Completely skips samples the model has mastered Gives up to 5× more compute to hard/confidently-wrong samples Dynamically adjusts sample weights using a "Mountain Curriculum" Just dropped v0.3.0 with native LoRA/PEFT, BF16, gradient checkpointing, torch.compile, and 8-bit optimizer support. I'm currently building a clean UI for it. I'm a 17-year-old indie dev working on this. Would love honest feedback, especially from people who do a lot of fine-tuning.
jappleseed987•1h ago
One thing you might want to consider as you build out the UI: having good observability into your actual cost savings across different scenarios. When I've worked with teams doing LLM optimization, they often struggle to quantify their improvements across different providers or track cost trends over time.
Have you thought about how you'll measure and display the real-world cost impact of your optimizations? It could be powerful for users to see not just the compute reduction percentages, but actual dollar savings and trends.
Speaking of cost observability - I recently came across zenllm.io and they're doing some interesting work in this space, focused on tracking LLM costs across different providers. Might be worth checking out for inspiration on what metrics and visualizations work well for users trying to optimize their LLM spend.
Keep up the great work - this kind of innovation is exactly what the community needs!