Trains and deploys a fine-tuned model.

Headers

X-Client-NamestringOptional

The name of the project that is making the request.

Request

This endpoint expects an object.

namestringRequired

FinetunedModel name (e.g. foobar).

settingsobjectRequired

FinetunedModel settings such as dataset, hyperparameters…

idstringOptional

read-only. FinetunedModel ID.

creator_idstringOptional

read-only. User ID of the creator.

organization_idstringOptional

read-only. Organization ID.

statusenumOptionalDefaults to STATUS_UNSPECIFIED

read-only. Current stage in the life-cycle of the fine-tuned model.

created_atdatetimeOptional

read-only. Creation timestamp.

updated_atdatetimeOptional

read-only. Latest update timestamp.

completed_atdatetimeOptional

read-only. Timestamp for the completed fine-tuning.

last_useddatetimeOptional

read-only. Timestamp for the latest request to this fine-tuned model.

Response

This endpoint returns an object.

finetuned_modelobjectOptional

Information about the fine-tuned model.

1 from cohere.finetuning import ( 2 BaseModel, 3 FinetunedModel, 4 Hyperparameters, 5 Settings, 6 WandbConfig 7 ) 8 import cohere 9 10 co = cohere.Client("<<apiKey>>") 11 hp = Hyperparameters( 12 early_stopping_patience=10, 13 early_stopping_threshold=0.001, 14 train_batch_size=16, 15 train_epoch=1, 16 learning_rate=0.01, 17 ) 18 wnb_config = WandbConfig( 19 project="test-project", 20 api_key="<<wandbApiKey>>", 21 entity="test-entity", 22 ) 23 finetuned_model = co.finetuning.create_finetuned_model( 24 request=FinetunedModel( 25 name="test-finetuned-model", 26 settings=Settings( 27 base_model=BaseModel( 28 base_type="BASE_TYPE_CHAT", 29 ), 30 dataset_id="my-dataset-id", 31 hyperparameters=hp, 32 wandb=wnb_config, 33 ), 34 ) 35 ) 36 print(finetuned_model)

1 { 2 "finetuned_model": { 3 "name": "name", 4 "settings": { 5 "base_model": { 6 "base_type": "BASE_TYPE_UNSPECIFIED" 7 }, 8 "dataset_id": "dataset_id", 9 "multi_label": true, 10 "wandb": { 11 "project": "project", 12 "api_key": "api_key" 13 } 14 }, 15 "id": "id", 16 "creator_id": "creator_id", 17 "organization_id": "organization_id", 18 "status": "STATUS_UNSPECIFIED", 19 "created_at": "2024-01-15T09:30:00Z", 20 "updated_at": "2024-01-15T09:30:00Z", 21 "completed_at": "2024-01-15T09:30:00Z", 22 "last_used": "2024-01-15T09:30:00Z" 23 } 24 }

1	from cohere.finetuning import (
2	BaseModel,
3	FinetunedModel,
4	Hyperparameters,
5	Settings,
6	WandbConfig
7	)
8	import cohere
9
10	co = cohere.Client("<<apiKey>>")
11	hp = Hyperparameters(
12	early_stopping_patience=10,
13	early_stopping_threshold=0.001,
14	train_batch_size=16,
15	train_epoch=1,
16	learning_rate=0.01,
17	)
18	wnb_config = WandbConfig(
19	project="test-project",
20	api_key="<<wandbApiKey>>",
21	entity="test-entity",
22	)
23	finetuned_model = co.finetuning.create_finetuned_model(
24	request=FinetunedModel(
25	name="test-finetuned-model",
26	settings=Settings(
27	base_model=BaseModel(
28	base_type="BASE_TYPE_CHAT",
29	),
30	dataset_id="my-dataset-id",
31	hyperparameters=hp,
32	wandb=wnb_config,
33	),
34	)
35	)
36	print(finetuned_model)

1	{
2	"finetuned_model": {
3	"name": "name",
4	"settings": {
5	"base_model": {
6	"base_type": "BASE_TYPE_UNSPECIFIED"
7	},
8	"dataset_id": "dataset_id",
9	"multi_label": true,
10	"wandb": {
11	"project": "project",
12	"api_key": "api_key"
13	}
14	},
15	"id": "id",
16	"creator_id": "creator_id",
17	"organization_id": "organization_id",
18	"status": "STATUS_UNSPECIFIED",
19	"created_at": "2024-01-15T09:30:00Z",
20	"updated_at": "2024-01-15T09:30:00Z",
21	"completed_at": "2024-01-15T09:30:00Z",
22	"last_used": "2024-01-15T09:30:00Z"
23	}
24	}