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mudler / LocalAI

:robot: The free, Open Source alternative to OpenAI, Claude and others. Self-hosted and local-first. Drop-in replacement for OpenAI, running on consumer-grade hardware. No GPU required. Runs gguf, transformers, diffusers and many more models architectures. Features: Generate Text, Audio, Video, Images, Voice Cloning, Distributed, P2P inference
https://localai.io
MIT License
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feature: fine-tuning endpoint #596

Open mudler opened 1 year ago

mudler commented 1 year ago

Is your feature request related to a problem? Please describe.

Describe the solution you'd like

Describe alternatives you've considered

Additional context

localai-bot commented 1 year ago

:warning::warning::warning::warning::warning:

Hi! I'm a bot running with LocalAI ( a crazy experiment of @mudler ) - please beware that I might hallucinate sometimes!

_but.... I can also be funny or helpful :smilecat: and I can provide generally speaking good tips or places where to look after in the documentation or in the code based on what you wrote in the issue.

Don't engage in conversation with me, I don't support (yet) replying!

:warning::warning::warning::warning::warning:

I apologize for the confusion, but it seems like there is no issue description in this thread. Could you please provide more information so I can assist you better?

Sources:

Giancarlo1974 commented 1 year ago

Regarding the feature request for fine-tuning endpoint:

Description: Implement a fine-tuning endpoint in the existing system that allows users to fine-tune the AI model based on their specific needs and data.

Objective: Enable users to customize the AI model's behavior, improving its performance in their domain or specialized tasks.

Implementation: Create an API endpoint where users can upload their dataset and provide parameters to fine-tune the model. The system should then fine-tune the AI model and provide a downloadable version for the user.

Benefits: This feature enhances the adaptability of the AI system, making it more versatile and applicable to a wider range of real-world use cases.

Example: A healthcare company can fine-tune the model to better understand medical records, improving accuracy and relevance in medical-related queries.

Reference: Refer to similar functionalities in popular AI frameworks like Hugging Face's Transformers library, which allows fine-tuning models for various NLP tasks.