Is using Qwen 3 0.6B to classify text data (call summaries in customer care) a bad idea?

I'm new to using local LLMs. I want to use this model because I think it might produce a faster output.

The actual purpose of the project is to figure out what were the calls about, figure out the major issues that were asked about. I want the data to be in a structured form so that the higher management can easily read it. Eventually want to visualise the output.

The call summaries include the enquiries , sentiments,agents reaction etc . In about 50-150 words. In last month there were about 2000 calls, would want to analyse each month's data.

My output format includes:

-Major Topics

-Sub topics under each Major topic

-Major Issues under each subtopic

-Brief description about the Issue

-How frequent each sub topic has occurred(counted using python)

-What was the customer sentiment towards the issue (summarized from all the given summies)

-How in general agents handled it(summarized from all the given summies)

-Key take away ( not sure about this part)

So current plan is to take every call and split each call into different issues,one call can have multiple issues discussed. Then classify each part of the call into different subtopics. I thought about giving the task of figuring out the subtopics to the llm model itself but realised that's not a good idea with this lower level model. So have made a set of subtopics. New issues can be given into 'others' category for now.

After every call is categorised into subtopics. Then the model would summaries all the calls(or parts of the calls) and list out the issues in them and give a brief (10-15 word) description about it. Python would count how many times the issues occurred.

Then from the summary the model summarises how the customers reacted to it and agents handled . Im not sure how efficient would be the 'key takeaways' part.

Can you give feedback about the approach. What can be improved.

PC has an integrated gpu of 8gb ,total 16gb RAM.