finetune reproduction
Thank you for your open source, I am replicating your fine-tuning process according to the code on github. Do the results of train loss=0.16 and eval_loss=0.21 I trained on the 75k dataset match yours? I will continue training on the 110k dataset.
I trained for 4 epochs and indeed started overfitting after the second epoch.
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“Impressive results! Seeing how Magicoder-S-DS-6.7B generalizes on large datasets makes me think about practical applications here in the Philippines, like streamlining processes for NBI or Barangay Clearance verification. AI models like this could really help government services handle big data faster and more securely.”
Just tried experimenting with this model for small code generation tasks and I’m honestly impressed by how structured the outputs are compared to many other 6–7B models. It seems to follow instructions more reliably when the prompt is clear and task-oriented. I’m curious how it performs on larger multi-file logic or debugging scenarios — has anyone tested it on real project-level code rather than short snippets?
The results on Magicoder-S-DS-6.7B are impressive. Models that handle large-scale structured data like this could have useful real-world applications beyond coding. In government systems that deal with heavy record verification, AI could help improve speed and accuracy, similar to how online processes are evolving for NBI clearance verification.
It’ll be interesting to see how models like this move from benchmarks into practical integrations.
Impressive results what stands out isn’t just how these models handle large datasets, but how they could simplify real-world processes that people struggle with daily.
In many government systems, delays don’t just come from data processing but from unclear schedules, availability, and user confusion during the process itself. Even small improvements in guidance can make a big difference.
For example, having a clear NBI schedule and availability guide helps users plan ahead and avoid unnecessary delays.
If AI models like this are integrated properly, they could improve not just speed, but the overall user experience of these systems.
Impressive results what stands out isn’t just how these models handle large datasets, but how they could simplify real-world processes that people struggle with daily.
In many government systems, delays don’t just come from data processing but from unclear schedules, availability, and user confusion during the process itself. Even small improvements in guidance can make a big difference.
For example, having a clear NBI schedule and availability guide helps users plan ahead and avoid unnecessary delays.
If AI models like this are integrated properly, they could improve not just speed, but the overall user experience of these systems. Learn more at the NBI appointment.
Thanks for this information :)