Trains and deploys a fine-tuned model. | Cohere

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Cohere API
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Deprecated

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Trains and deploys a fine-tuned model. | Cohere

Deprecated v1/finetuning

Deprecated

POST

https://api.cohere.com/v1/finetuning/finetuned-models

POST

/v1/finetuning/finetuned-models

1 from cohere.finetuning import (
2     BaseModel,
3     FinetunedModel,
4     Hyperparameters,
5     Settings,
6     WandbConfig,
7 )
8 import cohere
9 
10 co = cohere.Client()
11 hp = Hyperparameters(
12     early_stopping_patience=10,
13     early_stopping_threshold=0.001,
14     train_batch_size=16,
15     train_epochs=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": "string",
4     "settings": {
5       "base_model": {
6         "base_type": "BASE_TYPE_UNSPECIFIED",
7         "name": "string",
8         "version": "string",
9         "strategy": "STRATEGY_UNSPECIFIED"
10       },
11       "dataset_id": "string",
12       "hyperparameters": {
13         "early_stopping_patience": 1,
14         "early_stopping_threshold": 1.1,
15         "train_batch_size": 1,
16         "train_epochs": 1,
17         "learning_rate": 1.1,
18         "lora_alpha": 1,
19         "lora_rank": 1,
20         "lora_target_modules": "LORA_TARGET_MODULES_UNSPECIFIED"
21       },
22       "multi_label": true,
23       "wandb": {
24         "project": "string",
25         "api_key": "string",
26         "entity": "string"
27       }
28     },
29     "id": "string",
30     "creator_id": "string",
31     "organization_id": "string",
32     "status": "STATUS_UNSPECIFIED",
33     "created_at": "2024-01-15T09:30:00Z",
34     "updated_at": "2024-01-15T09:30:00Z",
35     "completed_at": "2024-01-15T09:30:00Z",
36     "last_used": "2024-01-15T09:30:00Z"
37   }
38 }

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Updates a fine-tuned model.

Creates a new fine-tuned model. The model will be trained on the dataset specified in the request body. The training process may take some time, and the model will be available once the training is complete.

Authentication

AuthorizationBearer

Bearer authentication of the form `Bearer <token>`, where token is your auth token.

Headers

X-Client-NamestringOptional

The name of the project that is making the request.

Request

Information about the fine-tuned model. Must contain name and settings.

namestringRequired

FinetunedModel name (e.g. foobar).

settingsobjectRequired

FinetunedModel settings such as dataset, hyperparameters...

statusenumOptionalDefaults to STATUS_UNSPECIFIED

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

Response

A successful response.

finetuned_modelobject

Information about the fine-tuned model.

Errors

400

Bad Request Error

401

Unauthorized Error

403

Forbidden Error

404

Not Found Error

500

Internal Server Error

503

Service Unavailable Error

The name of the project that is making the request.

A successful response.

Updates a fine-tuned model.

Trains and deploys a fine-tuned model.

Creates a new fine-tuned model. The model will be trained on the dataset specified in the request body. The training process may take some time, and the model will be available once the training is complete.

Information about the fine-tuned model. Must contain name and settings.

Bearer authentication of the form Bearer <token>, where token is your auth token.