Hello Everyone!

Last week we hosted a Community Roundtable in the Azure AI Discord on Fine-tuning. 

The Community Roundtables are a time where members of the Azure AI Discord can come and learn together on a specific AI topic that is voted on by the community. For this session, we covered Fine-tuning LLMs. The session started with a brief lighting talk from @nitya and then we open the stage for members to ask questions and talk about their experiences with fine-tuning. 

Here is the recording for anyone who missed it: 

Here is a summary of some of the questions and topics discussed generated with the help of CoPilot: 

Our next session in the AI Discord is an Office Hours about the RAG on Thursday, June 13th. This is an ask me anything and open floor discussion. 

See you in the Discord! 

– Korey 

​Hello Everyone!
 
Last week we hosted a Community Roundtable in the Azure AI Discord on Fine-tuning. 
 
The Community Roundtables are a time where members of the Azure AI Discord can come and learn together on a specific AI topic that is voted on by the community. For this session, we covered Fine-tuning LLMs. The session started with a brief lighting talk from @nitya and then we open the stage for members to ask questions and talk about their experiences with fine-tuning. 
 
Here is the recording for anyone who missed it: 

 
Here is a summary of some of the questions and topics discussed generated with the help of CoPilot: 
 

beginners. 0:59

Question: What is fine-tuning in the context of AI?

Answer: Fine-tuning is described as retraining an existing model with new data to improve its performance. 7:40

Question: Why should we fine-tune AI models?

Answer: Fine-tuning is appropriate if the response quality is not achievable with other techniques, considering the trade-offs such as cost efficiency. 8:50

Question: When should we consider fine-tuning AI models?

Answer: Fine-tuning should be considered only if the benefits outweigh the costs, after trying other approaches first. 10:22

Question: What are the steps involved in fine-tuning an AI model?

Answer: The steps include preparing data, training and evaluating the model, and then deploying and using it. 11:20

Question: How does one decide on the amount of data needed for fine-tuning?

Answer: The amount of data needed depends on the model, provider, and use case, ranging from a few hundred to thousands of samples. 11:45

Question: What are the considerations for deploying a fine-tuned AI model?

Answer: Considerations include deployment constraints, model rate limits, and validating the model in a playground before production. 13:53

Our next session in the AI Discord is an Office Hours about the RAG on Thursday, June 13th. This is an ask me anything and open floor discussion. 
 
See you in the Discord! 
 
– Korey   Read More

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