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docs add example doing ft
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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# [Beta] Fine-tuning API
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# [Beta] Fine-tuning API
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@ -11,7 +14,7 @@ This is an Enterprise only endpoint [Get Started with Enterprise here](https://c
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- Azure OpenAI
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- Azure OpenAI
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- OpenAI
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- OpenAI
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Add `finetune_settings` and `files_settings` to your litellm config.yaml to use the fine-tuning endpoints.
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## Usage
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## Usage
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```yaml
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```yaml
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model_list:
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model_list:
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finetune_settings:
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finetune_settings:
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- custom_llm_provider: azure
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- custom_llm_provider: azure
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api_base: https://exampleopenaiendpoint-production.up.railway.app
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api_base: https://exampleopenaiendpoint-production.up.railway.app
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api_key: fake-key
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api_key: os.environ/AZURE_API_KEY
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api_version: "2023-03-15-preview"
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api_version: "2023-03-15-preview"
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- custom_llm_provider: openai
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- custom_llm_provider: openai
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api_key: os.environ/OPENAI_API_KEY
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api_key: os.environ/OPENAI_API_KEY
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## Create File for Fine Tuning
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## Create File for Fine Tuning
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<Tabs>
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<TabItem value="openai" label="OpenAI Python v1.0.0+">
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```python
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client = AsyncOpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000") # base_url is your litellm proxy url
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file_name = "openai_batch_completions.jsonl"
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response = await client.files.create(
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extra_body={"custom_llm_provider": "azure"}, # tell litellm proxy which provider to use
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file=open(file_name, "rb"),
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purpose="fine-tune",
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)
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```
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</TabItem>
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</Tabs>
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## Create fine-tuning job
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## Create fine-tuning job
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<Tabs>
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</Tabs>
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## Cancel fine-tuning job
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## Cancel fine-tuning job
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<Tabs>
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</Tabs>
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## List fine-tuning jobs
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## List fine-tuning jobs
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<Tabs>
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</Tabs>
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