* Add LiteLLM Managed file support for `retrieve`, `list` and `cancel` finetuning jobs (#11033)
* feat: initial commit adding managed file support to fine tuning endpoints
* feat(fine_tuning/endpoints.py): working call to openai finetuning route
Uses litellm managed files for finetuning api support
* feat(fine-tuning/main.py): refactor to use LiteLLMFineTuningJob pydantic object
includes 'hidden_params'
* fix: initial commit adding unified finetuning id support
return a unified finetuning id we can use to understand which deployment to route the ft request to
* test: fix test
* feat(managed_files.py): return unified finetuning job id on create finetuning job
enables retrieve, delete to work with litellm managed files
* feat(managed_files.py): support managed files for cancel ft job endpoint
* feat(managed_files.py): support managed files for cancel ft job endpoint
* feat(fine_tuning_endpoints/endpoints.py): add managed files support to list finetuning jobs
* feat(finetuning_endpoints/main): add managed files support for retrieving ft job
Makes it easier to control permissions for ft endpoint
* LiteLLM Managed Files - Enforce validation check if user can access finetuning job (#11034)
* feat: initial commit adding managed file support to fine tuning endpoints
* feat(fine_tuning/endpoints.py): working call to openai finetuning route
Uses litellm managed files for finetuning api support
* feat(fine-tuning/main.py): refactor to use LiteLLMFineTuningJob pydantic object
includes 'hidden_params'
* fix: initial commit adding unified finetuning id support
return a unified finetuning id we can use to understand which deployment to route the ft request to
* test: fix test
* feat(managed_files.py): return unified finetuning job id on create finetuning job
enables retrieve, delete to work with litellm managed files
* feat(managed_files.py): support managed files for cancel ft job endpoint
* feat(managed_files.py): support managed files for cancel ft job endpoint
* feat(fine_tuning_endpoints/endpoints.py): add managed files support to list finetuning jobs
* feat(finetuning_endpoints/main): add managed files support for retrieving ft job
Makes it easier to control permissions for ft endpoint
* feat(managed_files.py): store create fine-tune / batch response object in db
storing this allows us to filter files returned on list based on what user created
* feat(managed_files.py): Ensures users can't retrieve / modify each others jobs
* fix: fix check
* fix: fix ruff check errors
* test: update to handle testing
* fix: suppress linting warning - openai 'seed' is none on azure
* test: update tests
* test: update test
* feat(model_info_view.tsx): enable updating model info for existing models on UI
Fixes LIT-154
* fix(model_info_view.tsx): instantly show model info updates on UI
* feat(proxy_server.py): enable flag on `/models` to include model access groups
This enables admin to assign model access groups to keys/teams on UI
* feat(ui/): add model access groups on ui dropdown when creating teams + keys
* refactor(parallel_request_limiter_v2.py): Migrate multi instance rate limiting to OSS
Closes https://github.com/BerriAI/litellm/issues/10052
* refactor(managed_files.py): move enterprise feature into enterprise folder
prevent unexpected surprises
* refactor: safely handle enterprise hooks
* fix: fix ruff check errors
* fix(files_endpoints.py): cleanup enterprise code from OSS
* refactor: complete cleanup
* fix(managed_files.py): complete cleanup
* fix(managed_files.py): instrument to be able to update deployment values post-router selection and just before making llm call
* fix(managed_files.py): instrument to be able to update deployment values post-router selection and just before making llm call
* fix: fix linting error
* fix: fix linting error
Krrish Dholakia