Remove Gemma-4 from FORCE_FLOAT32 (#4875)

Gemma-4 does not need FORCE_FLOAT32. Testing shows that both float16 and
bfloat16 work correctly without the forced float32 override:

- Inference: identical outputs for float16 and bfloat16 (greedy decoding)
- Training (100 steps, 4-bit LoRA, SFT on FineTome-100k):
  - float16 final loss: 3.048
  - bfloat16 final loss: 3.065
  - Losses converge to within 0.02 by step 60
  - Grad norms healthy and comparable for both dtypes

The FORCE_FLOAT32 path was actually causing training divergence. With
it enabled, the compiled float32 run diverged at step ~28 with grad norms
collapsing to near zero and loss plateauing at ~12.4. Without it, both
dtypes train normally.

This enables float16 on Tesla T4 and other GPUs without bfloat16 support.

unsloth/models/loader.py

@@ -108,8 +108,6 @@ FORCE_FLOAT32 = [
     "gemma3n",
     "gpt_oss",
     "qwen3_5",  # Qwen3.5 GDN layers produce NaN grad norms in float16 training
-    "gemma4,",  # Add comma bc gemma4 will match gemma4_text
-    "gemma4_text",
 ]
 
 global DISABLE_COMPILE_MODEL_NAMES