Use Latest Cut Cross Entropy (#2392)

* Update __init__.py

* Update README.md

* Update cutcrossentropy_install.py

* add test

tests/e2e/integrations/test_cut_cross_entropy.py

@@ -69,6 +69,51 @@ class TestCutCrossEntropyIntegration:
             train(cfg=cfg, dataset_meta=dataset_meta)
             check_model_output_exists(temp_dir, cfg)
 
+    # pylint: disable=redefined-outer-name
+    def test_qwen2_w_cce(self, temp_dir):
+        cfg = DictDefault(
+            {
+                "base_model": "Qwen/Qwen2.5-0.5B",
+                "plugins": [
+                    "axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin",
+                ],
+                "cut_cross_entropy": True,
+                "sequence_len": 1024,
+                "val_set_size": 0.1,
+                "special_tokens": {
+                    "pad_token": "<|endoftext|>",
+                },
+                "datasets": [
+                    {
+                        "path": "mhenrichsen/alpaca_2k_test",
+                        "type": "alpaca",
+                    },
+                ],
+                "num_epochs": 1,
+                "micro_batch_size": 4,
+                "gradient_accumulation_steps": 1,
+                "learning_rate": 0.00001,
+                "optimizer": "adamw_torch_fused",
+                "output_dir": temp_dir,
+                "lr_scheduler": "cosine",
+                "save_safetensors": True,
+                "max_steps": 10,
+                "bf16": "auto",
+            }
+        )
+        prepare_plugins(cfg)
+        normalize_config(cfg)
+        cli_args = TrainerCliArgs()
+        dataset_meta = load_datasets(cfg=cfg, cli_args=cli_args)
+
+        major, minor, _ = get_pytorch_version()
+        if (major, minor) < (2, 4):
+            with pytest.raises(ImportError):
+                train(cfg=cfg, dataset_meta=dataset_meta)
+        else:
+            train(cfg=cfg, dataset_meta=dataset_meta)
+            check_model_output_exists(temp_dir, cfg)
+
     @pytest.mark.parametrize(
         "attention_type",
         [