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pianzhikuang
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Model Details
Model Provider
pianzhikuang
Model Tree
Qwen/Qwen3.5-9B
Input Modalities
Output Modalities
Supported Functionality
The export was validated with Transformers 5.5.0 and PyTorch 2.10.0. Keep the supplied processor and perception chat template.
python
import torchfrom PIL import Imagefrom transformers import AutoProcessor, Qwen3_5ForConditionalGeneration model_id = "pianzhikuang/VisAlloc_Final"processor = AutoProcessor.from_pretrained(model_id)model = Qwen3_5ForConditionalGeneration.from_pretrained( model_id, dtype=torch.bfloat16, device_map="auto").eval() messages = [{"role": "user", "content": [ {"type": "image", "image": Image.open("image.jpg").convert("RGB")}, {"type": "text", "text": "Describe this image."},]}]inputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt",).to(model.device)with torch.inference_mode(): output = model.generate(**inputs, max_new_tokens=256, do_sample=False)print(processor.decode( output[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))training_metadata.json records the checkpoint, base revision, parameter count, template hash, and export validation. The weight file's SHA-256 is recorded in SHA256SUMS. All 760 tensors were checked for expected names, shapes, BF16 dtype, and finite values. The processor and chat template were validated locally.
The base model's Apache-2.0 license is included in LICENSE.
import torchfrom PIL import Imagefrom transformers import AutoProcessor, Qwen3_5ForConditionalGeneration model_id = "pianzhikuang/VisAlloc_Final"processor = AutoProcessor.from_pretrained(model_id)model = Qwen3_5ForConditionalGeneration.from_pretrained( model_id, dtype=torch.bfloat16, device_map="auto").eval() messages = [{"role": "user", "content": [ {"type": "image", "image": Image.open("image.jpg").convert("RGB")}, {"type": "text", "text": "Describe this image."},]}]inputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt",).to(model.device)with torch.inference_mode(): output = model.generate(**inputs, max_new_tokens=256, do_sample=False)print(processor.decode( output[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))