diff --git a/src/llamafactory/data/processor/supervised.py b/src/llamafactory/data/processor/supervised.py
index 26e50d93..b5aba11b 100644
--- a/src/llamafactory/data/processor/supervised.py
+++ b/src/llamafactory/data/processor/supervised.py
@@ -62,7 +62,7 @@ class SupervisedDatasetProcessor(DatasetProcessor):
 
             if self.data_args.train_on_prompt:
                 source_label = source_ids
-            elif self.template.efficient_eos:
+            elif self.template.efficient_eos and turn_idx != 0:
                 source_label = [self.tokenizer.eos_token_id] + [IGNORE_INDEX] * (source_len - 1)
             else:
                 source_label = [IGNORE_INDEX] * source_len
diff --git a/src/llamafactory/data/template.py b/src/llamafactory/data/template.py
index 70f5e435..e28d462b 100644
--- a/src/llamafactory/data/template.py
+++ b/src/llamafactory/data/template.py
@@ -1069,7 +1069,9 @@ register_template(
     format_assistant=StringFormatter(slots=["{{content}}<|end|>"]),
     format_system=StringFormatter(slots=["<|start|>system<|message|>{{content}}<|end|>"]),
     default_system="You are ChatGPT, a large language model trained by OpenAI.",
+    thought_words=("<|channel|>analysis<|message|>", "<|end|><|start|>assistant<|channel|>final<|message|>"),
     efficient_eos=True,
+    template_class=ReasoningTemplate,
 )
 
 
diff --git a/src/llamafactory/extras/constants.py b/src/llamafactory/extras/constants.py
index 077a3497..14ae8c8c 100644
--- a/src/llamafactory/extras/constants.py
+++ b/src/llamafactory/extras/constants.py
@@ -126,6 +126,7 @@ class QuantizationMethod(str, Enum):
     QUANTO = "quanto"
     EETQ = "eetq"
     HQQ = "hqq"
+    MXFP4 = "mxfp4"
 
 
 class RopeScaling(str, Enum):
diff --git a/src/llamafactory/model/model_utils/quantization.py b/src/llamafactory/model/model_utils/quantization.py
index d3160f69..8b227b7f 100644
--- a/src/llamafactory/model/model_utils/quantization.py
+++ b/src/llamafactory/model/model_utils/quantization.py
@@ -90,12 +90,13 @@ def configure_quantization(
         if model_args.quantization_bit is not None:
             logger.warning_rank0("`quantization_bit` will not affect on the PTQ-quantized models.")
 
-        if is_deepspeed_zero3_enabled() or is_fsdp_enabled():
-            raise ValueError("DeepSpeed ZeRO-3 or FSDP is incompatible with PTQ-quantized models.")
-
         quantization_config: dict[str, Any] = getattr(config, "quantization_config", None)
         quant_method = quantization_config.get("quant_method", "")
 
+        if quant_method != QuantizationMethod.MXFP4 and (is_deepspeed_zero3_enabled() or is_fsdp_enabled()):
+            # mxfp4 will dequant the model weights
+            raise ValueError("DeepSpeed ZeRO-3 or FSDP is incompatible with PTQ-quantized models.")
+
         if quant_method == QuantizationMethod.GPTQ:
             check_version("gptqmodel>=2.0.0", mandatory=True)
             quantization_config.pop("disable_exllama", None)  # remove deprecated args