1\$1model-arn\$1*, and *10* with your Region, model ARN, and maximum attempts.
```
import json
import boto3
from botocore.config import Config
REGION_NAME = ${region-name}
MODEL_ID= '${model-arn}'
config = Config(
retries={
'total_max_attempts': 10, //customizable
'mode': 'standard'
}
)
message = "Hello"
session = boto3.session.Session()
br_runtime = session.client(service_name = 'bedrock-runtime',
region_name=REGION_NAME,
config=config)
try:
invoke_response = br_runtime.invoke_model(modelId=MODEL_ID,
body=json.dumps({'prompt': message}),
accept="application/json",
contentType="application/json")
invoke_response["body"] = json.loads(invoke_response["body"].read().decode("utf-8"))
print(json.dumps(invoke_response, indent=4))
except Exception as e:
print(e)
print(e.__repr__())
```
1.
**Monitor response codes during retry attempts**
Each retry attempt starts model restoration process. The restoration time depends on the availability of the on-demand fleet and the model size. Monitor the response codes while the restoration process is going on.
If the retries are consistently failing, continue with the next steps.
1.
**Verify model was successfully imported**
You can verify if the model was successfully imported by checking the status of your import job in the console or by calling the [GetModelImportJob](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_GetModelImportJob.html) operation. Check the `Status` field in the response. The import job is successful if the Status for the model is **Complete**.
1.
**Contact Support for further investigation**
Open a ticket with Support For more information, see [Creating support cases](https://docs.aws.amazon.com//awssupport/latest/user/case-management.html).
Include relevant details such as model ID and timestamps in the support ticket.
# Handling Custom Chat Templates and Tokenizers
Custom chat templates and tokenizers are essential components for properly formatting conversational inputs and managing token generation in your imported models. Custom Model Import supports importing models with custom chat templates that define how multi-turn conversations are structured and tokenized.
## Importing Models with Custom Chat Templates
When importing a model with custom chat templates, ensure that you follow the best practices outlined in the main Custom Model Import documentation, including bringing in model weights in safe tensors format and providing all necessary configuration files.
### Chat Template Format Requirements
If your model uses a custom chat template and you want to use that template with Amazon Bedrock, you must include the chat template file in one of the following formats:
+ **`chat_template.jinja`** — A Jinja2-based template file that defines how messages are formatted.
+ **`chat_template.json`** — A JSON file containing the chat template as a string value.
+ **`tokenizer_config.json` with embedded chat template** — Alternatively, you can include the chat template directly within your `tokenizer_config.json` file as a `chat_template` field. For an example, see the [Hermes-2-Pro tokenizer\$1config.json](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B/blob/main/tokenizer_config.json#L2059) on Hugging Face.
Custom Model Import will automatically process these files and include them in the correct directories during the import process.
#### Chat Template Precedence
If you provide multiple chat template sources, Amazon Bedrock applies the following precedence rules:
1. **Separate chat template files take precedence** — If you include both a separate chat template file (`chat_template.jinja` or `chat_template.json`) AND a `chat_template` field in your `tokenizer_config.json`, the separate file will be used and the embedded template in `tokenizer_config.json` will be ignored.
1. **Embedded template as fallback** — If you do not provide a separate chat template file, Amazon Bedrock will use the `chat_template` field from your `tokenizer_config.json` if present.
**Warning**
**Choose one approach:** To avoid confusion and ensure predictable behavior, we strongly recommend using only ONE of these approaches:
**Option 1:** Provide separate chat template files (`chat_template.jinja` or `chat_template.json`) without including a `chat_template` field in your `tokenizer_config.json`.
**Option 2:** Include the `chat_template` field directly in your `tokenizer_config.json` and do not provide separate template files.
If you have custom tool templates or complex chat template configurations, we recommend using Option 2 (embedding the template in `tokenizer_config.json`) as it allows you to define multiple named templates (such as "default" and "tool\$1use") within a single configuration file.
**Note**
The chat template file must follow the Hugging Face format and naming conventions. Ensure that your template is compatible with the Transformers library.
#### Chat Template Format Examples
Here are examples of the two supported chat template formats:
------
#### [ Jinja Format (chat\$1template.jinja) ]
A simplified example of a Jinja2-based chat template:
```
{% for message in messages %}
{% if loop.first and message['role'] != 'system' %}
<|im_start|>system
You are a helpful assistant.<|im_end|>
{% endif %}
<|im_start|>{{ message['role'] }}
{{ message['content'] }}<|im_end|>
{% endfor %}
{% if add_generation_prompt %}
<|im_start|>assistant
{% endif %}
```
For a complete example, see the [GPT-OSS chat template](https://huggingface.co/openai/gpt-oss-20b/blob/main/chat_template.jinja) on Hugging Face.
------
#### [ JSON Format (chat\$1template.json) ]
A simplified example of a JSON-based chat template with vision support:
```
{
"chat_template": "{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' %}<|vision_start|><|image_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
}
```
For a complete example with multimodal support, see the [Qwen2-VL chat template](https://huggingface.co/Qwen/Qwen2-VL-72B-Instruct/blob/main/chat_template.json) on Hugging Face.
------
**Important**
Ensure that your chat template file follows the exact naming convention (`chat_template.jinja` or `chat_template.json`) and format shown in the examples above. Incorrectly formatted templates may cause import or inference failures.
## Invoking Models with Custom Chat Templates
Once you have imported a model with a custom chat template, you have two options for invoking the model with properly formatted conversational inputs:
### Using OpenAI ChatCompletion API with Messages
If you are providing inputs in the messages schema format, you should use the **OpenAI ChatCompletion API**. When you use this API format with a model that has an uploaded chat template (`chat_template.jinja` or `chat_template.json`), Amazon Bedrock will automatically convert your input messages into the correct format using the chat template.
This is the recommended approach as it provides the most seamless integration and allows Amazon Bedrock to handle the chat template application automatically.
**Example: Using OpenAI ChatCompletion API with custom chat template**
```
import json
import boto3
# Initialize Bedrock Runtime client
client = boto3.client('bedrock-runtime', region_name='us-east-1')
# Define the model ARN for your imported model with custom chat template
model_id = 'arn:aws:bedrock:us-east-1:123456789012:imported-model/your-model-id'
# Prepare the request payload using messages format
payload = {
"messages": [
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "Hello, how are you?"
}
],
"max_tokens": 150,
"temperature": 0.7
}
# Invoke the model
response = client.invoke_model(
modelId=model_id,
body=json.dumps(payload),
accept='application/json',
contentType='application/json'
)
# Parse and display the response
response_body = json.loads(response['body'].read())
print(json.dumps(response_body, indent=2))
```
In this example, Amazon Bedrock will automatically apply your custom chat template to the messages array, converting it into the appropriate format for your model.
### Manual Tokenization Approach
Alternatively, if you prefer to have full control over the chat template application and tokenization process, you can manually apply the chat template to your conversation and then use the **Completion API** (not ChatCompletion) with the pre-formatted text.
This approach is useful when you need to customize the template application logic or when working with specialized tokenization requirements.
#### Step 1: Apply Chat Template Locally
Use the following code snippet to load your custom chat template and apply it to a conversation locally:
```
from transformers import AutoTokenizer
# Configuration paths - update these with your actual paths
TOKENIZER_PATH = "" # Path to tokenizer directory
# Load tokenizer with updated chat template
tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_PATH)
# Test chat template with sample conversation
chat_history = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Hello, how are you?"},
]
# Apply chat template and display formatted output to make sure chat template works as expected
formatted_chat = tokenizer.apply_chat_template(chat_history, tokenize=False)
print(formatted_chat)
```
This code demonstrates how to:
1. Load your tokenizer configuration from the model files
1. Add the custom chat template to the tokenizer configuration
1. Apply the chat template to a conversation history
1. Generate the formatted text that can be sent to the model
#### Step 2: Invoke with Completion API
After applying the chat template locally, use the formatted text with the Completion API:
```
import json
import boto3
# Initialize Bedrock Runtime client
client = boto3.client('bedrock-runtime', region_name='us-east-1')
# Define the model ARN for your imported model
model_id = 'arn:aws:bedrock:us-east-1:123456789012:imported-model/your-model-id'
# Use the formatted_chat output from Step 1 as the prompt
payload = {
"prompt": formatted_chat,
"max_tokens": 150,
"temperature": 0.7
}
# Invoke the model using Completion format (not ChatCompletion)
response = client.invoke_model(
modelId=model_id,
body=json.dumps(payload),
accept='application/json',
contentType='application/json'
)
# Parse and display the response
response_body = json.loads(response['body'].read())
print(json.dumps(response_body, indent=2))
```
**Warning**
**Always use `max_tokens` parameter:** When using the Completion API with custom model imports, always use the `max_tokens` parameter to ensure OpenAI completion schema compatibility. This avoids any translation confusion and ensures consistent behavior across different SDK implementations. Do not use model-specific parameter names like `max_gen_len` or similar variants.
**Important**
When using the manual tokenization approach, you must use the **Completion API** format (with `prompt` field), not the ChatCompletion API format (with `messages` field). The ChatCompletion API expects raw messages and will attempt to apply the chat template again, resulting in incorrect formatting.
### Best Practices
+ **Use ChatCompletion API when possible** — The OpenAI ChatCompletion API with messages format provides the most seamless experience and allows Amazon Bedrock to handle chat template application automatically.
+ **Validate your chat template** — Before importing your model, test your chat template locally using the Transformers library to ensure it produces the expected output format.
+ **Include all special tokens** — Ensure your chat template includes all necessary special tokens (such as beginning-of-sequence, end-of-sequence, and role markers) that your model expects.
+ **Test with multi-turn conversations** — Validate that your chat template correctly handles multi-turn conversations with alternating user and assistant messages.
+ **Consider vision support** — If your model supports multimodal inputs, ensure your chat template includes logic for handling image and video content markers.
**Warning**
Incorrectly formatted chat templates can result in poor model performance, unexpected outputs, or inference failures. Always test your chat template thoroughly before deploying to production.
# Calculate the cost of running a custom model
You are charged for running inference with custom models that you import into Amazon Bedrock. Use the following information to estimate the cost of running a custom model.
A Custom Model Unit (CMU) is an abstract view of the hardware utilization that Amazon Bedrock needs to host a a single copy of your custom model. A model copy represents a single instance of your imported model that is ready to serve inference requests. Amazon Bedrock determines the number of custom model units that a model copy needs when you import the custom model. As inference demand changes, Amazon Bedrock can increase or decrease the number of running model copies that are running.
You can use CMUs to estimate the cost of running your custom model by using the following formula.
*Total cost = Number of running model copies × Number of CMUs per copy × billing rate per CMU per min × ((Number of 5-min windows)/60)*
+ *Number of running model copies *– Get from the `ModelCopy` metric in Amazon CloudWatch.
+ *Number of CMUs per copy * – Get the value from the model details page in the Amazon Bedrock details page. Alternatively, call the [GetImportedModel](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_GetImportedModel.html) operation and get the number of CMUs per copy from the `customModelUnitsPerModelCopy` field.
+ *Billing rate per CMU per min* – Get the custom model units version from the model details page in the Amazon Bedrock console. Alternatively, call the [GetImportedModel](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_GetImportedModel.html) operation and get the custom model units version from the `customModelUnitsVersion` field.
Then, get the billing rate (**Price per Custom Model Unit per min\$1**) for the model version (**Custom Model Unit version**) on the **Custom Model Import** section of the [Amazon Bedrock pricing](https://aws.amazon.com/bedrock/pricing/) page.
Billing is charged over 5-minute billing windows from the first successful inference call.
For more information, see [Amazon Bedrock pricing](https://aws.amazon.com/bedrock/pricing/).
# Code samples for custom model import
The following code samples show how to set up permissions, create a custom model import job, view the details of your import jobs and imported models, and delete imported model. These code samples apply to Mistral AI, Llama, Qwen, Flan, GPTBigCode, and Mixtral architectures.
## Important: Model Architecture Support
**GPT-OSS Model Limitations:**
+ **Converse API Not Supported:** GPT-OSS based custom model import models do NOT support the Converse API or ConverseStream API.
+ **Use InvokeModel API Instead:** Customers must use the [InvokeModel](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_InvokeModel.html) API when working with GPT-OSS based custom models.
+ **API Schema Requirement:** GPT-OSS models require OpenAI-compatible API schemas:
+ Completion format for completion requests
+ ChatCompletion format for chat requests
+ Response format follows OpenAI API specifications
+ **Supported Models for Converse API:** The Converse API is supported for Mistral AI, Llama, Qwen, Flan, GPTBigCode, and Mixtral architectures only.
For GPT-OSS model usage examples with [InvokeModel](https://docs.aws.amazon.com/bedrock/latest/APIReference/API_runtime_InvokeModel.html) API, refer to the OpenAI API documentation for request/response schemas.
1. Prepare model files for import
1. If you are importing from an Amazon S3 bucket, you need to supply the model files in the Hugging Face weights format. For more information, see [Import a model source from Amazon S3](model-customization-import-model.md#model-customization-import-model-source).
1. Create an Amazon S3 bucket for your model files (the names must be unique).
1. Upload the model files into the bucket.
1. Create a policy to access your model files and attach it to an IAM role with an Amazon Bedrock trust relationship. Choose the tab for your preferred method, and then follow the steps:
------
#### [ Console ]
1. Create Amazon S3 policy to access the Amazon S3 bucket that contains your model files
1. Navigate to the IAM console at [https://console.aws.amazon.com/iam](https://console.aws.amazon.com/iam) and choose **Policies** from the left navigation pane.
1. Select **Create policy** and then choose **JSON** to open the **Policy editor**.
1. Paste the following policy, replacing *\$1\$1model-file-bucket\$1* with your bucket name, and then select **Next**.
------
#### [ JSON ]
****
```
{
"Version":"2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"s3:GetObject",
"s3:ListBucket"
],
"Resource": [
"arn:aws:s3:::${model-file-bucket}",
"arn:aws:s3:::${model-file-bucket}/*"
]
}
]
}
```
------
1. Name the policy *S3BucketPolicy* and select **Create policy**.
1. Create an IAM role and attach the policy.
1. From the left navigation pane, choose **Roles** and then select **Create role**.
1. Select **Custom trust policy**, paste the following policy, and select **Next**.
------
#### [ JSON ]
****
```
{
"Version":"2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Principal": {
"Service": "bedrock.amazonaws.com"
},
"Action": "sts:AssumeRole"
}
]
}
```
------
1. Search for the *S3BucketPolicy* policy you created, select the checkbox, and choose **Next**.
1. Name the role *MyImportModelRole* and select *Create role*.
------
#### [ CLI ]
1. Create a file called *BedrockTrust.json* and paste the following policy into it.
------
#### [ JSON ]
****
```
{
"Version":"2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Principal": {
"Service": "bedrock.amazonaws.com"
},
"Action": "sts:AssumeRole"
}
]
}
```
------
1. Create another file called *S3BucketPolicy.json* and paste the following policy into it, replacing *\$1\$1model-file-bucket\$1* with your bucket names.
------
#### [ JSON ]
****
```
{
"Version":"2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"s3:GetObject",
"s3:ListBucket"
],
"Resource": [
"arn:aws:s3:::${model-file-bucket}",
"arn:aws:s3:::${model-file-bucket}/*"
]
}
]
}
```
------
1. In a terminal, navigate to the folder containing the policies you created.
1. Make a [CreateRole](https://docs.aws.amazon.com/IAM/latest/APIReference/API_CreateRole.html) request to create an IAM role called *MyImportModelRole* and attach the *BedrockTrust.json* trust policy that you created.
```
aws iam create-role \
--role-name MyImportModelRole \
--assume-role-policy-document file://BedrockTrust.json
```
1. Make a [CreatePolicy](https://docs.aws.amazon.com/IAM/latest/APIReference/API_CreatePolicy.html) request to create the S3 data access policy with the *S3BucketPolicy.json* file you created. The response returns an `Arn` for the policy.
```
aws iam create-policy \
--policy-name S3BucketPolicy \
--policy-document file://S3BucketPolicy.json
```
1. Make an [AttachRolePolicy](https://docs.aws.amazon.com/IAM/latest/APIReference/API_AttachRolePolicy.html) request to attach the S3 data access policy to your role, replacing the `policy-arn` with the ARN in the response from the previous step:
```
aws iam attach-role-policy \
--role-name MyImportModelRole \
--policy-arn ${policy-arn}
```
------
#### [ Python ]
1. Run the following code to make a [CreateRole](https://docs.aws.amazon.com/IAM/latest/APIReference/API_CreateRole.html) request to create an IAM role called *MyImportModel* and to make a [CreatePolicy](https://docs.aws.amazon.com/IAM/latest/APIReference/API_CreatePolicy.html) request to create an S3 data access policy called *S3BucketPolicy*. For the S3 data access policy, replace *\$1\$1model-file-bucket\$1* with your S3 bucket names.
```
import boto3
import json
iam = boto3.client("iam")
iam.create_role(
RoleName="MyImportModelRole",
AssumeRolePolicyDocument=json.dumps({
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Principal": {
"Service": "bedrock.amazonaws.com"
},
"Action": "sts:AssumeRole"
}
]
})
)
iam.create_policy(
PolicyName="S3BucketPolicy",
PolicyDocument=json.dumps({
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"s3:GetObject",
"s3:ListBucket"
],
"Resource": [
"arn:aws:s3:::${training-bucket}",
"arn:aws:s3:::${training-bucket}/*"
]
}
]
})
)
```
1. An `Arn` is returned in the response. Run the following code snippet to make an [AttachRolePolicy](https://docs.aws.amazon.com/IAM/latest/APIReference/API_AttachRolePolicy.html) request, replacing *\$1\$1policy-arn\$1* with the returned `Arn`.
```
iam.attach_role_policy(
RoleName="MyImportModelRole",
PolicyArn="${policy-arn}"
)
```
------
1. Select a language to see code samples to call the custom model import API operations.
------
#### [ CLI ]
To submit a custom model import job, in a terminal run the following command in the command line, replacing *\$1\$1my-import-model-role-arn\$1* with the model role that you set up and the *s3-bucket-path* with the S3 bucket path of your model files.
```
aws bedrock create-model-import-job
--job-name MyImportedModelJobName
--imported-model-name MyImportedModelName
--role-arn ${my-import-model-role-arn}
--model-data-source '{"s3DataSource": {"s3Uri": s3-bucket-path }}
```
The response returns a *jobArn*. The custom import job will take some time to complete. You can use the `jobArn` with the following command to check the status of the import job.
The following fields are optional:
+ To add a VPC configuration, add the following argument to the above command to specify the security group and subnets:
```
-\\-vpc-config '{securityGroupIds": ["sg-xx"], "subnetIds": ["subnet-yy", "subnet-zz"]}'
```
+ To encrypt your model with a KMS key, add the following argument to the above command, replacing the values to specify the key with which you want to encrypt your model.
```
-\\-customModelKmsKeyId 'arn:aws:kms:region:account-id:key/key-id'
```
+ To add tags, add the following argument to the above command, replacing the keys and values with the tags you want to attach to the job and/or output model and making sure to separate key/value pairs with a space:
```
-\\-tags key=key1,value=value1 key=key2,value=value2
```
The response returns a *jobArn*. The custom import job will take some time to complete. You can use the `jobArn` with the following command to check the status of the import job.
```
aws bedrock get-model-import-job \
--job-identifier "jobArn"
```
The response looks similar to this:
```
{
"jobArn": ${job-arn} ,
"jobName": MyImportedModelJobName,
"importedModelName": MyImportedModelName,
"roleArn": ${my-role-arn},
"modelDataSource": {
"s3DataSource": {
"s3Uri": "${S3Uri}"
}
},
"status": "Complete",
"creationTime": "2024-08-13T23:38:42.457Z",
"lastModifiedTime": "2024-08-13T23:39:25.158Z"
```
When the `status` is `Complete`. the import job is complete.
To run inference on your newly imported model, you must provide the ARN of the imported model as the `model-id`. Get ARN of the imported model.
```
aws bedrock list-imported-models
```
The response contains the model name and the model ARN. Use the model ARN to invoke the imported model. For more information, see [Submit a single prompt with InvokeModel](inference-invoke.md).
```
{
"modelSummaries": [
{
"modelArn": model-arn,
"modelName": "MyImportedModelName",
"modelArchitecture":model-architecture,
"instructSupported":Y,
"creationTime": "2024-08-13T19:20:14.058Z"
}
]
}
```
To delete your imported model, in a terminal run the following command in the command line, using the model name or the model ARN of the imported model you want to delete.
```
aws bedrock delete-imported-model
--model-identifier MyImportedModelName
```
------
#### [ Python ]
Run the following code snippet to submit an custom model import job. Replace *my-region* with the Region where you imported the model, *\$1\$1my-import-model-role-arn\$1* with the ARN of the *MyImportModelRole* that you set up and replace *\$1\$1model-file-bucket\$1* with your S3 bucket name.
```
import boto3
import json
REGION_NAME = my-region
bedrock = boto3.client(service_name='bedrock',
region_name=REGION_NAME)
JOB_NAME = MyImportedModelJobName
ROLE_ARN = ${my-import-model-role-arn}
IMPORTED_MODEL_NAME = ImportedModelName
S3_URI = ${S3Uri}
# createModelImportJob API
create_job_response = bedrock.create_model_import_job(
jobName=JOB_NAME,
importedModelName=IMPORTED_MODEL_NAME,
roleArn=ROLE_ARN,
modelDataSource={
"s3DataSource": {
"s3Uri": S3_URI
}
},
)
job_arn = create_job_response.get("jobArn")
```
The following fields are optional.
+ To add a VPC configuration, add the following argument to the above command to specify the security group and subnets:
```
vpc-config = {'securityGroupIds: ["sg-xx".], 'subnetIds': [subnet-yy, 'subnet-zz']}'
```
+ To encrypt your model with a KMS key, add the following argument to the above command, replacing the values to specify the key with which you want to encrypt your model.
```
importedModelKmsKeyId = 'arn:aws:kms:region:account-id:key/key-id'
```
+ To add tags, add the following argument to the above command, replacing the keys and values with the tags you want to attach to the job and/or output model and making sure to separate key/value pairs with a space:
```
jobTags key=key1,value=value1 key=key2,value=value2
```
The response returns a jobArn
```
job_arn = create_job_response.get("jobArn")
```
The custom import job will take some time to complete. You can use the `jobArn` with the following command to check the status of the import job.
```
bedrock.get_model_import_job(jobIdentifier=jobArn)
```
When the `status` is `Completed`, the import job is complete.
To run inference on your newly imported model, you must provide the ARN of the imported model as the `model-id`. Get ARN of the imported model.
```
response_pt = bedrock.list_imported_models(
creationTimeBefore=datetime (2015,1,1,
creationTimeAfter= datetime (2015,1,1,
nameContains = 'MyImportedModelName,
maxresults = 123
nextToken = 'none',
sortBy = 'creationTime',
sortOrder = 'Ascending'
```
The response returns the `modelArn` along with other details of the imported model.
```
{
'nextToken': '',
'modelSummaries': [
{
'modelArn': 'your-model-arn',
'modelName': 'MyImportedModelName',
'modelArchitecture':model-architecture,
'instructSupported':Y,
'creationTime': datetime(2015, 1, 1)
},
]
```
Use the model ARN to invoke the imported model. For more information, see [Submit a single prompt with InvokeModel](inference-invoke.md).
To delete your imported model, use the following command using the model name or the model ARN of the imported model you want to delete.
```
response = client.delete_imported_model(
modelIdentifier='MyImportedModelName'
)
```
------
# Converse API code samples for custom model import
If you're importing a Mistral, Llama, or Qwen type instruct model and you want to use the [Converse](https://docs.aws.amazon.com//bedrock/latest/APIReference/API_runtime_Converse.html) or the [ConverseStream](https://docs.aws.amazon.com//bedrock/latest/APIReference/API_runtime_ConverseStream.html) API, make sure to include the `chat_template` for the model type you are importing in the `tokenizer_config.json`. Select the tab corresponding to the model type you are importing in the following table.
------
#### [ Llama 3.2 Text ]
```
{
"added_tokens_decoder": {
"128000": {
"content": "<|begin_of_text|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128001": {
"content": "<|end_of_text|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128002": {
"content": "<|reserved_special_token_0|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128003": {
"content": "<|reserved_special_token_1|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128004": {
"content": "<|finetune_right_pad_id|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128005": {
"content": "<|reserved_special_token_2|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128006": {
"content": "<|start_header_id|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128007": {
"content": "<|end_header_id|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128008": {
"content": "<|eom_id|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128009": {
"content": "<|eot_id|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128010": {
"content": "<|python_tag|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128011": {
"content": "<|reserved_special_token_3|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128012": {
"content": "<|reserved_special_token_4|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128013": {
"content": "<|reserved_special_token_5|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128014": {
"content": "<|reserved_special_token_6|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128015": {
"content": "<|reserved_special_token_7|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128016": {
"content": "<|reserved_special_token_8|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128017": {
"content": "<|reserved_special_token_9|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128018": {
"content": "<|reserved_special_token_10|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128019": {
"content": "<|reserved_special_token_11|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128020": {
"content": "<|reserved_special_token_12|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
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"chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- if strftime_now is defined %}\n {%- set date_string = strftime_now(\"%d %b %Y\") %}\n {%- else %}\n {%- set date_string = \"26 Jul 2024\" %}\n {%- endif %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message #}\n{{- \"|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {{- \"<|eot_id|>\" }}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
"clean_up_tokenization_spaces": true,
"eos_token": "<|eot_id|>",
"model_input_names": [
"input_ids",
"attention_mask"
],
"model_max_length": 131072,
"tokenizer_class": "PreTrainedTokenizerFast"
}
```
------
#### [ Llama 3.1 Text ]
```
{
"added_tokens_decoder": {
"128000": {
"content": "<|begin_of_text|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128001": {
"content": "<|end_of_text|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128002": {
"content": "<|reserved_special_token_0|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128003": {
"content": "<|reserved_special_token_1|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128004": {
"content": "<|finetune_right_pad_id|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128005": {
"content": "<|reserved_special_token_2|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128006": {
"content": "<|start_header_id|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128007": {
"content": "<|end_header_id|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128008": {
"content": "<|eom_id|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128009": {
"content": "<|eot_id|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128010": {
"content": "<|python_tag|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128011": {
"content": "<|reserved_special_token_3|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128012": {
"content": "<|reserved_special_token_4|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128013": {
"content": "<|reserved_special_token_5|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128014": {
"content": "<|reserved_special_token_6|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128015": {
"content": "<|reserved_special_token_7|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128016": {
"content": "<|reserved_special_token_8|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128017": {
"content": "<|reserved_special_token_9|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128018": {
"content": "<|reserved_special_token_10|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128019": {
"content": "<|reserved_special_token_11|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128020": {
"content": "<|reserved_special_token_12|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128021": {
"content": "<|reserved_special_token_13|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128022": {
"content": "<|reserved_special_token_14|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128023": {
"content": "<|reserved_special_token_15|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128024": {
"content": "<|reserved_special_token_16|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128025": {
"content": "<|reserved_special_token_17|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128026": {
"content": "<|reserved_special_token_18|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128027": {
"content": "<|reserved_special_token_19|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128028": {
"content": "<|reserved_special_token_20|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128029": {
"content": "<|reserved_special_token_21|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128030": {
"content": "<|reserved_special_token_22|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128031": {
"content": "<|reserved_special_token_23|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128032": {
"content": "<|reserved_special_token_24|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128033": {
"content": "<|reserved_special_token_25|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128034": {
"content": "<|reserved_special_token_26|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128035": {
"content": "<|reserved_special_token_27|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128036": {
"content": "<|reserved_special_token_28|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128037": {
"content": "<|reserved_special_token_29|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128038": {
"content": "<|reserved_special_token_30|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128039": {
"content": "<|reserved_special_token_31|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128040": {
"content": "<|reserved_special_token_32|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128041": {
"content": "<|reserved_special_token_33|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128042": {
"content": "<|reserved_special_token_34|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128043": {
"content": "<|reserved_special_token_35|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128044": {
"content": "<|reserved_special_token_36|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128045": {
"content": "<|reserved_special_token_37|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128046": {
"content": "<|reserved_special_token_38|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128047": {
"content": "<|reserved_special_token_39|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128048": {
"content": "<|reserved_special_token_40|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128049": {
"content": "<|reserved_special_token_41|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128050": {
"content": "<|reserved_special_token_42|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128051": {
"content": "<|reserved_special_token_43|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128052": {
"content": "<|reserved_special_token_44|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128053": {
"content": "<|reserved_special_token_45|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128054": {
"content": "<|reserved_special_token_46|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128055": {
"content": "<|reserved_special_token_47|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128056": {
"content": "<|reserved_special_token_48|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128057": {
"content": "<|reserved_special_token_49|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128058": {
"content": "<|reserved_special_token_50|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128059": {
"content": "<|reserved_special_token_51|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128060": {
"content": "<|reserved_special_token_52|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128061": {
"content": "<|reserved_special_token_53|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128062": {
"content": "<|reserved_special_token_54|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128063": {
"content": "<|reserved_special_token_55|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128064": {
"content": "<|reserved_special_token_56|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128065": {
"content": "<|reserved_special_token_57|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128066": {
"content": "<|reserved_special_token_58|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128067": {
"content": "<|reserved_special_token_59|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128068": {
"content": "<|reserved_special_token_60|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128069": {
"content": "<|reserved_special_token_61|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128070": {
"content": "<|reserved_special_token_62|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128071": {
"content": "<|reserved_special_token_63|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128072": {
"content": "<|reserved_special_token_64|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128073": {
"content": "<|reserved_special_token_65|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128074": {
"content": "<|reserved_special_token_66|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128075": {
"content": "<|reserved_special_token_67|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128076": {
"content": "<|reserved_special_token_68|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128077": {
"content": "<|reserved_special_token_69|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128078": {
"content": "<|reserved_special_token_70|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128079": {
"content": "<|reserved_special_token_71|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128080": {
"content": "<|reserved_special_token_72|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128081": {
"content": "<|reserved_special_token_73|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128082": {
"content": "<|reserved_special_token_74|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128083": {
"content": "<|reserved_special_token_75|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128084": {
"content": "<|reserved_special_token_76|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128085": {
"content": "<|reserved_special_token_77|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128086": {
"content": "<|reserved_special_token_78|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128087": {
"content": "<|reserved_special_token_79|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128088": {
"content": "<|reserved_special_token_80|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128089": {
"content": "<|reserved_special_token_81|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128090": {
"content": "<|reserved_special_token_82|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128091": {
"content": "<|reserved_special_token_83|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128092": {
"content": "<|reserved_special_token_84|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128093": {
"content": "<|reserved_special_token_85|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128094": {
"content": "<|reserved_special_token_86|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128095": {
"content": "<|reserved_special_token_87|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128096": {
"content": "<|reserved_special_token_88|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128097": {
"content": "<|reserved_special_token_89|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128098": {
"content": "<|reserved_special_token_90|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128099": {
"content": "<|reserved_special_token_91|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128100": {
"content": "<|reserved_special_token_92|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128101": {
"content": "<|reserved_special_token_93|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128102": {
"content": "<|reserved_special_token_94|>",
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"content": "<|reserved_special_token_220|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128229": {
"content": "<|reserved_special_token_221|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128230": {
"content": "<|reserved_special_token_222|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128231": {
"content": "<|reserved_special_token_223|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128232": {
"content": "<|reserved_special_token_224|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128233": {
"content": "<|reserved_special_token_225|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128234": {
"content": "<|reserved_special_token_226|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128235": {
"content": "<|reserved_special_token_227|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128236": {
"content": "<|reserved_special_token_228|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128237": {
"content": "<|reserved_special_token_229|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128238": {
"content": "<|reserved_special_token_230|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128239": {
"content": "<|reserved_special_token_231|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128240": {
"content": "<|reserved_special_token_232|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128241": {
"content": "<|reserved_special_token_233|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128242": {
"content": "<|reserved_special_token_234|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128243": {
"content": "<|reserved_special_token_235|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128244": {
"content": "<|reserved_special_token_236|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128245": {
"content": "<|reserved_special_token_237|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128246": {
"content": "<|reserved_special_token_238|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128247": {
"content": "<|reserved_special_token_239|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128248": {
"content": "<|reserved_special_token_240|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128249": {
"content": "<|reserved_special_token_241|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128250": {
"content": "<|reserved_special_token_242|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128251": {
"content": "<|reserved_special_token_243|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128252": {
"content": "<|reserved_special_token_244|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128253": {
"content": "<|reserved_special_token_245|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128254": {
"content": "<|reserved_special_token_246|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128255": {
"content": "<|reserved_special_token_247|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
}
},
"bos_token": "<|begin_of_text|>",
"chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n {%- for arg_name, arg_val in tool_call.arguments | items %}\n {{- arg_name + '=\"' + arg_val + '\"' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \")\" }}\n {%- else %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {%- endif %}\n {%- if builtin_tools is defined %}\n {#- This means we're in ipython mode #}\n {{- \"<|eom_id|>\" }}\n {%- else %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
"clean_up_tokenization_spaces": true,
"eos_token": "<|eot_id|>",
"model_input_names": [
"input_ids",
"attention_mask"
],
"model_max_length": 131072,
"tokenizer_class": "PreTrainedTokenizerFast"
}
```
------
#### [ Llama 3.0 Text ]
```
{
"added_tokens_decoder": {
"128000": {
"content": "<|begin_of_text|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128001": {
"content": "<|end_of_text|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128002": {
"content": "<|reserved_special_token_0|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128003": {
"content": "<|reserved_special_token_1|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128004": {
"content": "<|reserved_special_token_2|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128005": {
"content": "<|reserved_special_token_3|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128006": {
"content": "<|start_header_id|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128007": {
"content": "<|end_header_id|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128008": {
"content": "<|reserved_special_token_4|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128009": {
"content": "<|eot_id|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128010": {
"content": "<|reserved_special_token_5|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128011": {
"content": "<|reserved_special_token_6|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128012": {
"content": "<|reserved_special_token_7|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128013": {
"content": "<|reserved_special_token_8|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128014": {
"content": "<|reserved_special_token_9|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128015": {
"content": "<|reserved_special_token_10|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128016": {
"content": "<|reserved_special_token_11|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128017": {
"content": "<|reserved_special_token_12|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128018": {
"content": "<|reserved_special_token_13|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128019": {
"content": "<|reserved_special_token_14|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128020": {
"content": "<|reserved_special_token_15|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128021": {
"content": "<|reserved_special_token_16|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128022": {
"content": "<|reserved_special_token_17|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128023": {
"content": "<|reserved_special_token_18|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128024": {
"content": "<|reserved_special_token_19|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128025": {
"content": "<|reserved_special_token_20|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128026": {
"content": "<|reserved_special_token_21|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128027": {
"content": "<|reserved_special_token_22|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128028": {
"content": "<|reserved_special_token_23|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128029": {
"content": "<|reserved_special_token_24|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128030": {
"content": "<|reserved_special_token_25|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128031": {
"content": "<|reserved_special_token_26|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128032": {
"content": "<|reserved_special_token_27|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128033": {
"content": "<|reserved_special_token_28|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128034": {
"content": "<|reserved_special_token_29|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128035": {
"content": "<|reserved_special_token_30|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128036": {
"content": "<|reserved_special_token_31|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128037": {
"content": "<|reserved_special_token_32|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128038": {
"content": "<|reserved_special_token_33|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128039": {
"content": "<|reserved_special_token_34|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128040": {
"content": "<|reserved_special_token_35|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128041": {
"content": "<|reserved_special_token_36|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128042": {
"content": "<|reserved_special_token_37|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128043": {
"content": "<|reserved_special_token_38|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128044": {
"content": "<|reserved_special_token_39|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128045": {
"content": "<|reserved_special_token_40|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128046": {
"content": "<|reserved_special_token_41|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128047": {
"content": "<|reserved_special_token_42|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128048": {
"content": "<|reserved_special_token_43|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128049": {
"content": "<|reserved_special_token_44|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128050": {
"content": "<|reserved_special_token_45|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128051": {
"content": "<|reserved_special_token_46|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128052": {
"content": "<|reserved_special_token_47|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128053": {
"content": "<|reserved_special_token_48|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128054": {
"content": "<|reserved_special_token_49|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128055": {
"content": "<|reserved_special_token_50|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128056": {
"content": "<|reserved_special_token_51|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128057": {
"content": "<|reserved_special_token_52|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128058": {
"content": "<|reserved_special_token_53|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128059": {
"content": "<|reserved_special_token_54|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128060": {
"content": "<|reserved_special_token_55|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128061": {
"content": "<|reserved_special_token_56|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128062": {
"content": "<|reserved_special_token_57|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128063": {
"content": "<|reserved_special_token_58|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128064": {
"content": "<|reserved_special_token_59|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128065": {
"content": "<|reserved_special_token_60|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128066": {
"content": "<|reserved_special_token_61|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128067": {
"content": "<|reserved_special_token_62|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128068": {
"content": "<|reserved_special_token_63|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128069": {
"content": "<|reserved_special_token_64|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128070": {
"content": "<|reserved_special_token_65|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
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"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128197": {
"content": "<|reserved_special_token_192|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128198": {
"content": "<|reserved_special_token_193|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128199": {
"content": "<|reserved_special_token_194|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128200": {
"content": "<|reserved_special_token_195|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128201": {
"content": "<|reserved_special_token_196|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128202": {
"content": "<|reserved_special_token_197|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128203": {
"content": "<|reserved_special_token_198|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128204": {
"content": "<|reserved_special_token_199|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128205": {
"content": "<|reserved_special_token_200|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128206": {
"content": "<|reserved_special_token_201|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128207": {
"content": "<|reserved_special_token_202|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128208": {
"content": "<|reserved_special_token_203|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128209": {
"content": "<|reserved_special_token_204|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128210": {
"content": "<|reserved_special_token_205|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128211": {
"content": "<|reserved_special_token_206|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128212": {
"content": "<|reserved_special_token_207|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128213": {
"content": "<|reserved_special_token_208|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128214": {
"content": "<|reserved_special_token_209|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128215": {
"content": "<|reserved_special_token_210|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128216": {
"content": "<|reserved_special_token_211|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128217": {
"content": "<|reserved_special_token_212|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128218": {
"content": "<|reserved_special_token_213|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128219": {
"content": "<|reserved_special_token_214|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128220": {
"content": "<|reserved_special_token_215|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128221": {
"content": "<|reserved_special_token_216|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128222": {
"content": "<|reserved_special_token_217|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128223": {
"content": "<|reserved_special_token_218|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128224": {
"content": "<|reserved_special_token_219|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128225": {
"content": "<|reserved_special_token_220|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128226": {
"content": "<|reserved_special_token_221|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128227": {
"content": "<|reserved_special_token_222|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128228": {
"content": "<|reserved_special_token_223|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128229": {
"content": "<|reserved_special_token_224|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128230": {
"content": "<|reserved_special_token_225|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128231": {
"content": "<|reserved_special_token_226|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128232": {
"content": "<|reserved_special_token_227|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128233": {
"content": "<|reserved_special_token_228|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128234": {
"content": "<|reserved_special_token_229|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128235": {
"content": "<|reserved_special_token_230|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128236": {
"content": "<|reserved_special_token_231|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128237": {
"content": "<|reserved_special_token_232|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128238": {
"content": "<|reserved_special_token_233|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128239": {
"content": "<|reserved_special_token_234|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128240": {
"content": "<|reserved_special_token_235|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128241": {
"content": "<|reserved_special_token_236|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128242": {
"content": "<|reserved_special_token_237|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128243": {
"content": "<|reserved_special_token_238|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128244": {
"content": "<|reserved_special_token_239|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128245": {
"content": "<|reserved_special_token_240|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128246": {
"content": "<|reserved_special_token_241|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128247": {
"content": "<|reserved_special_token_242|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128248": {
"content": "<|reserved_special_token_243|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128249": {
"content": "<|reserved_special_token_244|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128250": {
"content": "<|reserved_special_token_245|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128251": {
"content": "<|reserved_special_token_246|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128252": {
"content": "<|reserved_special_token_247|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128253": {
"content": "<|reserved_special_token_248|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128254": {
"content": "<|reserved_special_token_249|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"128255": {
"content": "<|reserved_special_token_250|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
}
},
"bos_token": "<|begin_of_text|>",
"chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}",
"clean_up_tokenization_spaces": true,
"eos_token": "<|eot_id|>",
"model_input_names": [
"input_ids",
"attention_mask"
],
"model_max_length": 1000000000000000019884624838656,
"tokenizer_class": "PreTrainedTokenizerFast"
}
```
------
#### [ Llama 2.0 Chat models ]
```
{
"add_bos_token": true,
"add_eos_token": false,
"bos_token": {
"__type": "AddedToken",
"content": "",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"chat_template": "{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<>\\n' + system_message + '\\n<>\\n\\n' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ ' ' + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}",
"clean_up_tokenization_spaces": false,
"eos_token": {
"__type": "AddedToken",
"content": "",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
},
"legacy": false,
"model_max_length": 1000000000000000019884624838656,
"pad_token": null,
"padding_side": "right",
"sp_model_kwargs": {},
"tokenizer_class": "LlamaTokenizer",
"unk_token": {
"__type": "AddedToken",
"content": "",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false
}
}
```
------
#### [ Mistral 8x7b Text ]
```
{
"add_bos_token": true,
"add_eos_token": false,
"add_prefix_space": null,
"added_tokens_decoder": {
"0": {
"content": "",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"1": {
"content": "",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"2": {
"content": "",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
}
},
"additional_special_tokens": [],
"bos_token": "",
"chat_template": "{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content'] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set loop_messages = messages %}\n{%- endif %}\n\n{{- bos_token }}\n{%- for message in loop_messages %}\n {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}\n {{- raise_exception('After the optional system message, conversation roles must alternate user/assistant/user/assistant/...') }}\n {%- endif %}\n {%- if message['role'] == 'user' %}\n {%- if loop.first and system_message is defined %}\n {{- ' [INST] ' + system_message + '\\n\\n' + message['content'] + ' [/INST]' }}\n {%- else %}\n {{- ' [INST] ' + message['content'] + ' [/INST]' }}\n {%- endif %}\n {%- elif message['role'] == 'assistant' %}\n {{- ' ' + message['content'] + eos_token}}\n {%- else %}\n {{- raise_exception('Only user and assistant roles are supported, with the exception of an initial optional system message!') }}\n {%- endif %}\n{%- endfor %}\n",
"clean_up_tokenization_spaces": false,
"eos_token": "",
"legacy": false,
"model_max_length": 1000000000000000019884624838656,
"pad_token": null,
"sp_model_kwargs": {},
"spaces_between_special_tokens": false,
"tokenizer_class": "LlamaTokenizer",
"unk_token": "",
"use_default_system_prompt": false
}
```
------
#### [ Mistral 7b ]
```
{
"add_bos_token": true,
"add_eos_token": false,
"add_prefix_space": true,
"added_tokens_decoder": {
"0": {
"content": "",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"1": {
"content": "",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"2": {
"content": "",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"3": {
"content": "[INST]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"4": {
"content": "[/INST]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"5": {
"content": "[TOOL_CALLS]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"6": {
"content": "[AVAILABLE_TOOLS]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"7": {
"content": "[/AVAILABLE_TOOLS]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"8": {
"content": "[TOOL_RESULTS]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"9": {
"content": "[/TOOL_RESULTS]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"10": {
"content": "[control_8]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"11": {
"content": "[control_9]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"12": {
"content": "[control_10]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"13": {
"content": "[control_11]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"14": {
"content": "[control_12]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"15": {
"content": "[control_13]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"16": {
"content": "[control_14]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"17": {
"content": "[control_15]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"18": {
"content": "[control_16]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"19": {
"content": "[control_17]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"20": {
"content": "[control_18]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"21": {
"content": "[control_19]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"22": {
"content": "[control_20]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"23": {
"content": "[control_21]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"24": {
"content": "[control_22]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"25": {
"content": "[control_23]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"26": {
"content": "[control_24]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"27": {
"content": "[control_25]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"28": {
"content": "[control_26]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"29": {
"content": "[control_27]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"30": {
"content": "[control_28]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"31": {
"content": "[control_29]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"32": {
"content": "[control_30]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"33": {
"content": "[control_31]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"34": {
"content": "[control_32]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"35": {
"content": "[control_33]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"36": {
"content": "[control_34]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"37": {
"content": "[control_35]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"38": {
"content": "[control_36]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"39": {
"content": "[control_37]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"40": {
"content": "[control_38]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"41": {
"content": "[control_39]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"42": {
"content": "[control_40]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"43": {
"content": "[control_41]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"44": {
"content": "[control_42]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"45": {
"content": "[control_43]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
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"single_word": false,
"special": true
},
"750": {
"content": "[control_748]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"751": {
"content": "[control_749]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"752": {
"content": "[control_750]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"753": {
"content": "[control_751]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"754": {
"content": "[control_752]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"755": {
"content": "[control_753]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"756": {
"content": "[control_754]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"757": {
"content": "[control_755]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"758": {
"content": "[control_756]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"759": {
"content": "[control_757]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"760": {
"content": "[control_758]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"761": {
"content": "[control_759]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"762": {
"content": "[control_760]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"763": {
"content": "[control_761]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"764": {
"content": "[control_762]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"765": {
"content": "[control_763]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"766": {
"content": "[control_764]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"767": {
"content": "[control_765]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"768": {
"content": "[control_766]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"769": {
"content": "[control_767]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"770": {
"content": "[control_768]",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
}
},
"bos_token": "",
"chat_template": "{%- if messages[0][\"role\"] == \"system\" %}\n {%- set system_message = messages[0][\"content\"] %}\n {%- set loop_messages = messages[1:] %}\n{%- else %}\n {%- set loop_messages = messages %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n{%- set user_messages = loop_messages | selectattr(\"role\", \"equalto\", \"user\") | list %}\n\n{#- This block checks for alternating user/assistant messages, skipping tool calling messages #}\n{%- set ns = namespace() %}\n{%- set ns.index = 0 %}\n{%- for message in loop_messages %}\n {%- if not (message.role == \"tool\" or message.role == \"tool_results\" or (message.tool_calls is defined and message.tool_calls is not none)) %}\n {%- if (message[\"role\"] == \"user\") != (ns.index % 2 == 0) %}\n {{- raise_exception(\"After the optional system message, conversation roles must alternate user/assistant/user/assistant/...\") }}\n {%- endif %}\n {%- set ns.index = ns.index + 1 %}\n {%- endif %}\n{%- endfor %}\n\n{{- bos_token }}\n{%- for message in loop_messages %}\n {%- if message[\"role\"] == \"user\" %}\n {%- if tools is not none and (message == user_messages[-1]) %}\n {{- \"[AVAILABLE_TOOLS] [\" }}\n {%- for tool in tools %}\n {%- set tool = tool.function %}\n {{- '{\"type\": \"function\", \"function\": {' }}\n {%- for key, val in tool.items() if key != \"return\" %}\n {%- if val is string %}\n {{- '\"' + key + '\": \"' + val + '\"' }}\n {%- else %}\n {{- '\"' + key + '\": ' + val|tojson }}\n {%- endif %}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \"}}\" }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" }}\n {%- endif %}\n {%- endfor %}\n {{- \"[/AVAILABLE_TOOLS]\" }}\n {%- endif %}\n {%- if loop.last and system_message is defined %}\n {{- \"[INST] \" + system_message + \"\\n\\n\" + message[\"content\"] + \"[/INST]\" }}\n {%- else %}\n {{- \"[INST] \" + message[\"content\"] + \"[/INST]\" }}\n {%- endif %}\n {%- elif message.tool_calls is defined and message.tool_calls is not none %}\n {{- \"[TOOL_CALLS] [\" }}\n {%- for tool_call in message.tool_calls %}\n {%- set out = tool_call.function|tojson %}\n {{- out[:-1] }}\n {%- if not tool_call.id is defined or tool_call.id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- ', \"id\": \"' + tool_call.id + '\"}' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- else %}\n {{- \"]\" + eos_token }}\n {%- endif %}\n {%- endfor %}\n {%- elif message[\"role\"] == \"assistant\" %}\n {{- \" \" + message[\"content\"]|trim + eos_token}}\n {%- elif message[\"role\"] == \"tool_results\" or message[\"role\"] == \"tool\" %}\n {%- if message.content is defined and message.content.content is defined %}\n {%- set content = message.content.content %}\n {%- else %}\n {%- set content = message.content %}\n {%- endif %}\n {{- '[TOOL_RESULTS] {\"content\": ' + content|string + \", \" }}\n {%- if not message.tool_call_id is defined or message.tool_call_id|length != 9 %}\n {{- raise_exception(\"Tool call IDs should be alphanumeric strings with length 9!\") }}\n {%- endif %}\n {{- '\"call_id\": \"' + message.tool_call_id + '\"}[/TOOL_RESULTS]' }}\n {%- else %}\n {{- raise_exception(\"Only user and assistant roles are supported, with the exception of an initial optional system message!\") }}\n {%- endif %}\n{%- endfor %}\n",
"clean_up_tokenization_spaces": false,
"eos_token": "",
"legacy": false,
"model_max_length": 1000000000000000019884624838656,
"pad_token": null,
"sp_model_kwargs": {},
"spaces_between_special_tokens": false,
"tokenizer_class": "LlamaTokenizer",
"unk_token": "",
"use_default_system_prompt": false
}
```
------
#### [ Qwen2 ]
```
{
"add_prefix_space": false,
"added_tokens_decoder": {
"151643": {
"content": "<|endoftext|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151644": {
"content": "<|im_start|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151645": {
"content": "<|im_end|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
}
},
"additional_special_tokens": ["<|im_start|>", "<|im_end|>"],
"bos_token": null,
"chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
"clean_up_tokenization_spaces": false,
"eos_token": "<|im_end|>",
"errors": "replace",
"model_max_length": 131072,
"pad_token": "<|endoftext|>",
"split_special_tokens": false,
"tokenizer_class": "Qwen2Tokenizer",
"unk_token": null
}
```
------