Model Hub
The SambaStudio platform provides a repository of models available to be used for a variety of applications. You can access the repository from either the GUI (the Model Hub) or the CLI (the model list).
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Base models do not support inference and cannot be deployed for endpoints. It is recommended to use Base models for training and not inference. |
View the Model Hub using the GUI
Click Models from the left menu to navigate to the Model Hub. The Model Hub provides a two-panel interface for viewing the SambaStudio repository of models.
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See Create your own CoE model to learn how to create your own Composition of Experts model. 
Figure 1. Create CoE model
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- Model Hub filters
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The Model Hub filters in the left panel host a robust set of options that refine the display of the model cards panel. In addition to the selectable filter options, you can enter a term or value into the Search field to refine the model card list by that input.
- Model cards
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In the right panel, model card previews are displayed in a three column grid layout. The tabs above the grid filter the displayed models by status:
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All models displays every model in the Model Hub, including downloaded models, upcoming models, and models that are ready to be downloaded. These models can be viewed by all users of the organization.
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Upcoming displays future models that will soon be available in the Model Hub for download and can be viewed by all users of the organization.
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All users of the organization will receive a notification that a new model is ready to be downloaded.
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Only organization administrators (OrgAdmin) can download models to the Model Hub. Once downloaded, models will be available in all tenants.
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Available displays models that have been downloaded by organization administrators (OrgAdmin) to the Model Hub. These models can be viewed by all users of the organization.
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View a CoE model card
Starting with SambaStudio release 24.2.1, SN40L 
users can use Composition of Experts (CoE) models. CoE models are indicated by a CoE badge in their previews and model cards.
Click a CoE model card preview to view detailed information about that model including:
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The status of the model.
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Available designates that the model has been downloaded by organization administrators (OrgAdmin) and is ready to use.
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Download designates that the model can be downloaded by organization administrators (OrgAdmin) to be used.
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Application denotes the model’s application type of Language.
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Type displays the CoE model type.
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Owner denotes the CoE model owner.
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Overview provides useful information about the model.
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Experts in this Composition lists the expert models used to create the Samba-1 CoE model.
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You can adjust the number of rows displayed and page through the list.
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Hover over each expert to view its description.
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The supported SambaNova hardware generation (SN40L) for each job type.
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The minimum number of RDUs required for each job type.
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View a model card
Click a model card preview to view detailed information about that model including:
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The status of the model.
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Available designates that the model has been downloaded by organization administrators (OrgAdmin) and is ready to use.
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Download designates that the model can be downloaded by organization administrators (OrgAdmin) to be used.
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Application denotes the model’s application type of Speech, Language, or Vision.
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Languages displays the languages supported by Speech or Language models.
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Type displays the model type including Base, Finetuned, and Pretrained.
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Size displays the storage size of the model.
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Owner denotes the model owner.
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Overview provides useful information about the model.
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Dataset provides information about the training and dataset(s) used for the model.
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Inputs & Outputs provides input/output specifics for supported tasks.
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Information displays the status and architecture of the model.
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The supported SambaNova hardware generation (SN10, SN30, SN40L) for each job type.
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The minimum number of RDUs required for each job type.
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View the model list using the CLI
Similar to the GUI, the SambaNova API (snapi) provides the ability to view the repository of models (the model list) via the CLI. The example below demonstrates the snapi model list command. You can include the --verbose option to provide additional model information such as the assigned model ID.
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Our examples only display the information for one model of the complete list. |
The following information is displayed for each model in the list:
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The Name of the model.
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The App ID of the model.
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The Dataset information of the model.
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The Status of the model.
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Available designates that the model has been downloaded by organization administrators (OrgAdmin) and is ready to use.
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AvailableToDownload designates that the model can be downloaded by organization administrators (OrgAdmin) to be used.
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$ snapi model list
GPT13B 2k SS HAv3
============
Name : GPT13B 2k SS HAv3
App : 57f6a3c8-1f04-488a-bb39-3cfc5b4a5d7a
Dataset : { 'info': '\n'
'The starting point for this checkpoint was the GPT 13B 8k SS '
'checkpoint which had been trained on 550B pretraining tokens,\n'
'and further instruction-tuned on 39.3B tokens of instruction '
'data. We then trained this checkpoint on the following datasets:\n'
'\n'
'1. databricks-dolly-15k\n'
'2. oasst1\n'
'\n'
'We trained on this mixture for 16 epochs.\n',
'url': ''}
Status : AvailableClick to view the example snapi model list --verbose command.
$ snapi model list \
--verbose
GPT13B 2k SS HAv3
============
ID : c7be342b-208b-4393-b5c2-496aa54eb917
Name : GPT13B 2k SS HAv3
Architecture : GPT 13B
Field of Application : language
Validation Loss : -
Validation Accuracy : -
App : 57f6a3c8-1f04-488a-bb39-3cfc5b4a5d7a
Dataset : { 'info': '\n'
'The starting point for this checkpoint was the GPT 13B 8k SS '
'checkpoint which had been trained on 550B pretraining tokens,\n'
'and further instruction-tuned on 39.3B tokens of instruction '
'data. We then trained this checkpoint on the following datasets:\n'
'\n'
'1. databricks-dolly-15k\n'
'2. oasst1\n'
'\n'
'We trained on this mixture for 16 epochs.\n',
'url': ''}
SambaNova Provided : True
Version : 1
Description : Pre-trained large language models excel in predicting the next word in sentences, but are not aligned for generating the correct responses for many of the common use cases, such as summarization or question answering. Human-facing applications in particular, such as for a chatbot, are a pain point. This checkpoint has been trained on human alignment data to optimize it for such applications. This checkpoint can serve two primary use cases:
1. It can be directly used for human-facing applications.
2. It can be used as a starting checkpoint for further alignment to instill further human-aligned qualities, such as politeness, helpfulness, or harmlessness. Some of its instruction-following capabilities may have been lost in the human alignment process, but it is still usable for instruction following applications.
This checkpoint is the same as the 8k SS HAv3 checkpoint, but has its positional embeddings truncated to 2048. If you expect to work with shorter sequences, 2k SS HAv3 will have slightly faster inference latency.
Please run inference with do_sample=True and a sampling temperature >= 0.7 for best results.
Created Time : 2024-01-19 14:35:42.274822 +0000 UTC
Status : Available
Steps : 0
Hyperparameters :
{'batch_predict': {}, 'deploy': {}, 'train': {}}
Size In GB : 49
Checkpoint Type : finetuned
Model IO : { 'infer': { 'input': {'description': '', 'example': ''},
'output': {'description': '', 'example': ''}},
'serve': { 'input': {'description': '', 'example': ''},
'output': { 'description': 'The output is a concatenation '
'of the prompt (input) string, '
'and the generated completion.',
'example': ''}},
'train': { 'input': {'description': '', 'example': ''},
'output': {'description': '', 'example': ''}}}
Evaluation : {}
Params : { 'invalidates_checkpoint': {'max_seq_length': 2048, 'vocab_size': 50260},
'modifiable': None}
rdu|
Run snapi model list --help to display additional usage and options. |
View model information using the CLI
The example below demonstrates how to view detailed information for a specific model using the snapi model info command. You will need to specify the following:
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The model name or ID for the --model input.
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Run the snapi model list command and include the --verbose option to view the model IDs for each model.
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The type of job to get detailed information for the --job-type input.
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Input train for training jobs, batch_predict for batch inference jobs, or deploy for endpoints.
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Click to view the example snapi model info command.
$ snapi model info \
--model c7be342b-208b-4393-b5c2-496aa54eb917 \
--job-type train
Model Info
============
ID : c7be342b-208b-4393-b5c2-496aa54eb917
Name : GPT13B 2k SS HAv3
Architecture : GPT 13B
Field of Application : language
Validation Loss : -
Validation Accuracy : -
App : 57f6a3c8-1f04-488a-bb39-3cfc5b4a5d7a
Dataset : { 'info': '\n'
'The starting point for this checkpoint was the GPT 13B 8k SS '
'checkpoint which had been trained on 550B pretraining tokens,\n'
'and further instruction-tuned on 39.3B tokens of instruction '
'data. We then trained this checkpoint on the following datasets:\n'
'\n'
'1. databricks-dolly-15k\n'
'2. oasst1\n'
'\n'
'We trained on this mixture for 16 epochs.\n',
'url': ''}
SambaNova Provided : True
Version : 1
Description : Pre-trained large language models excel in predicting the next word in sentences, but are not aligned for generating the correct responses for many of the common use cases, such as summarization or question answering. Human-facing applications in particular, such as for a chatbot, are a pain point. This checkpoint has been trained on human alignment data to optimize it for such applications. This checkpoint can serve two primary use cases:
1. It can be directly used for human-facing applications.
2. It can be used as a starting checkpoint for further alignment to instill further human-aligned qualities, such as politeness, helpfulness, or harmlessness. Some of its instruction-following capabilities may have been lost in the human alignment process, but it is still usable for instruction following applications.
This checkpoint is the same as the 8k SS HAv3 checkpoint, but has its positional embeddings truncated to 2048. If you expect to work with shorter sequences, 2k SS HAv3 will have slightly faster inference latency.
Please run inference with do_sample=True and a sampling temperature >= 0.7 for best results.
Created Time : 2024-01-19 14:35:42.274822 +0000 UTC
Status : Available
Steps : 0
Hyperparameters :
{ 'batch_predict': {},
'deploy': {},
'train': { 'sn10': { 'imageVariants': [],
'imageVersion': '3.0.0-20231218',
'jobTypes': ['compile', 'train'],
'runtimeVersion': '5.3.0',
'sockets': 2,
'supports_data_parallel': True,
'user_params': [ { 'CONSTRAINTS': { 'ge': '',
'gt': '',
'le': '',
'lt': '',
'values': [ 'true',
'false']},
'DATATYPE': 'bool',
'DESCRIPTION': 'whether or '
'not to do '
'final '
'evaluation',
'FIELD_NAME': 'do_eval',
'MESSAGE': '',
'TASK_TYPE': ['train'],
'TYPE_SPECIFIC_SETTINGS': { 'train': { 'DEFAULT': 'true',
'USER_MODIFIABLE': True}},
'VARIANT_SELECTION': False},
{ 'CONSTRAINTS': { 'ge': '1',
'gt': '',
'le': '',
'lt': '',
'values': [ ]},
'DATATYPE': 'int',
'DESCRIPTION': 'Period of '
'evaluating '
'the model '
'in number '
'of '
'training '
'steps. '
'This '
'parameter '
'is only '
'effective '
'when '
'evaluation_strategy '
'is set to '
"'steps'.",
'FIELD_NAME': 'eval_steps',
'MESSAGE': '',
'TASK_TYPE': ['train'],
'TYPE_SPECIFIC_SETTINGS': { 'train': { 'DEFAULT': '50',
'USER_MODIFIABLE': True}},
'VARIANT_SELECTION': False},
{ 'CONSTRAINTS': { 'ge': '',
'gt': '',
'le': '',
'lt': '',
'values': [ 'no',
'steps',
'epoch']},
'DATATYPE': 'str',
'DESCRIPTION': 'Strategy '
'to '
'validate '
'the model '
'during '
'training',
'FIELD_NAME': 'evaluation_strategy',
'MESSAGE': '',
'TASK_TYPE': ['train'],
'TYPE_SPECIFIC_SETTINGS': { 'train': { 'DEFAULT': 'steps',
'USER_MODIFIABLE': True}},
'VARIANT_SELECTION': False},
{ 'CONSTRAINTS': { 'ge': '0',
'gt': '',
'le': '',
'lt': '',
'values': [ ]},
'DATATYPE': 'float',
'DESCRIPTION': 'learning '
'rate to '
'use in '
'optimizer',
'FIELD_NAME': 'learning_rate',
'MESSAGE': '',
'TASK_TYPE': ['train'],
'TYPE_SPECIFIC_SETTINGS': { 'train': { 'DEFAULT': '7.5e-06',
'USER_MODIFIABLE': True}},
'VARIANT_SELECTION': False},
{ 'CONSTRAINTS': { 'ge': '1',
'gt': '',
'le': '',
'lt': '',
'values': [ ]},
'DATATYPE': 'int',
'DESCRIPTION': 'Period of '
'logging '
'training '
'loss in '
'number of '
'training '
'steps',
'FIELD_NAME': 'logging_steps',
'MESSAGE': '',
'TASK_TYPE': ['train'],
'TYPE_SPECIFIC_SETTINGS': { 'train': { 'DEFAULT': '10',
'USER_MODIFIABLE': True}},
'VARIANT_SELECTION': False},
{ 'CONSTRAINTS': { 'ge': '',
'gt': '',
'le': '',
'lt': '',
'values': [ 'polynomial_decay_schedule_with_warmup',
'cosine_schedule_with_warmup',
'fixed_lr']},
'DATATYPE': 'str',
'DESCRIPTION': 'Type of '
'learning '
'rate '
'scheduler '
'to use',
'FIELD_NAME': 'lr_schedule',
'MESSAGE': '',
'TASK_TYPE': ['train'],
'TYPE_SPECIFIC_SETTINGS': { 'train': { 'DEFAULT': 'cosine_schedule_with_warmup',
'USER_MODIFIABLE': True}},
'VARIANT_SELECTION': False},
{ 'CONSTRAINTS': { 'ge': '',
'gt': '',
'le': '',
'lt': '',
'values': [ '2048',
'8192']},
'DATATYPE': 'int',
'DESCRIPTION': 'Sequence '
'length to '
'pad or '
'truncate '
'the '
'dataset',
'FIELD_NAME': 'max_seq_length',
'MESSAGE': '',
'TASK_TYPE': [ 'compile',
'infer',
'serve',
'train'],
'TYPE_SPECIFIC_SETTINGS': { 'train': { 'DEFAULT': '2048',
'USER_MODIFIABLE': False}},
'VARIANT_SELECTION': False},
{ 'CONSTRAINTS': { 'ge': '1',
'gt': '',
'le': '',
'lt': '',
'values': [ ]},
'DATATYPE': 'int',
'DESCRIPTION': 'number of '
'iterations '
'to run',
'FIELD_NAME': 'num_iterations',
'MESSAGE': '',
'TASK_TYPE': ['train'],
'TYPE_SPECIFIC_SETTINGS': { 'train': { 'DEFAULT': '100',
'USER_MODIFIABLE': True}},
'VARIANT_SELECTION': False},
{ 'CONSTRAINTS': { 'ge': '0',
'gt': '',
'le': '',
'lt': '',
'values': [ ]},
'DATATYPE': 'float',
'DESCRIPTION': 'Loss scale '
'for prompt '
'tokens',
'FIELD_NAME': 'prompt_loss_weight',
'MESSAGE': '',
'TASK_TYPE': ['train'],
'TYPE_SPECIFIC_SETTINGS': { 'train': { 'DEFAULT': '0.1',
'USER_MODIFIABLE': True}},
'VARIANT_SELECTION': False},
{ 'CONSTRAINTS': { 'ge': '',
'gt': '',
'le': '',
'lt': '',
'values': [ 'true',
'false']},
'DATATYPE': 'bool',
'DESCRIPTION': 'Whether to '
'save the '
'optimizer '
'state when '
'saving a '
'checkpoint',
'FIELD_NAME': 'save_optimizer_state',
'MESSAGE': '',
'TASK_TYPE': ['train'],
'TYPE_SPECIFIC_SETTINGS': { 'train': { 'DEFAULT': 'true',
'USER_MODIFIABLE': True}},
'VARIANT_SELECTION': False},
{ 'CONSTRAINTS': { 'ge': '1',
'gt': '',
'le': '',
'lt': '',
'values': [ ]},
'DATATYPE': 'int',
'DESCRIPTION': 'Period of '
'saving the '
'model '
'checkpoints '
'in number '
'of '
'training '
'steps',
'FIELD_NAME': 'save_steps',
'MESSAGE': '',
'TASK_TYPE': ['train'],
'TYPE_SPECIFIC_SETTINGS': { 'train': { 'DEFAULT': '50',
'USER_MODIFIABLE': True}},
'VARIANT_SELECTION': False},
{ 'CONSTRAINTS': { 'ge': '',
'gt': '',
'le': '',
'lt': '',
'values': [ 'true',
'false']},
'DATATYPE': 'bool',
'DESCRIPTION': 'whether or '
'not to '
'skip the '
'checkpoint',
'FIELD_NAME': 'skip_checkpoint',
'MESSAGE': '',
'TASK_TYPE': ['train'],
'TYPE_SPECIFIC_SETTINGS': { 'train': { 'DEFAULT': 'false',
'USER_MODIFIABLE': True}},
'VARIANT_SELECTION': False},
{ 'CONSTRAINTS': { 'ge': '0',
'gt': '',
'le': '',
'lt': '',
'values': [ ]},
'DATATYPE': 'float',
'DESCRIPTION': 'Subsample '
'for the '
'evaluation '
'dataset',
'FIELD_NAME': 'subsample_eval',
'MESSAGE': '',
'TASK_TYPE': ['train'],
'TYPE_SPECIFIC_SETTINGS': { 'train': { 'DEFAULT': '0.01',
'USER_MODIFIABLE': True}},
'VARIANT_SELECTION': False},
{ 'CONSTRAINTS': { 'ge': '1',
'gt': '',
'le': '',
'lt': '',
'values': [ ]},
'DATATYPE': 'int',
'DESCRIPTION': 'Random '
'seed to '
'use for '
'the '
'subsample '
'evaluation',
'FIELD_NAME': 'subsample_eval_seed',
'MESSAGE': '',
'TASK_TYPE': ['train'],
'TYPE_SPECIFIC_SETTINGS': { 'train': { 'DEFAULT': '123',
'USER_MODIFIABLE': True}},
'VARIANT_SELECTION': False},
{ 'CONSTRAINTS': { 'ge': '',
'gt': '',
'le': '',
'lt': '',
'values': [ 'true',
'false']},
'DATATYPE': 'bool',
'DESCRIPTION': 'Whether to '
'use '
'token_type_ids '
'to compute '
'loss',
'FIELD_NAME': 'use_token_type_ids',
'MESSAGE': '',
'TASK_TYPE': ['train'],
'TYPE_SPECIFIC_SETTINGS': { 'train': { 'DEFAULT': 'true',
'USER_MODIFIABLE': True}},
'VARIANT_SELECTION': False},
{ 'CONSTRAINTS': { 'ge': '',
'gt': '',
'le': '',
'lt': '',
'values': [ '50260',
'307200']},
'DATATYPE': 'int',
'DESCRIPTION': 'Maximum '
'size of '
'vocabulary',
'FIELD_NAME': 'vocab_size',
'MESSAGE': '',
'TASK_TYPE': [ 'compile',
'infer',
'serve',
'train'],
'TYPE_SPECIFIC_SETTINGS': { 'train': { 'DEFAULT': '50260',
'USER_MODIFIABLE': False}},
'VARIANT_SELECTION': False},
{ 'CONSTRAINTS': { 'ge': '0',
'gt': '',
'le': '',
'lt': '',
'values': [ ]},
'DATATYPE': 'int',
'DESCRIPTION': 'warmup '
'steps to '
'use in '
'learning '
'rate '
'scheduler '
'in '
'optimizer',
'FIELD_NAME': 'warmup_steps',
'MESSAGE': '',
'TASK_TYPE': ['train'],
'TYPE_SPECIFIC_SETTINGS': { 'train': { 'DEFAULT': '0',
'USER_MODIFIABLE': True}},
'VARIANT_SELECTION': False},
{ 'CONSTRAINTS': { 'ge': '0',
'gt': '',
'le': '',
'lt': '',
'values': [ ]},
'DATATYPE': 'float',
'DESCRIPTION': 'weight '
'decay rate '
'to use in '
'optimizer',
'FIELD_NAME': 'weight_decay',
'MESSAGE': '',
'TASK_TYPE': [ 'infer',
'serve',
'train'],
'TYPE_SPECIFIC_SETTINGS': { 'train': { 'DEFAULT': '0.1',
'USER_MODIFIABLE': True}},
'VARIANT_SELECTION': False}],
'variantSetVersion': ''}}}
Size In GB : 49
Checkpoint Type : finetuned
Model IO : { 'infer': { 'input': {'description': '', 'example': ''},
'output': {'description': '', 'example': ''}},
'serve': { 'input': {'description': '', 'example': ''},
'output': { 'description': 'The output is a concatenation '
'of the prompt (input) string, '
'and the generated completion.',
'example': ''}},
'train': { 'input': {'description': '', 'example': ''},
'output': {'description': '', 'example': ''}}}
Evaluation : {}
Params : { 'invalidates_checkpoint': {'max_seq_length': 2048, 'vocab_size': 50260},
'modifiable': None}
rduCreate ASR pipelines
SambaStudio allows you to create new ASR pipelines that utilize your trained Hubert ASR models. You can use your trained Hubert ASR models to create a new ASR pipeline with diarization (Diarization ASR Pipeline) or without diarization (ASR Pipeline). The process is the same for both workflows. Follow the steps below to create either a new ASR Pipeline or a new Diarization ASR Pipeline.
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Click Create new pipeline from the kebob menu (three vertical dots) drop-down of either the model card preview or the detailed model card.
Figure 6. Create new pipeline from a model card preview
Figure 7. Create new pipeline from a detailed model card-
The Create pipeline model window will open.
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In the Create pipeline model window, select your trained Hubert ASR model to replace the default Hubert_ASR model from the drop-down.
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We used our trained Hubert_ASR_finetuned_02 model in the example below.
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Enter a new name into the Pipeline model name field.
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Select either finetuned or pretrained from the Model type drop-down.
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The Share settings drop-down provides options for which tenant to share your new pipeline model.
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Share with <current-tenant> allows the new model to be shared with the current tenant you are using, identified by its name in the drop-down.
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Share with all tenants allows the new model to be shared across all tenants.
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-
Click Save model to save the new ASR pipeline to the Model Hub.
Figure 8. Create pipeline model window
Download models using the GUI
SambaStudio provides the ability for organization administrators (OrgAdmin) to download models to the Model Hub. This allows SambaNova created models to be downloaded and used when new models are released. A model available for download will display a download icon in the associated model cards panel and its model card.
Add models using the GUI
SambaStudio allows you to add a model from a training job or from local storage.
Add a checkpoint from a training job using the GUI
Follow the steps below to add a checkpoint from a training job using the SambaStudio GUI.
-
From the Model Hub window, click Add checkpoint. The Add a checkpoint to Model Hub box will open.
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Select the From training job tab.
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From the Select project drop-down, select the project that contains checkpoint.
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Enter a name for the new model-checkpoint in the Model name field.
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From the Select job drop-down, select the job that contains the checkpoint.
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The Select checkpoint drop-down opens a list of available checkpoints from the specified job. Select the model-checkpoint you wish to add from the list.
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Select finetuned or pretrained from the Select model type drop-down.
-
Click Add checkpoint to Model Hub to confirm adding your selected checkpoint and return to the Model Hub. Click Cancel to close the box and return to the Model Hub.
Add a checkpoint from storage using the GUI
|
Adding a checkpoint from storage is an optional feature of SambaStudio. Please contact your administrator or SambaNova representative for more information. |
SambaStudio allows you to take a model from a different environment and add it as a new model to the current SambaStudio environment through NFS. Follow the steps below to add a checkpoint to the Model Hub from your local storage using the GUI.
-
Select Local from the Select storage type drop-down.
-
Enter a unique name for the new model-checkpoint in the Import model name field.
-
Provide the checkpoint path from your NFS location in the Import model path field. An example path would be
user1/checkpoints/subpath. -
Specify the step the training job will resume from by entering it into the Steps field. Steps prior to the specified step will be skipped.
-
From the Select ML App drop-down, choose the ML App to which the model-checkpoint belongs.
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Select finetuned or pretrained from the Select checkpoint type drop-down.
-
Click Add checkpoint to Model Hub to confirm adding your selected checkpoint and return to the Model Hub. Click Cancel to close the box and return to the Model Hub.
Insufficient storage message
If the required amount of storage space is not available to add the checkpoint, the Insufficient storage message will display describing the Available space and the Required space to import the model. You will need to free up storage space or contact your administrator. Please choose one of the following options.
-
Click Cancel to stop the add a checkpoint process. Please free up storage space and then restart the add a checkpoint to the Model Hub process.
-
Click Add checkpoint to Model Hub to proceed and add the checkpoint. Please free up storage space, otherwise the add a checkpoint to the Model Hub process will fail.
|
A minimum of 10 minutes is required after sufficient storage space has been cleared before the checkpoint will start successfully saving to the Model Hub. |
Import models using the CLI
Use the snapi model import command to import a model to the model list. The example below demonstrates how to import a pretrained model from local storage and have it start at step 20. You will need to specify the following:
-
The Model ID for the --model-id input.
-
Provide the relative path to the model from your NFS location for the --import-path input.
-
A name for your model for the --import-model-name input.
$ snapi model import \
--model-id '97ea373f-498e-4ede-822e-a34a88693a09' \
--import-path 'user1/checkpoints/subpath' \
--import-model-name 'gt_model_imported' \
--storage-type 'Local' \
--steps 20|
Run snapi model import --help to display additional usage and options. |
Share models
SambaStudio allows you to share your models with other users in your organization via tenants. You can share a model that you own to be available to your current tenant or all tenants. Follow the steps below to share your model.
|
A tenant is an allotment of users and resources within an organization. Each tenant will have its own resources, users, and workloads. See Tenants and management for more information. |
-
Select a model that you own and want to share from the left panel of the model card list.
-
Click Share to the right of the model name. The Model share settings box will open.
-
From the Share settings drop-down, select the tenant for the model to be shared.
-
Share with <tenant-name> allows the model to be shared with the current tenant you are using, identified by its name in the drop-down.
-
Share with all tenants allows the model to be shared across all tenants.
-
-
Click Update to complete the process. Click Cancel to stop the process and return to the Model Hub.
Export models
SambaStudio allows users to export any checkpoint they have published to the Model Hub.
|
SambaNova owned models cannot be exported from the platform. |
Export a checkpoint using the GUI
Follow the steps below to export a checkpoint using the SambaStudio GUI.
-
Select your model from the left panel of the model card list.
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Click Export to the right of the model name. The Export details box will open.
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Click Continue. The Export details box will display the source path to the exported model’s checkpoint. Copy the path displayed in Exported model path.
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Click Done to complete the export process.
Export using the CLI
Use the snapi model export command to export a model as demonstrated below. You will need to provide the Model ID for the --model-id input.
$ snapi model export \
--model-id '97ea373f-498e-4ede-822e-a34a88693a09' \
--storage-type 'Local'|
Run snapi model export --help to display additional usage and options. |
Use the snapi model list-exported command to view a list of exported models. The help command below displays the usage and options for snapi model list-exported --help.
$ snapi model list-exported --help
Usage: snapi model export [OPTIONS]
Export the model
╭─ Options ────────────────────────────────────────────────────────────────────╮
│ --file TEXT │
│ * --model-id -m TEXT Model ID [default: None] [required] │
│ --storage-type -s TEXT Supported storage type for export is │
│ 'Local' │
│ [default: Local] │
│ --help Show this message and exit. │
╰──────────────────────────────────────────────────────────────────────────────╯Delete models
SambaStudio allows you to delete your models from the Model Hub.
|
SambaNova owned models cannot be deleted from the platform. |
Delete a model using the GUI
Follow the steps below to delete a model using the SambaStudio GUI.
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Select your model from the left panel of the model card list.
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Click Delete to the right of the model name. The Delete model box will open. A warning message will display informing you that you are about to delete a model.
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Click Yes, delete model to confirm that you want to delete the model.
Delete a model using the CLI
Use the snapi model remove command to remove and delete a model form the Model Hub.
The help command below displays the usage and options for snapi model remove --help.
$ snapi model remove --help
Usage: snapi model remove [OPTIONS]
Remove the model
Note: For NFS, the model is not copied as a part of export. Deleting the model deletes the contents of the original model.
╭─ Options ────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ --file TEXT │
│ * --model -m TEXT Model Name or ID [default: None] [required] │
│ --help Show this message and exit. │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯Delete an exported model using the CLI
Use the snapi model delete-exported command to delete an exported model as demonstrated below.
$ snapi model delete-exported \
--model-id '97ea373f-498e-4ede-822e-a34a88693a09' \
--model-activity-id '9abbec28-c1cf-11ed-afa1-0242ac120002'|
Run snapi model delete-exported --help to display additional usage and options. |