litellm-mirror/docs/my-website/docs/fine_tuning.md

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/fine_tuning

:::info

This is an Enterprise only endpoint Get Started with Enterprise here

:::

Feature	Supported	Notes
Supported Providers	OpenAI, Azure OpenAI, Vertex 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",
)

curl http://localhost:4000/v1/files \
    -H "Authorization: Bearer sk-1234" \
    -F purpose="batch" \
    -F custom_llm_provider="azure"\
    -F file="@mydata.jsonl"

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
)

curl http://localhost:4000/v1/fine_tuning/jobs \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer sk-1234" \
    -d '{
    "custom_llm_provider": "azure",
    "model": "gpt-35-turbo-1106",
    "training_file": "file-abc123"
    }'

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.

{
  "model": "gpt-4o-mini",
  "training_file": "file-abcde12345",
  "hyperparameters": {
    "batch_size": 4,
    "learning_rate_multiplier": 0.1,
    "n_epochs": 3
  },
  "suffix": "custom-model-v1",
  "validation_file": "file-fghij67890",
  "seed": 42
}

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))

curl -X POST http://localhost:4000/v1/fine_tuning/jobs/ftjob-abc123/cancel \
  -H "Authorization: Bearer sk-1234" \
  -H "Content-Type: application/json" \
  -d '{"custom_llm_provider": "azure"}'

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))

curl -X GET 'http://localhost:4000/v1/fine_tuning/jobs?custom_llm_provider=azure' \
     -H "Content-Type: application/json" \
     -H "Authorization: Bearer sk-1234"

👉 Proxy API Reference