Auto-detect GPU: bfloat16+batch2+gen8 on A100, float16+batch1+gen4 on T4 — same script works on both

Reduce max_completion_length to 160 for T4 speed: target 1000 steps in <8hrs

Optimize for Kaggle P100: float16, batch=1, grad_accum=8, num_gen=4, max_completion=256, lora_r=8

Fix GRPOConfig: rename max_new_tokens to max_completion_length for trl==0.14.0

Align gradio version with Hugging Face Space builder2

Stabilize Space runtime: pin ML deps and disable runtime package drift

Pin torch to cu121 build + use model.device instead of hardcoded cuda string

Replace unsloth with bitsandbytes+peft: fixes CUDA driver incompatibility on HF A100

Reduce training to 500 steps with tightened curriculum for A10G budget

Fix eval device selection with CUDA-safe fallback

Optimize for A100 80GB: 8 generations, batch 4, lr 2e-5, dense logging

Restore full 1000-step training with original curriculum

Reduce training to 500 steps with tightened curriculum for A10G budget

Add Gradio training monitor and fix subprocess python path

Update: Started making changes for the hackathon