fix: Mistral Small 4 fails to start due to config/weight format mismatch (#21620)

Co-authored-by: mengxiancheng03 <mengxiancheng03@kuaishou.com>
Co-authored-by: Baizhou Zhang <sobereddiezhang@gmail.com>
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

python/sglang/srt/server_args.py

@@ -3157,22 +3157,42 @@ class ServerArgs:
     def _is_mistral_native_format(self) -> bool:
         """Detect if the model uses Mistral native format (params.json + consolidated weights).
 
-        Models like Mistral-7B-Instruct-v0.3 have BOTH params.json (native) and
-        config.json (HF standard). When both exist, prefer the HF format to avoid
-        parameter name mismatches between consolidated.safetensors (native names
-        like layers.0.attention.wk.weight) and HuggingFace model classes (names
-        like model.layers.0.self_attn.k_proj.weight).
+        When both params.json and config.json exist, default to HF format to
+        avoid weight-name mismatches (e.g. Mistral-7B-Instruct-v0.3).
+
+        Exception: models routed through ``_load_mistral_large_3_for_causal_LM``
+        (mistral-large-3, mistral-small-4, leanstral) build their config from
+        params.json and expect native weight names, so native format is required
+        even when config.json is also present.
         """
+        # Keep in sync with the name checks in
+        # hf_transformers_utils.py::get_config / get_tokenizer.
+        _MISTRAL_NATIVE_CONFIG_PATTERNS = (
+            "mistral-large-3",
+            "mistral-small-4",
+            "leanstral",
+        )
+
+        def _check_format(has_params: bool, has_hf_config: bool) -> bool:
+            if has_params and not has_hf_config:
+                return True
+            if has_params and has_hf_config:
+                model_lower = str(self.model_path).lower()
+                if any(name in model_lower for name in _MISTRAL_NATIVE_CONFIG_PATTERNS):
+                    return True
+            return False
+
         if os.path.isdir(self.model_path):
             has_params = os.path.exists(os.path.join(self.model_path, "params.json"))
             has_hf_config = os.path.exists(os.path.join(self.model_path, "config.json"))
-            return has_params and not has_hf_config
+            return _check_format(has_params, has_hf_config)
+
         # For hub models, check remote files
         try:
             from huggingface_hub import HfApi
 
             files = {s.rfilename for s in HfApi().model_info(self.model_path).siblings}
-            return "params.json" in files and "config.json" not in files
+            return _check_format("params.json" in files, "config.json" in files)
         except Exception:
             return False
 

test/registered/models/test_ministral4_models.py

@@ -0,0 +1,32 @@
+import unittest
+
+from sglang.test.ci.ci_register import register_cuda_ci
+from sglang.test.kits.eval_accuracy_kit import GSM8KMixin
+from sglang.test.kits.mmmu_vlm_kit import MMMUMixin
+from sglang.test.server_fixtures.default_fixture import DefaultServerBase
+from sglang.test.server_fixtures.mmmu_fixture import MMMUServerBase
+
+register_cuda_ci(
+    est_time=200,
+    suite="stage-b-test-2-gpu-large",
+)
+
+MODEL = "mistralai/Mistral-Small-4-119B-2603"
+
+
+class TestMistralSmall4TextOnly(GSM8KMixin, DefaultServerBase):
+    gsm8k_accuracy_thres = 0.9
+    model = MODEL
+    other_args = ["--tp-size", "2", "--trust-remote-code"]
+
+
+class TestMistralSmall4MMMU(MMMUMixin, MMMUServerBase):
+    accuracy = 0.45
+    model = MODEL
+    other_args = ["--tp-size", "2", "--trust-remote-code"]
+    mmmu_args = ["--limit=0.1"]
+    """`--limit=0.1`: 10 percent of each task - this is fine for testing since the nominal result isn't interesting - this run is just to prevent relative regressions."""
+
+
+if __name__ == "__main__":
+    unittest.main()