Models in BMC HelixGPT
A model is a program or algorithm that relies on training data to recognize patterns and make predictions. BMC HelixGPT supports different vendors and models. For more information about vendors and models, see Supported models in BMC HelixGPT.
BMC HelixGPT uses Llama3, a self-hosted solution containing the weights and configuration files required to create inferences.
Inferencing and training
Inferencing is the main task of a model, resulting in the generation of chat and summarization. Inferencing occurs in the following way:
Data fed to the model includes example outputs, and the model is adjusted or fine-tuned regularly.
The model training is accomplished in the following methods:
- BMC trains the model based on generic, non-sensitive data and fine-tunes the application use cases, which are not specific to any customer. For example, we provide a global prompt that establishes the tone and expectation for all responses.
- Customers train the model by using their private data where the output is a model that is specific to the customer.
- AISM use cases depend on ready-made models, and you can also train the model as per your requirements.
Inference service
The inference service deploys the model and exposes an API through a network endpoint. BMC Helix applications use this service at runtime during user interaction. The inference service is available in the following categories:
- Subscribed service: A subscribed service is a generalized, large model service where you purchase access and get an API key from a third-party provider, such as Microsoft or Azure OpenAI. The vendor hosts and runs the service. BMC integrates with AI providers through REST APIs. BMC supports the Azure OpenAI provider.
- Self-hosted service: A self-hosted service runs on one of the AI/ML platforms offered by the three major cloud vendors: Google Cloud Platform Vertex, Amazon Web Service Bedrock, and Azure ML. These platforms require the customer to be a Google Cloud Platform (GCP), Amazon Web Services (AWS), or Microsoft Azure customer. BMC supports Vertex AI on Google Cloud Platform.
Supported models
The following providers and their models are supported out of the box to create skills and prompts.
All the supported models are authenticated by using an API key. You must specify the API keys while provisioning the AI provider in HelixGPT Agent Studio. Learn more about configuring the AI provider in Provisioning-and-setting-up-the-generative-AI-provider-for-your-application.
| LLM provider | LLM host | Model name | API version | Description |
|---|---|---|---|---|
OpenAI
| MS Azure
| GPT-4.1 | 2024-10-21 | gpt-4.1 (2025-04-14) |
GPT-4.1 mini | 2024-10-21 | gpt-4.1-mini (2025-04-14) | ||
OpenAI | GPT-4.1 | 2024-10-21 | gpt-4.1-2025-04-14 | |
Google Vertex AI | Gemini 2.5 Flash |
| gemini-2.5-flash | |
Meta
| Google Vertex AI | Llama 4 |
| publishers/meta/models/llama-4-maverick-17b-128e-instruct-maas? |
Amazon Bedrock | Llama 4 | Converse API | Llama 4 Maverick |
Details about the default configuration parameters
The following table lists the description of the parameters used in the model configuration. For more information about the parameters, see Azure OpenAI Service REST API reference and Reproducible output support.
Parameter | Description |
|---|---|
temperature | Controls the randomness of the text generated by the model. A lower value generates a deterministic output and a higher value generates a randomized output. The default value is 0.0. |
apiType | Specifies the API type of the AI provider. For example, azure_ad. |
deploymentName | Specifies the deployment name of the model. For example, se-gpt-4-turbo |
top_p | An alternative to sampling with temperature, this parameter considers the results of the tokens with top_p probability mass. The default value is 0.1. A value of 0.1 means the tokens comprising the top 10% probability mass are considered. |
suppportsJsonResponse | When the value is set to true, returns a valid JSON response as an output. |
read_timeout_in_seconds | Specifies the time in seconds after which the model times out. The default value is 300 seconds. |
question_max_retry | Specifies the number of times the model reattempts to respond. The default value is 3. |
seed | (Optional) Controls the reproducibility of the response, such that repeated requests with the same seed value return the same result. |
Models supported by BMC Helix Digital Workplace
BMC Helix Digital Workplace supports the following models:
Models supported by BMC Helix Innovation Suite
BMC Helix Innovation Suite supports the following models:
Models supported by BMC Helix Business Workflows
BMC Helix Business Workflows supports the following models:
Models supported by BMC Helix ITSM
BMC Helix ITSM supports the following models:
Models supported by BMC Helix ITSM: Service Desk
BMC Helix ITSM: Service Desk supports the following models:
Models supported by BMC Helix Knowledge Management
BMC Helix Knowledge Management supports the following models:
Models supported by BMC Helix Dashboards
BMC Helix Dashboards supports the following models:
Models supported by BMC Helix AIOps
BMC Helix AIOps supports the following models:
Related topics