Skip flex_attention on pre-Ampere GPUs (T4, V100)

flex_attention Triton kernels require sm80+ (Ampere). On older GPUs the
dense Python fallback runs instead, but sdpa_dense_backward has a dtype
mismatch under fp16 autocast -- the matmul at line 904 of
torch/_higher_order_ops/flex_attention.py does
softmax_scores.to(query.dtype) @ grad_out where query.dtype is Half and
grad_out is Float, producing "expected scalar type Float but found Half".

This affected Ministral-3B/8B on T4 GPUs (issue #4295) and potentially
any model using flex_attention on pre-Ampere hardware.

Fix: check torch.cuda.get_device_capability() >= (8, 0) before enabling
flex_attention. Falls back to sdpa on older GPUs.

unsloth/models/_utils.py

@@ -234,6 +234,17 @@ def prefer_flex_attn_if_supported(model_class, config):
             model_class, "_supports_flex_attn", False
         ):
             return None
+        # flex_attention Triton kernels require sm80+ (Ampere and above).
+        # On older GPUs (T4/sm75, V100/sm70) the dense Python fallback runs
+        # instead, but sdpa_dense_backward has a dtype mismatch under fp16
+        # autocast (Half @ Float matmul). Skip flex_attention there.
+        import torch
+        if torch.cuda.is_available():
+            major, _ = torch.cuda.get_device_capability()
+            if major < 8:
+                return None
+        else:
+            return None
         # GPT-OSS, Mllama and Gemma3N use eager/sdpa attention during
         # inference since flex attention returns incorrect results or errors out.
         # GPT-OSS: left padding issues cause incorrect outputs.