Introduction
With Red Hat AI InstructLab, you can use an existing, pre-trained LLM compiled by a community of contributors, and then generate the data to further train the model. By incorporating IBM Cloud, you have a place to store the taxonomy, which is the informational structure, for the model as you modify and train it on an ongoing basis.
Error handling
This API uses standard HTTP response codes to indicate whether a method completed successfully. A 200 response indicates success. A 400 type response is some sort of failure, and a 500 type response usually indicates an internal system error.
| HTTP error code | Description | Recovery |
|---|---|---|
200 |
Success | The request was successful. |
400 |
Bad Request | The input parameters in the request body are either incomplete or in the wrong format. Include all required parameters in your request. |
401 |
Unauthorized | You are not authorized to make this request. Log in to IBM Cloud and try again. If this error persists, contact the account owner to check your permissions. |
403 |
Forbidden | The supplied authentication is not authorized to access the resource. Check that you have the correct access credentials and permissions. |
404 |
Not Found | The requested resource could not be found. |
408 |
Request Timeout | The connection to the server timed out. Wait a few minutes, then try again. |
409 |
Conflict | The resource with the same name or ID already exists. |
500 |
Internal Server Error | The service is currently unavailable. Your request could not be processed. Wait a few minutes and try again. If you still encounter this problem, note the incident ID and contact IBM Cloud support. |
Methods
Request
Taxonomy resource
The name you want to give your taxonomy.
Possible values: 1 ≤ length ≤ 32, Value must match regular expression
^[a-zA-Z0-9]([_-]?[a-zA-Z0-9]+)*$Example:
example-taxonomy-1The path in your Cloud Object Storage bucket where you want to store your taxonomy. By default, taxonomies are stored at 'taxonomies/taxonomy-<current_time>.tar.gz'.
Possible values: 20 ≤ length ≤ 100, Value must match regular expression
^([-_./a-zA-Z0-9][a-zA-Z0-9]+)*[^/]$Example:
taxonomies/taxonomy.tar.gzCOS bucket information
Secrets Manager configuration details.
Response
Taxonomy object
The unique identifier of a taxonomy.
Possible values: length = 36, Value must match regular expression
^[a-z0-9-]+$Example:
202a03c4-dcf1-432a-82b7-abecb2e019f7Your InstructLab project ID. You can find your project ID in the IBM Cloud console.
Possible values: length = 36, Value must match regular expression
^[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}$Example:
917bc95a-fef0-4039-b936-e0b6fb17b721The name of a taxonomy.
Possible values: 1 ≤ length ≤ 32, Value must match regular expression
^[a-zA-Z0-9]([_-]?[a-zA-Z0-9]+)*$Example:
example-taxonomy-name-1The path in your Cloud Object Storage bucket where you want to store your taxonomy. By default, taxonomies are stored at 'taxonomies/taxonomy-<current_time>.tar.gz'.
Possible values: 5 ≤ length ≤ 800, Value must match regular expression
^([-_./a-zA-Z0-9][a-zA-Z0-9]+)*[^/]$Example:
taxonomies/taxonomy.tar.gzThe timestamp that the taxonomy resource was created.
Possible values: 20 ≤ length ≤ 100, Value must match regular expression
^\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}(\.\d+)?Z$Example:
2024-10-23T02:58:50.000ZHref for the response.
Possible values: 1 ≤ length ≤ 500, Value must match regular expression
^https?:\/\/.+\/[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12}$Example:
https://us-east.instructlab.ibm.com/v1/projects/baa8cfb5-e306-4e15-869d-735b74b1919d/taxonomies/202a03c4-dcf1-432a-82b7-abecb2e019f7The estimated token count for each of the generated knowledge and skill samples needed to train the model.
COS bucket information
Secrets Manager Configuration details.
Status Code
Created
Bad Request
Unauthorized
Forbidden
{ "id": "202a03c4-dcf1-432a-82b7-abecb2e019f7", "project_id": "bdcd0181-7f5d-4bfb-be54-ef2af8cf7a35", "name": "example-taxonomy-name-1", "taxonomy_path_cos": "taxonomies/taxonomy.tar.gz", "created_at": "2024-10-23T02:58:50.000Z", "tokens": { "data_tokens_estimated": 1000000 }, "cos_bucket_information": { "bucket": "example-bucket-1", "endpoint": "https://s3.us-east.cloud-object-storage.appdomain.cloud" }, "secrets_manager_config": { "url": "https://12345678-abcd-1234-5678-abcdefghijkl.us-east.secrets-manager.appdomain.cloud", "git_id": "d9428888-122b-11e1-b85c-61cd3cbb3210" }, "href": "https://us-east.instructlab.ibm.com/v1/projects/baa8cfb5-e306-4e15-869d-735b74b1919d/taxonomies/202a03c4-dcf1-432a-82b7-abecb2e019f7" }{ "errors": [ { "id": "EQI6N2", "code": "invalid_request", "message": "Invalid request.", "more_info": "", "target": { "type": "field", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 400 }{ "errors": [ { "id": "E5P4UX", "code": "unauthorized", "message": "Unauthorized.", "more_info": "", "target": { "type": "header", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 401 }{ "errors": [ { "id": "EDKHR1", "code": "forbidden", "message": "Forbidden.", "more_info": "", "target": { "type": "header", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 403 }
Response
List the taxonomies in your IntructLab project.
List the taxonomies in your IntructLab project.
Possible values: 0 ≤ number of items ≤ 500
Status Code
OK
Bad Request
Unauthorized
Forbidden
{ "taxonomies": [ { "id": "202a03c4-dcf1-432a-82b7-abecb2e019f7", "project_id": "bdcd0181-7f5d-4bfb-be54-ef2af8cf7a35", "name": "example-taxonomy-name-1", "taxonomy_path_cos": "taxonomies/taxonomy.tar.gz", "created_at": "2024-10-23T02:58:50.000Z", "href": "https://us-east.instructlab.ibm.com/v1/projects/baa8cfb5-e306-4e15-869d-735b74b1919d/taxonomies/202a03c4-dcf1-432a-82b7-abecb2e019f7" } ] }{ "errors": [ { "id": "EQI6N2", "code": "invalid_request", "message": "Invalid request.", "more_info": "", "target": { "type": "field", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 400 }{ "errors": [ { "id": "E5P4UX", "code": "unauthorized", "message": "Unauthorized.", "more_info": "", "target": { "type": "header", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 401 }{ "errors": [ { "id": "EDKHR1", "code": "forbidden", "message": "Forbidden.", "more_info": "", "target": { "type": "header", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 403 }
Request
Path Parameters
Unique Identifier to a taxonomy
Possible values: length = 36, Value must match regular expression
^[a-z0-9-]+$Example:
817bc95a-fef0-4039-b936-e0b6fb17b723
Response
Taxonomy object
The unique identifier of a taxonomy.
Possible values: length = 36, Value must match regular expression
^[a-z0-9-]+$Example:
202a03c4-dcf1-432a-82b7-abecb2e019f7Your InstructLab project ID. You can find your project ID in the IBM Cloud console.
Possible values: length = 36, Value must match regular expression
^[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}$Example:
917bc95a-fef0-4039-b936-e0b6fb17b721The name of a taxonomy.
Possible values: 1 ≤ length ≤ 32, Value must match regular expression
^[a-zA-Z0-9]([_-]?[a-zA-Z0-9]+)*$Example:
example-taxonomy-name-1The path in your Cloud Object Storage bucket where you want to store your taxonomy. By default, taxonomies are stored at 'taxonomies/taxonomy-<current_time>.tar.gz'.
Possible values: 5 ≤ length ≤ 800, Value must match regular expression
^([-_./a-zA-Z0-9][a-zA-Z0-9]+)*[^/]$Example:
taxonomies/taxonomy.tar.gzThe timestamp that the taxonomy resource was created.
Possible values: 20 ≤ length ≤ 100, Value must match regular expression
^\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}(\.\d+)?Z$Example:
2024-10-23T02:58:50.000ZHref for the response.
Possible values: 1 ≤ length ≤ 500, Value must match regular expression
^https?:\/\/.+\/[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12}$Example:
https://us-east.instructlab.ibm.com/v1/projects/baa8cfb5-e306-4e15-869d-735b74b1919d/taxonomies/202a03c4-dcf1-432a-82b7-abecb2e019f7The estimated token count for each of the generated knowledge and skill samples needed to train the model.
COS bucket information
Secrets Manager Configuration details.
Status Code
OK
Bad Request
Unauthorized
Forbidden
{ "id": "202a03c4-dcf1-432a-82b7-abecb2e019f7", "project_id": "bdcd0181-7f5d-4bfb-be54-ef2af8cf7a35", "name": "example-taxonomy-name-1", "taxonomy_path_cos": "taxonomies/taxonomy.tar.gz", "created_at": "2024-10-23T02:58:50.000Z", "tokens": { "data_tokens_estimated": 1000000 }, "cos_bucket_information": { "bucket": "example-bucket-1", "endpoint": "https://s3.us-east.cloud-object-storage.appdomain.cloud" }, "secrets_manager_config": { "url": "https://12345678-abcd-1234-5678-abcdefghijkl.us-east.secrets-manager.appdomain.cloud", "git_id": "d9428888-122b-11e1-b85c-61cd3cbb3210" }, "href": "https://us-east.instructlab.ibm.com/v1/projects/baa8cfb5-e306-4e15-869d-735b74b1919d/taxonomies/202a03c4-dcf1-432a-82b7-abecb2e019f7" }{ "errors": [ { "id": "EQI6N2", "code": "invalid_request", "message": "Invalid request.", "more_info": "", "target": { "type": "field", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 400 }{ "errors": [ { "id": "E5P4UX", "code": "unauthorized", "message": "Unauthorized.", "more_info": "", "target": { "type": "header", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 401 }{ "errors": [ { "id": "EDKHR1", "code": "forbidden", "message": "Forbidden.", "more_info": "", "target": { "type": "header", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 403 }
Request
Path Parameters
Unique Identifier to a taxonomy
Possible values: length = 36, Value must match regular expression
^[a-z0-9-]+$Example:
817bc95a-fef0-4039-b936-e0b6fb17b723
Response
Status Code
No Content
Bad Request
Unauthorized
Forbidden
{ "errors": [ { "id": "EQI6N2", "code": "invalid_request", "message": "Invalid request.", "more_info": "", "target": { "type": "field", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 400 }{ "errors": [ { "id": "E5P4UX", "code": "unauthorized", "message": "Unauthorized.", "more_info": "", "target": { "type": "header", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 401 }{ "errors": [ { "id": "EDKHR1", "code": "forbidden", "message": "Forbidden.", "more_info": "", "target": { "type": "header", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 403 }
Request
Data resource
The name to give a data resource.
Possible values: 1 ≤ length ≤ 32, Value must match regular expression
^[a-zA-Z0-9]([_-]?[a-zA-Z0-9]+)*$Example:
example-data-1The taxonomy ID that was used for synthetic data generation. In model training, this value is used for validating the data ID. This value is available only after the model training job is accepted by an agent.
Possible values: length = 36, Value must match regular expression
^[a-z0-9-]+$Example:
202a03c4-dcf1-432a-82b7-abecb2e019f7
Response
Data object
Unique identifier of the synthetic data generation process.
Possible values: length = 36, Value must match regular expression
^[a-z0-9-]+$Example:
add785e6-a8c3-4f5f-ab89-c506a3f115daYour InstructLab project ID. You can find your project ID in the IBM Cloud console.
Possible values: length = 36, Value must match regular expression
^[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}$Example:
917bc95a-fef0-4039-b936-e0b6fb17b721Name of the the synthetic data generation process.
Possible values: 1 ≤ length ≤ 32, Value must match regular expression
^[a-zA-Z0-9]([_-]?[a-zA-Z0-9]+)*$Example:
example-data-1Current status of the synthetic data generation process. Can be one of "queued, running, completed, canceled, or failed".
Possible values: [
queued,running,completed,canceled,failed]The date that the synthetic data generation process started.
Possible values: 20 ≤ length ≤ 100, Value must match regular expression
^\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}(\.\d+)?Z$Example:
2024-10-23T02:58:50.000ZThe taxonomy ID that was used for synthetic data generation. In model training, this value is used for validating the data ID. This value is available only after the model training job is accepted by an agent.
Possible values: length = 36, Value must match regular expression
^[a-z0-9-]+$Example:
202a03c4-dcf1-432a-82b7-abecb2e019f7Href for the response.
Possible values: 1 ≤ length ≤ 500, Value must match regular expression
^https?:\/\/.+\/[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12}$Example:
https://us-east.instructlab.ibm.com/v1/projects/baa8cfb5-e306-4e15-869d-735b74b1919d/taxonomies/202a03c4-dcf1-432a-82b7-abecb2e019f7The "state" of the synthetic data generation process indicates the progress of the data generation task assigned to the machine (VSI performing the data generation job). This value is after the data generation job is accepted by an agent.
Possible values: 0 ≤ length ≤ 100, Value must match regular expression
^[a-z]*$The date that the synthetic data generation process complete
Possible values: 20 ≤ length ≤ 100, Value must match regular expression
^\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}(\.\d+)?Z$Example:
2024-10-23T03:58:50.00ZThe data metrics object for the synthetic data generation process includes the number of samples of the knowledge and skills created during the SDG, along with an estimate of the number of tokens needed for model training. This value is available after the data generation task is accepted by an agent (VSI).
Status Code
Accepted
Bad Request
Unauthorized
Forbidden
{ "id": "add785e6-a8c3-4f5f-ab89-c506a3f115da", "project_id": "bdcd0181-7f5d-4bfb-be54-ef2af8cf7a35", "name": "example-data-1", "state": "", "status": "queued", "created_at": "2024-10-23T02:58:50.000Z", "taxonomy_id": "202a03c4-dcf1-432a-82b7-abecb2e019f7", "data_metrics": { "samples": { "additionalProp1": 1, "additionalProp2": 2, "additionalProp3": 3 } }, "href": "https://us-east.instructlab.ibm.com/v1/projects/bdcd0181-7f5d-4bfb-be54-ef2af8cf7a35/data/add785e6-a8c3-4f5f-ab89-c506a3f115da" }{ "errors": [ { "id": "EQI6N2", "code": "invalid_request", "message": "Invalid request.", "more_info": "", "target": { "type": "field", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 400 }{ "errors": [ { "id": "E5P4UX", "code": "unauthorized", "message": "Unauthorized.", "more_info": "", "target": { "type": "header", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 401 }{ "errors": [ { "id": "EDKHR1", "code": "forbidden", "message": "Forbidden.", "more_info": "", "target": { "type": "header", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 403 }
Response
List of synthetic data generation jobs.
List of synthetic data generation jobs.
Possible values: 0 ≤ number of items ≤ 500
Status Code
OK
Bad Request
Unauthorized
Forbidden
{ "data": [ { "id": "add785e6-a8c3-4f5f-ab89-c506a3f115da", "project_id": "bdcd0181-7f5d-4bfb-be54-ef2af8cf7a35", "name": "example-data-1", "state": "", "status": "queued", "created_at": "2024-10-23T02:58:50.000Z", "completed_at": "2024-10-23T03:58:50.00Z", "taxonomy_id": "202a03c4-dcf1-432a-82b7-abecb2e019f7", "href": "https://us-east.instructlab.ibm.com/v1/projects/baa8cfb5-e306-4e15-869d-735b74b1919d/data/202a03c4-dcf1-432a-82b7-abecb2e019f7" } ] }{ "errors": [ { "id": "EQI6N2", "code": "invalid_request", "message": "Invalid request.", "more_info": "", "target": { "type": "field", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 400 }{ "errors": [ { "id": "E5P4UX", "code": "unauthorized", "message": "Unauthorized.", "more_info": "", "target": { "type": "header", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 401 }{ "errors": [ { "id": "EDKHR1", "code": "forbidden", "message": "Forbidden.", "more_info": "", "target": { "type": "header", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 403 }
Request
Path Parameters
Unique Identifier to data run
Possible values: length = 36, Value must match regular expression
^[a-z0-9-]+$Example:
817bc95a-fef0-4039-b936-e0b6fb17b723
Response
Data object
Unique identifier of the synthetic data generation process.
Possible values: length = 36, Value must match regular expression
^[a-z0-9-]+$Example:
add785e6-a8c3-4f5f-ab89-c506a3f115daYour InstructLab project ID. You can find your project ID in the IBM Cloud console.
Possible values: length = 36, Value must match regular expression
^[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}$Example:
917bc95a-fef0-4039-b936-e0b6fb17b721Name of the the synthetic data generation process.
Possible values: 1 ≤ length ≤ 32, Value must match regular expression
^[a-zA-Z0-9]([_-]?[a-zA-Z0-9]+)*$Example:
example-data-1Current status of the synthetic data generation process. Can be one of "queued, running, completed, canceled, or failed".
Possible values: [
queued,running,completed,canceled,failed]The date that the synthetic data generation process started.
Possible values: 20 ≤ length ≤ 100, Value must match regular expression
^\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}(\.\d+)?Z$Example:
2024-10-23T02:58:50.000ZThe taxonomy ID that was used for synthetic data generation. In model training, this value is used for validating the data ID. This value is available only after the model training job is accepted by an agent.
Possible values: length = 36, Value must match regular expression
^[a-z0-9-]+$Example:
202a03c4-dcf1-432a-82b7-abecb2e019f7Href for the response.
Possible values: 1 ≤ length ≤ 500, Value must match regular expression
^https?:\/\/.+\/[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12}$Example:
https://us-east.instructlab.ibm.com/v1/projects/baa8cfb5-e306-4e15-869d-735b74b1919d/taxonomies/202a03c4-dcf1-432a-82b7-abecb2e019f7The "state" of the synthetic data generation process indicates the progress of the data generation task assigned to the machine (VSI performing the data generation job). This value is after the data generation job is accepted by an agent.
Possible values: 0 ≤ length ≤ 100, Value must match regular expression
^[a-z]*$The date that the synthetic data generation process complete
Possible values: 20 ≤ length ≤ 100, Value must match regular expression
^\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}(\.\d+)?Z$Example:
2024-10-23T03:58:50.00ZThe data metrics object for the synthetic data generation process includes the number of samples of the knowledge and skills created during the SDG, along with an estimate of the number of tokens needed for model training. This value is available after the data generation task is accepted by an agent (VSI).
Status Code
OK
Bad Request
Unauthorized
Forbidden
{ "id": "add785e6-a8c3-4f5f-ab89-c506a3f115da", "project_id": "bdcd0181-7f5d-4bfb-be54-ef2af8cf7a35", "name": "example-data-1", "state": "", "status": "completed", "created_at": "2024-10-23T02:58:50.000Z", "completed_at": "2024-10-23T03:58:50.00Z", "taxonomy_id": "202a03c4-dcf1-432a-82b7-abecb2e019f7", "data_metrics": { "samples": { "additionalProp1": 1, "additionalProp2": 2, "additionalProp3": 3 }, "tokens": { "data_tokens_total": 1000000, "data_leaf_nodes": { "additionalProp1": 300000, "additionalProp2": 450000, "additionalProp3": 250000 }, "training_estimated": 50000000, "training_phases": { "phase_1_knowledge": 5000000, "phase_2_skills": 5000000 } } }, "href": "https://us-east.instructlab.ibm.com/v1/projects/baa8cfb5-e306-4e15-869d-735b74b1919d/data/202a03c4-dcf1-432a-82b7-abecb2e019f7" }{ "errors": [ { "id": "EQI6N2", "code": "invalid_request", "message": "Invalid request.", "more_info": "", "target": { "type": "field", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 400 }{ "errors": [ { "id": "E5P4UX", "code": "unauthorized", "message": "Unauthorized.", "more_info": "", "target": { "type": "header", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 401 }{ "errors": [ { "id": "EDKHR1", "code": "forbidden", "message": "Forbidden.", "more_info": "", "target": { "type": "header", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 403 }
Request
Path Parameters
Unique Identifier to data run
Possible values: length = 36, Value must match regular expression
^[a-z0-9-]+$Example:
817bc95a-fef0-4039-b936-e0b6fb17b723
Response
Status Code
No Content
Bad Request
Unauthorized
Forbidden
{ "errors": [ { "id": "EQI6N2", "code": "invalid_request", "message": "Invalid request.", "more_info": "", "target": { "type": "field", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 400 }{ "errors": [ { "id": "E5P4UX", "code": "unauthorized", "message": "Unauthorized.", "more_info": "", "target": { "type": "header", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 401 }{ "errors": [ { "id": "EDKHR1", "code": "forbidden", "message": "Forbidden.", "more_info": "", "target": { "type": "header", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 403 }
Request
Path Parameters
Unique Identifier to data run
Possible values: length = 36, Value must match regular expression
^[a-z0-9-]+$Example:
817bc95a-fef0-4039-b936-e0b6fb17b723
Response
Status Code
No Content
Bad Request
Unauthorized
Forbidden
{ "errors": [ { "id": "EQI6N2", "code": "invalid_request", "message": "Invalid request.", "more_info": "", "target": { "type": "field", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 400 }{ "errors": [ { "id": "E5P4UX", "code": "unauthorized", "message": "Unauthorized.", "more_info": "", "target": { "type": "header", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 401 }{ "errors": [ { "id": "EDKHR1", "code": "forbidden", "message": "Forbidden.", "more_info": "", "target": { "type": "header", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 403 }
Request
Model training request object.
The name to give a model resource.
Possible values: 1 ≤ length ≤ 32, Value must match regular expression
^[a-zA-Z0-9]([_-]?[a-zA-Z0-9]+)*$Example:
example-model-1The ID of the synthetic data generation (SDG) run. You can use this ID to monitor the data generation process. After SDG is complete, your can this ID to retrieve the synthetic data.
Possible values: length = 36, Value must match regular expression
^[a-z0-9-]+$Example:
add785e6-a8c3-4f5f-ab89-c506a3f115da
Response
Model Train Object. InstructLab implements a multiphase training process to incrementally improve the model's performance. This process involves first training the model with knowledge samples, and then using the newly acquired knowledge to train the model in developing additional skills.
Unique identifier of the model training process.
Possible values: length = 36, Value must match regular expression
^[a-z0-9-]+$Example:
baa8cfb5-e306-4e15-869d-735b74b1919dYour InstructLab project ID. You can find your project ID in the IBM Cloud console.
Possible values: length = 36, Value must match regular expression
^[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}$Example:
917bc95a-fef0-4039-b936-e0b6fb17b721Name of the model training process
Possible values: 1 ≤ length ≤ 32, Value must match regular expression
^[a-zA-Z0-9]([_-]?[a-zA-Z0-9]+)*$Example:
example-model-1The "state" of the model training processs indicates the progress training. This value is available after the model training job is accepted by an agent.
Possible values: 0 ≤ length ≤ 100, Value must match regular expression
^[a-z]*$Current status of the model training process. Can be one of "queued, running, completed, canceled, or failed.
Possible values: [
queued,running,completed,canceled,failed]Date that the model training process was created
Possible values: 20 ≤ length ≤ 100, Value must match regular expression
^\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}(\.\d+)?Z$Example:
2024-10-23T02:58:50.000ZThe ID of the synthetic data generation (SDG) run. You can use this ID to monitor the data generation process. After SDG is complete, your can this ID to retrieve the synthetic data.
Possible values: length = 36, Value must match regular expression
^[a-z0-9-]+$Example:
add785e6-a8c3-4f5f-ab89-c506a3f115daThe base model your want to use for training. This value is available only after the model training job is accepted by an agent.
Possible values: 0 ≤ length ≤ 100, Value must match regular expression
^[a-z0-9-]+$Example:
granite-7bThe taxonomy ID that was used for synthetic data generation. In model training, this value is used for validating the data ID. This value is available only after the model training job is accepted by an agent.
Possible values: length = 36, Value must match regular expression
^[a-z0-9-]+$Example:
202a03c4-dcf1-432a-82b7-abecb2e019f7Href for the response.
Possible values: 1 ≤ length ≤ 500, Value must match regular expression
^https?:\/\/.+\/[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12}$Example:
https://us-east.instructlab.ibm.com/v1/projects/baa8cfb5-e306-4e15-869d-735b74b1919d/taxonomies/202a03c4-dcf1-432a-82b7-abecb2e019f7Date that the model training process was completed
Possible values: 20 ≤ length ≤ 100, Value must match regular expression
^\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}(\.\d+)?Z$Example:
2024-10-23T03:58:50.00ZModel metrics object. It provides the scores for the performance bechmark tests conducted in both knowledge and skills training phase of model training. This value is available after the model training job is accepted by an agent.
Status Code
Accepted
Bad Request
Unauthorized
Forbidden
{ "id": "baa8cfb5-e306-4e15-869d-735b74b1919d", "project_id": "bdcd0181-7f5d-4bfb-be54-ef2af8cf7a35", "name": "example-model-1", "state": "", "status": "queued", "created_at": "2024-10-23T02:58:50.000Z", "data_id": "add785e6-a8c3-4f5f-ab89-c506a3f115da", "base_model": "granite-7b", "taxonomy_id": "202a03c4-dcf1-432a-82b7-abecb2e019f7", "model_metrics": { "mmlu": { "overall_average": 0.3, "scores": { "additionalProp1": 1, "additionalProp2": 2, "additionalProp3": 3 } }, "mmlu_branch": { "error_rate": 0.4, "improvements": { "additionalProp1": 1, "additionalProp2": 2, "additionalProp3": 3 }, "regressions": { "additionalProp1": 1, "additionalProp2": 2, "additionalProp3": 3 }, "no_change": { "additionalProp1": 1, "additionalProp2": 2, "additionalProp3": 3 } }, "mt_bench": { "overall_average": 0.8, "error_rate": 0.6, "scores": { "additionalProp1": 1, "additionalProp2": 2, "additionalProp3": 3 } }, "mt_bench_branch": { "error_rate": 0.4, "improvements": { "additionalProp1": 1, "additionalProp2": 2, "additionalProp3": 3 }, "regressions": { "additionalProp1": 1, "additionalProp2": 2, "additionalProp3": 3 }, "no_change": { "additionalProp1": 1, "additionalProp2": 2, "additionalProp3": 3 } } }, "href": "https://us-east.instructlab.ibm.com/v1/projects/baa8cfb5-e306-4e15-869d-735b74b1919d/models/202a03c4-dcf1-432a-82b7-abecb2e019f7" }{ "errors": [ { "id": "EQI6N2", "code": "invalid_request", "message": "Invalid request.", "more_info": "", "target": { "type": "field", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 400 }{ "errors": [ { "id": "E5P4UX", "code": "unauthorized", "message": "Unauthorized.", "more_info": "", "target": { "type": "header", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 401 }{ "errors": [ { "id": "EDKHR1", "code": "forbidden", "message": "Forbidden.", "more_info": "", "target": { "type": "header", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 403 }
Response
A collection of model training processes for an InstructLab project.
List of model training processes created for an InstructLab project.
Possible values: 0 ≤ number of items ≤ 500
Status Code
OK
Bad Request
Unauthorized
Forbidden
{ "models": [ { "id": "baa8cfb5-e306-4e15-869d-735b74b1919d", "project_id": "bdcd0181-7f5d-4bfb-be54-ef2af8cf7a35", "name": "example-model-1", "state": "", "status": "queued", "created_at": "2024-10-23T02:58:50.000Z", "completed_at": "2024-10-23T03:58:50.00Z", "data_id": "add785e6-a8c3-4f5f-ab89-c506a3f115da", "base_model": "granite-7b", "taxonomy_id": "202a03c4-dcf1-432a-82b7-abecb2e019f7", "href": "https://us-east.instructlab.ibm.com/v1/projects/baa8cfb5-e306-4e15-869d-735b74b1919d/models/202a03c4-dcf1-432a-82b7-abecb2e019f7" } ] }{ "errors": [ { "id": "EQI6N2", "code": "invalid_request", "message": "Invalid request.", "more_info": "", "target": { "type": "field", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 400 }{ "errors": [ { "id": "E5P4UX", "code": "unauthorized", "message": "Unauthorized.", "more_info": "", "target": { "type": "header", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 401 }{ "errors": [ { "id": "EDKHR1", "code": "forbidden", "message": "Forbidden.", "more_info": "", "target": { "type": "header", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 403 }
Request
Path Parameters
Unique Identifier to a model training run
Possible values: length = 36, Value must match regular expression
^[a-z0-9-]+$Example:
817bc95a-fef0-4039-b936-e0b6fb17b723
Response
Model Train Object. InstructLab implements a multiphase training process to incrementally improve the model's performance. This process involves first training the model with knowledge samples, and then using the newly acquired knowledge to train the model in developing additional skills.
Unique identifier of the model training process.
Possible values: length = 36, Value must match regular expression
^[a-z0-9-]+$Example:
baa8cfb5-e306-4e15-869d-735b74b1919dYour InstructLab project ID. You can find your project ID in the IBM Cloud console.
Possible values: length = 36, Value must match regular expression
^[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}$Example:
917bc95a-fef0-4039-b936-e0b6fb17b721Name of the model training process
Possible values: 1 ≤ length ≤ 32, Value must match regular expression
^[a-zA-Z0-9]([_-]?[a-zA-Z0-9]+)*$Example:
example-model-1The "state" of the model training processs indicates the progress training. This value is available after the model training job is accepted by an agent.
Possible values: 0 ≤ length ≤ 100, Value must match regular expression
^[a-z]*$Current status of the model training process. Can be one of "queued, running, completed, canceled, or failed.
Possible values: [
queued,running,completed,canceled,failed]Date that the model training process was created
Possible values: 20 ≤ length ≤ 100, Value must match regular expression
^\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}(\.\d+)?Z$Example:
2024-10-23T02:58:50.000ZThe ID of the synthetic data generation (SDG) run. You can use this ID to monitor the data generation process. After SDG is complete, your can this ID to retrieve the synthetic data.
Possible values: length = 36, Value must match regular expression
^[a-z0-9-]+$Example:
add785e6-a8c3-4f5f-ab89-c506a3f115daThe base model your want to use for training. This value is available only after the model training job is accepted by an agent.
Possible values: 0 ≤ length ≤ 100, Value must match regular expression
^[a-z0-9-]+$Example:
granite-7bThe taxonomy ID that was used for synthetic data generation. In model training, this value is used for validating the data ID. This value is available only after the model training job is accepted by an agent.
Possible values: length = 36, Value must match regular expression
^[a-z0-9-]+$Example:
202a03c4-dcf1-432a-82b7-abecb2e019f7Href for the response.
Possible values: 1 ≤ length ≤ 500, Value must match regular expression
^https?:\/\/.+\/[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12}$Example:
https://us-east.instructlab.ibm.com/v1/projects/baa8cfb5-e306-4e15-869d-735b74b1919d/taxonomies/202a03c4-dcf1-432a-82b7-abecb2e019f7Date that the model training process was completed
Possible values: 20 ≤ length ≤ 100, Value must match regular expression
^\d{4}-\d{2}-\d{2}T\d{2}:\d{2}:\d{2}(\.\d+)?Z$Example:
2024-10-23T03:58:50.00ZModel metrics object. It provides the scores for the performance bechmark tests conducted in both knowledge and skills training phase of model training. This value is available after the model training job is accepted by an agent.
Status Code
OK
Bad Request
Unauthorized
Forbidden
{ "id": "baa8cfb5-e306-4e15-869d-735b74b1919d", "project_id": "bdcd0181-7f5d-4bfb-be54-ef2af8cf7a35", "name": "example-model-1", "state": "", "status": "completed", "created_at": "2024-10-23T02:58:50.000Z", "completed_at": "2024-10-23T03:58:50.00Z", "data_id": "add785e6-a8c3-4f5f-ab89-c506a3f115da", "base_model": "granite-7b", "taxonomy_id": "202a03c4-dcf1-432a-82b7-abecb2e019f7", "model_metrics": { "mmlu": { "overall_average": 0.3, "scores": { "additionalProp1": 1, "additionalProp2": 2, "additionalProp3": 3 } }, "mmlu_branch": { "error_rate": 0.4, "improvements": { "additionalProp1": 1, "additionalProp2": 2, "additionalProp3": 3 }, "regressions": { "additionalProp1": 1, "additionalProp2": 2, "additionalProp3": 3 }, "no_change": { "additionalProp1": 1, "additionalProp3": 3, "additionalProp2": 2 } }, "mt_bench": { "overall_average": 0.8, "error_rate": 0.6, "scores": { "additionalProp1": 1, "additionalProp2": 2, "additionalProp3": 3 } }, "mt_bench_branch": { "error_rate": 0.4, "improvements": { "additionalProp1": 1, "additionalProp2": 2, "additionalProp3": 3 }, "regressions": { "additionalProp1": 1, "additionalProp2": 2, "additionalProp3": 3 }, "no_change": { "additionalProp1": 1, "additionalProp2": 2, "additionalProp3": 3 } }, "tokens": { "training_estimated": 50000000, "training_total": 50000000, "training_phases": { "phase_1_knowledge": 20000000, "phase_2_skills": 30000000 } } }, "href": "https://us-east.instructlab.ibm.com/v1/projects/baa8cfb5-e306-4e15-869d-735b74b1919d/models/202a03c4-dcf1-432a-82b7-abecb2e019f7" }{ "errors": [ { "id": "EQI6N2", "code": "invalid_request", "message": "Invalid request.", "more_info": "", "target": { "type": "field", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 400 }{ "errors": [ { "id": "E5P4UX", "code": "unauthorized", "message": "Unauthorized.", "more_info": "", "target": { "type": "header", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 401 }{ "errors": [ { "id": "EDKHR1", "code": "forbidden", "message": "Forbidden.", "more_info": "", "target": { "type": "header", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 403 }
Request
Path Parameters
Unique Identifier to a model training run
Possible values: length = 36, Value must match regular expression
^[a-z0-9-]+$Example:
817bc95a-fef0-4039-b936-e0b6fb17b723
Response
Status Code
No Content
Bad Request
Unauthorized
Forbidden
{ "errors": [ { "id": "EQI6N2", "code": "invalid_request", "message": "Invalid request.", "more_info": "", "target": { "type": "field", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 400 }{ "errors": [ { "id": "E5P4UX", "code": "unauthorized", "message": "Unauthorized.", "more_info": "", "target": { "type": "header", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 401 }{ "errors": [ { "id": "EDKHR1", "code": "forbidden", "message": "Forbidden.", "more_info": "", "target": { "type": "header", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 403 }
Request
Path Parameters
Unique Identifier to a model training run
Possible values: length = 36, Value must match regular expression
^[a-z0-9-]+$Example:
817bc95a-fef0-4039-b936-e0b6fb17b723
Response
Status Code
No Content
Bad Request
Unauthorized
Forbidden
{ "errors": [ { "id": "EQI6N2", "code": "invalid_request", "message": "Invalid request.", "more_info": "", "target": { "type": "field", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 400 }{ "errors": [ { "id": "E5P4UX", "code": "unauthorized", "message": "Unauthorized.", "more_info": "", "target": { "type": "header", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 401 }{ "errors": [ { "id": "EDKHR1", "code": "forbidden", "message": "Forbidden.", "more_info": "", "target": { "type": "header", "name": "" } } ], "trace": "d56f6808-d25e-438b-a1a6-c8424453c616", "status_code": 403 }