Upload Qwen_Qwen3-VL-32B-Instruct-FP8_1.txt with huggingface_hub

Qwen_Qwen3-VL-32B-Instruct-FP8_1.txt

@@ -0,0 +1,63 @@
+```CODE: 
+# Load model directly
+from transformers import AutoProcessor, AutoModelForVision2Seq
+
+processor = AutoProcessor.from_pretrained("Qwen/Qwen3-VL-32B-Instruct-FP8")
+model = AutoModelForVision2Seq.from_pretrained("Qwen/Qwen3-VL-32B-Instruct-FP8")
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
+            {"type": "text", "text": "What animal is on the candy?"}
+        ]
+    },
+]
+inputs = processor.apply_chat_template(
+	messages,
+	add_generation_prompt=True,
+	tokenize=True,
+	return_dict=True,
+	return_tensors="pt",
+).to(model.device)
+
+outputs = model.generate(**inputs, max_new_tokens=40)
+print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
+```
+
+ERROR: 
+Traceback (most recent call last):
+  File "/tmp/Qwen_Qwen3-VL-32B-Instruct-FP8_1H3mGsX.py", line 18, in <module>
+    model = AutoModelForVision2Seq.from_pretrained("Qwen/Qwen3-VL-32B-Instruct-FP8")
+  File "/tmp/.cache/uv/environments-v2/3a120645d4f704f5/lib/python3.13/site-packages/transformers/models/auto/modeling_auto.py", line 2289, in from_pretrained
+    return super().from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)
+           ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/tmp/.cache/uv/environments-v2/3a120645d4f704f5/lib/python3.13/site-packages/transformers/models/auto/auto_factory.py", line 604, in from_pretrained
+    return model_class.from_pretrained(
+           ~~~~~~~~~~~~~~~~~~~~~~~~~~~^
+        pretrained_model_name_or_path, *model_args, config=config, **hub_kwargs, **kwargs
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+    )
+    ^
+  File "/tmp/.cache/uv/environments-v2/3a120645d4f704f5/lib/python3.13/site-packages/transformers/modeling_utils.py", line 277, in _wrapper
+    return func(*args, **kwargs)
+  File "/tmp/.cache/uv/environments-v2/3a120645d4f704f5/lib/python3.13/site-packages/transformers/modeling_utils.py", line 4881, in from_pretrained
+    hf_quantizer, config, dtype, device_map = get_hf_quantizer(
+                                              ~~~~~~~~~~~~~~~~^
+        config, quantization_config, dtype, from_tf, from_flax, device_map, weights_only, user_agent
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+    )
+    ^
+  File "/tmp/.cache/uv/environments-v2/3a120645d4f704f5/lib/python3.13/site-packages/transformers/quantizers/auto.py", line 319, in get_hf_quantizer
+    hf_quantizer.validate_environment(
+    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^
+        dtype=dtype,
+        ^^^^^^^^^^^^
+    ...<3 lines>...
+        weights_only=weights_only,
+        ^^^^^^^^^^^^^^^^^^^^^^^^^^
+    )
+    ^
+  File "/tmp/.cache/uv/environments-v2/3a120645d4f704f5/lib/python3.13/site-packages/transformers/quantizers/quantizer_finegrained_fp8.py", line 48, in validate_environment
+    raise RuntimeError("No GPU or XPU found. A GPU or XPU is needed for FP8 quantization.")
+RuntimeError: No GPU or XPU found. A GPU or XPU is needed for FP8 quantization.