check out https://github.com/opendilab/awesome-RLHF?tab=readme-ov-file#codebases
you might need a basic understanding of python to set up these repos, but once they are set up the docs are usually great for guiding you how to get started.
other than that you could try some Saas services that do not require you set up your own code base:
1. **Entry Point AI**:
- A modern fine-tuning platform for proprietary and open-source large language models, including GPT, Llama-2, and Mistral.
- It simplifies the fine-tuning process, making it accessible even with a few dozen training examples[6].
2. **Hugging Face**:
- Provides access to thousands of pre-trained models for a wide range of tasks.
- Offers detailed guides and tools for fine-tuning pre-trained models with deep learning frameworks of your choice[11].
3. **Trudo AI**:
- A no-code platform that allows users to fine-tune OpenAI GPT-3 models using spreadsheets, enhancing apps with personalized AI capabilities[21].
4. **Fine-Tuner.ai**:
- Listed as one of the AI tools for fine-tuning platforms, although specific details about its features are not provided in the search results[3].
5. **OpenAI API**:
- Offers fine-tuning capabilities that provide higher quality results than prompting alone and the ability to train on more specific data[9].
6. **Snorkel AI**:
- Focuses on programmatic labeling and fine-tuning for enterprise use cases, providing a platform for error analysis, targeted labeling, and collaboration with internal experts[10].
7. **Vertex AI by Google Cloud**:
- Supports supervised tuning, reinforcement learning from human feedback (RLHF) tuning, and model distillation for tuning language foundation models[18].
8. **DeepSpeed**:
- An optional tool mentioned for optimizing training and inference jobs, which can be used in conjunction with off-the-shelf pre-trained models[13].
When choosing an off-the-shelf solution for fine-tuning AI models, it's important to consider the specific objectives, the volume and quality of data available, compliance and ethics, risk management, and performance management criteria[1]. Each platform offers different features and capabilities, so selecting the right one will depend on the use case, available resources, and desired outcomes.
Links:
[1] https://shelf.io/blog/fine-tuning-llms-for-ai-accuracy-and-effectiveness/
[2] https://www.sciencedirect.com/science/article/pii/S2666651021000231
[3] https://topai.tools/s/AI-fine-tuning-platform
[4] https://www.linkedin.com/pulse/tailoring-ai-your-business-needs-how-fine-tune-large-language
[5] https://neptune.ai/blog/hugging-face-pre-trained-models-find-the-best
[6] https://www.entrypointai.com
[7] https://techstrong.ai/articles/when-to-use-off-the-shelf-ai-versus-custom-models/
[8] https://spotintelligence.com/2023/10/13/pre-trained-models/
[9] https://platform.openai.com/docs/guides/fine-tuning
[10] https://snorkel.ai/how-to-fine-tune-large-language-models-for-enterprise-use-cases/
[11] https://huggingface.co/docs/transformers/en/training
[12] https://humansignal.com/fine-tuning-models/
[13] https://presencepg.com/journal/fine-tuning-llms
[14] https://www.linkedin.com/pulse/mastering-fine-tuning-enhancing-pre-trained-models-tasks-marzougui-zb3zf
[15] https://topai.tools/s/OpenAI-GPT-3-model-fine-tuning-tool
[16] https://www.linkedin.com/pulse/custom-ai-vs-off-the-shelf-solutions-navigating-complex-terrain-oxzyc
[17] https://www.tensorflow.org/tutorials/images/transfer_learning
[18] https://cloud.google.com/vertex-ai/docs/generative-ai/models/tune-models
[19] https://www.techtarget.com/searchenterpriseai/definition/fine-tuning
[20] https://productcoalition.com/fine-tuning-pre-trained-models-unleashing-the-power-of-generative-ai-1b58fc903554?gi=be624ed69918
[21] https://deepgram.com/ai-apps/trudo-ai
[22] https://dlabs.ai/blog/machine-learning-off-the-shelf-models-or-custom-build-pros-and-cons/
