I did not quite get what we are trying to achieve.. You gave sample data which extracts text and creates json format.. Then you tested with a line and that also extracted json data.. So what is next step ? What exactly does this mean ? How is this fine tuning ? How are you training the last layer weights and biases ? was this an example of LORA or QLORA ?
It seems there might be some confusion between fine-tuning an open-source LLM (Large Language Model) and fine-tuning a closed-source model like OpenAI's API-based LLMs. With OpenAI models, we don’t have direct control over specific layers or weights. Instead, we fine-tune by submitting our data to their platform, which processes it and provides us with a new model ID. This is different from methods like LoRA or QLoRA, which are typically used for open-source models and involve directly adjusting specific layers or weights. Let me know if you'd like more details on either approach!
Yes, absolutely! Fine-tuning can definitely be applied to NL2SQL tasks. However, for the best results, it’s important to ensure that your training data includes examples covering each column and table at least once. This will help the model learn to handle the full range of potential queries.
yes please do with images and text. Also, can you do a video on RAG where for example its on all company documents but someone like a junior will have access to specific documents, a manager will have access to more documents, ceo will have access to all documents etc...
Hey I'm joining a company recently which related data work in Big query and looker studio so I just want to learn and upskill I have an idea of ML and DL which I was done in certificate so can I go with deep learning engineering profile right now or something else please suggest.
Yes please explain with images and text
I did not quite get what we are trying to achieve.. You gave sample data which extracts text and creates json format.. Then you tested with a line and that also extracted json data.. So what is next step ? What exactly does this mean ? How is this fine tuning ? How are you training the last layer weights and biases ? was this an example of LORA or QLORA ?
It seems there might be some confusion between fine-tuning an open-source LLM (Large Language Model) and fine-tuning a closed-source model like OpenAI's API-based LLMs. With OpenAI models, we don’t have direct control over specific layers or weights. Instead, we fine-tune by submitting our data to their platform, which processes it and provides us with a new model ID. This is different from methods like LoRA or QLoRA, which are typically used for open-source models and involve directly adjusting specific layers or weights. Let me know if you'd like more details on either approach!
Amazing video.
Can fine tuning be done for Nl2sql tasks too?
Yes, absolutely! Fine-tuning can definitely be applied to NL2SQL tasks. However, for the best results, it’s important to ensure that your training data includes examples covering each column and table at least once. This will help the model learn to handle the full range of potential queries.
yes please do with images and text. Also, can you do a video on RAG where for example its on all company documents but someone like a junior will have access to specific documents, a manager will have access to more documents, ceo will have access to all documents etc...
I could do but these is part of access control and specific to RAG
Hey I'm joining a company recently which related data work in Big query and looker studio so I just want to learn and upskill I have an idea of ML and DL which I was done in certificate so can I go with deep learning engineering profile right now or something else please suggest.
It would be better how the model was performing before and after fine tuning.
Rest all is good 💯.
Thanks for suggestions