add test_qwen3_vl.py

tests/models/ovis/image_to_test_dataset.py

@@ -7,6 +7,7 @@
 from gptqmodel.models.definitions.base_qwen2_vl import BaseQwen2VLGPTQ
 from gptqmodel.models.definitions.ovis import OvisQModel
 from gptqmodel.models.definitions.ovis2 import Ovis2QModel
+from gptqmodel.models.definitions.qwen3_vl import Qwen3_VLQModel
 
 
 def format_ovis_dataset(image, assistant):
@@ -95,4 +96,7 @@ def get_calib_dataset(model):
     if isinstance(model, BaseQwen2_5_OmniGPTQ):
         return prepare_dataset(format_qwen2_5_omni_dataset, n_sample=20)
 
+    if isinstance(model, Qwen3_VLQModel):
+        return prepare_dataset(format_qwen2_vl_dataset, n_sample=20)
+
     raise NotImplementedError(f"Unsupported MODEL: {model.__class__}")

tests/models/test_qwen3_vl.py

@@ -0,0 +1,75 @@
+# SPDX-FileCopyrightText: 2024-2025 ModelCloud.ai
+# SPDX-FileCopyrightText: 2024-2025 qubitium@modelcloud.ai
+# SPDX-License-Identifier: Apache-2.0
+# Contact: qubitium@modelcloud.ai, x.com/qubitium
+
+from model_test import ModelTest
+
+from gptqmodel.models.definitions.qwen2_vl import Qwen2VLQModel
+from gptqmodel.utils.eval import EVAL
+
+
+class TestQwen2_VL(ModelTest):
+    NATIVE_MODEL_ID = "/monster/data/model/Qwen3-VL-2B-Instruct/"
+    EVAL_TASKS = {
+        EVAL.LM_EVAL.ARC_CHALLENGE: {
+            "chat_template": True,
+            "acc": {"value": 0.3618, "floor_pct": 0.04},
+            "acc_norm": {"value": 00.3882, "floor_pct": 0.04},
+        },
+    }
+    TRUST_REMOTE_CODE = False
+    EVAL_BATCH_SIZE = 6
+
+    def test_qwen3_vl(self):
+        model, tokenizer, processor = self.quantModel(self.NATIVE_MODEL_ID, trust_remote_code=self.TRUST_REMOTE_CODE,
+                                                      dtype=self.TORCH_DTYPE)
+
+        # check image to text
+        messages = [
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image",
+                        "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
+                    },
+                    {"type": "text", "text": "Describe this image."},
+                ],
+            }
+        ]
+        # Preparation for inference
+        text = processor.apply_chat_template(
+            messages, tokenize=False, add_generation_prompt=True
+        )
+
+        image_inputs = Qwen2VLQModel.process_vision_info(messages)
+        inputs = processor(
+            text=[text],
+            images=image_inputs,
+            videos=None,
+            padding=True,
+            return_tensors="pt",
+        )
+        inputs = inputs.to("cuda")
+
+        # Inference: Generation of the output
+        generated_ids = model.generate(**inputs, max_new_tokens=128)
+        generated_ids_trimmed = [
+            out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+        ]
+        output_text = processor.batch_decode(
+            generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+        )[0]
+        print("output_text:", output_text)
+
+        self.assertIn("dog", output_text)
+
+
+        # check lm_eval results
+        self.check_kernel(model, self.KERNEL_INFERENCE)
+
+        task_results = self.lm_eval(model=model,
+                                    trust_remote_code=self.TRUST_REMOTE_CODE,
+                                    delete_quantized_model=self.DELETE_QUANTIZED_MODEL)
+        self.check_results(task_results)