litellm VS label-studio

There are a few libs that already abstract this away, for example:

- https://github.com/BerriAI/litellm

- https://jxnl.github.io/instructor/

- langchain

It's not hard for me to imagine a future where there is something like the CNCF for AI models, tools, and infra.

Hey, are you just looking for litellm - https://github.com/BerriAI/litellm

context - i'm the repo maintainer

It should be possible using LiteLLM and a patch or a proxy.

https://github.com/BerriAI/litellm

This article will demonstrate how txtai can integrate with llama.cpp, LiteLLM and custom generation methods. For custom generation, we'll show how to run inference with a Mamba model.

I use this with ollama and works perfectly https://github.com/BerriAI/litellm

Another abstraction layer library is: https://github.com/BerriAI/litellm

For me the killer feature of a library like this would be if it implemented function calling. Even if it was for a very restricted grammar - like the traditional ReAct prompt:

  Solve a question answering task with interleaving Thought, Action, Observation   usteps. Thought can reason about the current situation, and Action can be three types:

Welcome to Open Interpreter. ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── ▌ OpenAI API key not found To use GPT-4 (recommended) please provide an OpenAI API key. To use Code-Llama (free but less capable) press enter. ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── OpenAI API key: [the API Key I inputed] Tip: To save this key for later, run export OPENAI_API_KEY=your_api_key on Mac/Linux or setx OPENAI_API_KEY your_api_key on Windows. ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── ▌ Model set to GPT-4 Open Interpreter will require approval before running code. Use interpreter -y to bypass this. Press CTRL-C to exit. > export OPENAI_API_KEY=your_api_key Give Feedback / Get Help: https://github.com/BerriAI/litellm/issues/new LiteLLM.Info: If you need to debug this error, use `litellm.set_verbose=True'. Traceback (most recent call last): File "/Library/Frameworks/Python.framework/Versions/3.12/bin/interpreter", line 8, in sys.exit(cli()) ^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/interpreter/core/core.py", line 22, in cli cli(self) File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/interpreter/cli/cli.py", line 254, in cli interpreter.chat() File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/interpreter/core/core.py", line 76, in chat for _ in self._streaming_chat(message=message, display=display): File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/interpreter/core/core.py", line 97, in _streaming_chat yield from terminal_interface(self, message) File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/interpreter/terminal_interface/terminal_interface.py", line 62, in terminal_interface for chunk in interpreter.chat(message, display=False, stream=True): File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/interpreter/core/core.py", line 105, in _streaming_chat yield from self._respond() File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/interpreter/core/core.py", line 131, in _respond yield from respond(self) File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/interpreter/core/respond.py", line 61, in respond for chunk in interpreter._llm(messages_for_llm): File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/interpreter/llm/setup_openai_coding_llm.py", line 94, in coding_llm response = litellm.completion(**params) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/litellm/utils.py", line 792, in wrapper raise e File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/litellm/utils.py", line 751, in wrapper result = original_function(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/litellm/timeout.py", line 53, in wrapper result = future.result(timeout=local_timeout_duration) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/concurrent/futures/_base.py", line 456, in result return self.__get_result() ^^^^^^^^^^^^^^^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result raise self._exception File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/litellm/timeout.py", line 42, in async_func return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/litellm/main.py", line 1183, in completion raise exception_type( ^^^^^^^^^^^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/litellm/utils.py", line 2959, in exception_type raise e File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/litellm/utils.py", line 2355, in exception_type raise original_exception File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/litellm/main.py", line 441, in completion raise e File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/litellm/main.py", line 423, in completion response = openai.ChatCompletion.create( ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/openai/api_resources/chat_completion.py", line 25, in create return super().create(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/openai/api_resources/abstract/engine_api_resource.py", line 155, in create response, _, api_key = requestor.request( ^^^^^^^^^^^^^^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/openai/api_requestor.py", line 299, in request resp, got_stream = self._interpret_response(result, stream) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/openai/api_requestor.py", line 710, in _interpret_response self._interpret_response_line( File "/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/openai/api_requestor.py", line 775, in _interpret_response_line raise self.handle_error_response( openai.error.InvalidRequestError: The model `gpt-4` does not exist or you do not have access to it. Learn more: https://help.openai.com/en/articles/7102672-how-can-i-access-gpt-4.

If instead you have a cohort on hand — -i.e., you do not want to send your data to a third party for any reason, or perhaps you have energetic undergrads — -then you could alternatively consider local, open-source annotation such as CVAT and Label Studio. Finally, nowadays, you might instead work with Large Multimodal Models to have them annotate your data; more on this awkward angle later.

14. LabelStudio by Human Signal | Github | tutorial

For instance, the COCO Annotator is a web-based image annotation tool tailored for the COCO dataset format, allowing collaborative labeling with features like attribute tagging and automatic segmentation. Similarly, Label Studio offers an easy-to-use interface for bounding box object labeling in images.

Huh?

I wrote my own system for classifying a stream of texts in Python, I might Open Source it one of these days but I have to get it to the point where it is modular enough that I can customize it to do the particular things I want without subjecting people to my whims... I use it every day and I'm not afraid to demo it because it is rock solid.

My understanding is that my system would not be hard to adapt to work on images for certain kinds of tasks.

Pytorch is open source, Huggingface is open source. CUDA isn't. This is

https://labelstud.io/

and for annotating text spans there are so many open source tools

https://github.com/doccano/doccano

I worked for a company a few years back that built annotation tools for projects we sold to customers but never quite got to a polished general purpose annotator. Today there are an overwhelming number of companies in this space and products I never heard of, many of which are cloud based or paid. Looks like a gold rush to me.

LabelStudio is free for single use. https://labelstud.io/

LabelStudio is pretty nice, and free & open source, but I have yet to try out their ML integration with a YOLO object detection model.

Label Studio is an open source data labeling tool that includes annotation functionality. It provides a simple user interface (UI) that lets you label various data types, including text, audio, time series data, videos, and images, and export the information to various model formats.