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Model Details
Model Provider
huihui-ai
Model Tree
Qwen/Qwen3-VL-4B-Instruct
Input Modalities
Output Modalities
Supported Functionality
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from transformers import Qwen3VLForConditionalGeneration, AutoProcessor, BitsAndBytesConfigimport osimport torch cpu_count = os.cpu_count()print(f"Number of CPU cores in the system: {cpu_count}")half_cpu_count = cpu_count // 2os.environ["MKL_NUM_THREADS"] = str(half_cpu_count)os.environ["OMP_NUM_THREADS"] = str(half_cpu_count)torch.set_num_threads(half_cpu_count) MODEL_ID = "huihui-ai/Huihui-Qwen3-VL-4B-Instruct-abliterated-FP8" # default: Load the model on the available device(s)model = Qwen3VLForConditionalGeneration.from_pretrained( MODEL_ID, device_map="auto", trust_remote_code=True, dtype=torch.bfloat16, low_cpu_mem_usage=True,)# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.# model = Qwen3VLForConditionalGeneration.from_pretrained(# "Qwen/Qwen3-VL-235B-A22B-Instruct",# dtype=torch.bfloat16,# attn_implementation="flash_attention_2",# device_map="auto",# ) processor = AutoProcessor.from_pretrained(MODEL_ID) image_path = "/png/cars.jpg" messages = [ { "role": "user", "content": [ { "type": "image", "image": f"{image_path}", }, {"type": "text", "text": "Describe this image."}, ], }] # Preparation for inferenceinputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt").to(model.device) # Inference: Generation of the outputgenerated_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)print(output_text)Risk of Sensitive or Controversial Outputs: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs.
Not Suitable for All Audiences: Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security.
Legal and Ethical Responsibilities: Users must ensure their usage complies with local laws and ethical standards. Generated content may carry legal or ethical risks, and users are solely responsible for any consequences.
Research and Experimental Use: It is recommended to use this model for research, testing, or controlled environments, avoiding direct use in production or public-facing commercial applications.
Monitoring and Review Recommendations: Users are strongly advised to monitor model outputs in real-time and conduct manual reviews when necessary to prevent the dissemination of inappropriate content.
No Default Safety Guarantees: Unlike standard models, this model has not undergone rigorous safety optimization. huihui.ai bears no responsibility for any consequences arising from its use.
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bc1qqnkhuchxw0zqjh2ku3lu4hq45hc6gy84uk70gefrom transformers import Qwen3VLForConditionalGeneration, AutoProcessor, BitsAndBytesConfigimport osimport torchcpu_count = os.cpu_count()print(f"Number of CPU cores in the system: {cpu_count}")half_cpu_count = cpu_count // 2os.environ["MKL_NUM_THREADS"] = str(half_cpu_count)os.environ["OMP_NUM_THREADS"] = str(half_cpu_count)torch.set_num_threads(half_cpu_count)MODEL_ID = "huihui-ai/Huihui-Qwen3-VL-4B-Instruct-abliterated-FP8"# default: Load the model on the available device(s)model = Qwen3VLForConditionalGeneration.from_pretrained( MODEL_ID, device_map="auto", trust_remote_code=True, dtype=torch.bfloat16, low_cpu_mem_usage=True,)# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.# model = Qwen3VLForConditionalGeneration.from_pretrained(# "Qwen/Qwen3-VL-235B-A22B-Instruct",# dtype=torch.bfloat16,# attn_implementation="flash_attention_2",# device_map="auto",# )processor = AutoProcessor.from_pretrained(MODEL_ID)image_path = "/png/cars.jpg"messages = [ { "role": "user", "content": [ { "type": "image", "image": f"{image_path}", }, {"type": "text", "text": "Describe this image."}, ], }]# Preparation for inferenceinputs = processor.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt").to(model.device)# Inference: Generation of the outputgenerated_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)print(output_text) bc1qqnkhuchxw0zqjh2ku3lu4hq45hc6gy84uk70ge