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/fine_tuning

info

This is an Enterprise only endpoint Get Started with Enterprise here

FeatureSupportedNotes
Supported ProvidersOpenAI, Azure OpenAI, Vertex AI-

⚡️See an exhaustive list of supported models and providers at models.litellm.ai​

| Cost Tracking | 🟡 | Let us know if you need this | | Logging | ✅ | Works across all logging integrations |

Add finetune_settings and files_settings to your litellm config.yaml to use the fine-tuning endpoints.

Example config.yaml for finetune_settings and files_settings​

model_list:
  - model_name: gpt-4
    litellm_params:
      model: openai/fake
      api_key: fake-key
      api_base: https://exampleopenaiendpoint-production.up.railway.app/

# For /fine_tuning/jobs endpoints
finetune_settings:
  - custom_llm_provider: azure
    api_base: https://exampleopenaiendpoint-production.up.railway.app
    api_key: os.environ/AZURE_API_KEY
    api_version: "2023-03-15-preview"
  - custom_llm_provider: openai
    api_key: os.environ/OPENAI_API_KEY
  - custom_llm_provider: "vertex_ai"
    vertex_project: "adroit-crow-413218"
    vertex_location: "us-central1"
    vertex_credentials: "/Users/ishaanjaffer/Downloads/adroit-crow-413218-a956eef1a2a8.json"

# for /files endpoints
files_settings:
  - custom_llm_provider: azure
    api_base: https://exampleopenaiendpoint-production.up.railway.app
    api_key: fake-key
    api_version: "2023-03-15-preview"
  - custom_llm_provider: openai
    api_key: os.environ/OPENAI_API_KEY

Create File for fine-tuning​

client = AsyncOpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000") # base_url is your litellm proxy url

file_name = "openai_batch_completions.jsonl"
response = await client.files.create(
    extra_body={"custom_llm_provider": "azure"}, # tell litellm proxy which provider to use
    file=open(file_name, "rb"),
    purpose="fine-tune",
)

Create fine-tuning job​

ft_job = await client.fine_tuning.jobs.create(
    model="gpt-35-turbo-1106",                   # Azure OpenAI model you want to fine-tune
    training_file="file-abc123",                 # file_id from create file response
    extra_body={"custom_llm_provider": "azure"}, # tell litellm proxy which provider to use
)

Request Body​

  • model

    Type: string
    Required: Yes
    The name of the model to fine-tune

  • custom_llm_provider

    Type: Literal["azure", "openai", "vertex_ai"]

    Required: Yes The name of the model to fine-tune. You can select one of the supported providers

  • training_file

    Type: string
    Required: Yes
    The ID of an uploaded file that contains training data.

    • See upload file for how to upload a file.
    • Your dataset must be formatted as a JSONL file.

  • hyperparameters

    Type: object
    Required: No
    The hyperparameters used for the fine-tuning job.

    Supported hyperparameters​

    batch_size​

    Type: string or integer
    Required: No
    Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance.

    learning_rate_multiplier​

    Type: string or number
    Required: No
    Scaling factor for the learning rate. A smaller learning rate may be useful to avoid overfitting.

    n_epochs​

    Type: string or integer
    Required: No
    The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset.

  • suffix Type: string or null
    Required: No
    Default: null
    A string of up to 18 characters that will be added to your fine-tuned model name. Example: A suffix of "custom-model-name" would produce a model name like ft:gpt-4o-mini:openai:custom-model-name:7p4lURel.

  • validation_file Type: string or null
    Required: No
    The ID of an uploaded file that contains validation data.

    • If provided, this data is used to generate validation metrics periodically during fine-tuning.

  • integrations Type: array or null
    Required: No
    A list of integrations to enable for your fine-tuning job.

  • seed Type: integer or null
    Required: No
    The seed controls the reproducibility of the job. Passing in the same seed and job parameters should produce the same results, but may differ in rare cases. If a seed is not specified, one will be generated for you.

Cancel fine-tuning job​

# cancel specific fine tuning job
cancel_ft_job = await client.fine_tuning.jobs.cancel(
    fine_tuning_job_id="123",                          # fine tuning job id
    extra_body={"custom_llm_provider": "azure"},       # tell litellm proxy which provider to use
)

print("response from cancel ft job={}".format(cancel_ft_job))

List fine-tuning jobs​

list_ft_jobs = await client.fine_tuning.jobs.list(
    extra_query={"custom_llm_provider": "azure"}   # tell litellm proxy which provider to use
)

print("list of ft jobs={}".format(list_ft_jobs))

👉 Proxy API Reference​