Feature: Fine-tune TinyLlama and Qwen2.5-coder models for Magistrala and Prism codebase #27
Description
Is your feature request related to a problem? Please describe.
No
Describe the feature you are requesting, as well as the possible use case(s) for it.
As LLM can fine-tuned on custom data sets, so can SLMs.
We want to fine-tune:
- TinyLlama
- Phi-3
And we want to fine-tune them on our custom Magistrala, Prism and Cocos repositories, so that we can enhance their intelligence for code generation for our purposes.
We want to compare:
- Which is better to fine-tune - more documented, easier, faster, etc ...
- Which shows better result after fine-tuning
Some references:
- https://dongreanay.medium.com/fine-tuning-the-tiny-llama-model-on-a-custom-dataset-9d1ce4eb663d
- https://www.kaggle.com/code/tommyadams/fine-tuning-tinyllama
- https://www.reddit.com/r/LocalLLaMA/comments/16t7la1/fine_tune_llama_for_code_generation_seek/
- https://techcommunity.microsoft.com/t5/ai-machine-learning-blog/finetune-small-language-model-slm-phi-3-using-azure-machine/ba-p/4130399
- https://www.datacamp.com/tutorial/phi-3-tutorial
- https://www.kaggle.com/code/lucamassaron/fine-tune-phi-3-for-sentiment-analysis
Analysis should be done if we should use fine-tuning or RAG for this purpose: https://medium.com/@bijit211987/when-to-apply-rag-vs-fine-tuning-90a34e7d6d25
Indicate the importance of this feature to you.
Must-have
Anything else?
No response