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huihui-ai
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Model Details
Model Provider
huihui-ai
Model Tree
openbmb/MiniCPM-V-4_5
Input Modalities
Output Modalities
Supported Functionality
(llama-mtmd-cli needs to be compiled.)
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llama-mtmd-cli -m huihui-ai/Huihui-Qwen3-8B-abliterated/GGUF/ggml-model-Q4_K_M.gguf --mmproj huihui-ai/Huihui-Qwen3-8B-abliterated/GGUF/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image abc.png -p "What is in the image?" text
import torchfrom PIL import Imagefrom transformers import AutoModel, AutoTokenizer torch.manual_seed(100) model = AutoModel.from_pretrained('huihui-ai/Huihui-MiniCPM-V-4_5-abliterated', trust_remote_code=True, attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eagermodel = model.eval().cuda()tokenizer = AutoTokenizer.from_pretrained('huihui-ai/Huihui-MiniCPM-V-4_5-abliterated', trust_remote_code=True) image = Image.open('./assets/minicpmo2_6/show_demo.jpg').convert('RGB') enable_thinking=False # If `enable_thinking=True`, the thinking mode is enabled.stream=True # If `stream=True`, the answer is string # First round chat question = "What is the landform in the picture?"msgs = [{'role': 'user', 'content': [image, question]}] answer = model.chat( msgs=msgs, tokenizer=tokenizer, enable_thinking=enable_thinking, stream=True) generated_text = ""for new_text in answer: generated_text += new_text print(new_text, flush=True, end='') # Second round chat, pass history context of multi-turn conversationmsgs.append({"role": "assistant", "content": [generated_text]})msgs.append({"role": "user", "content": ["What should I pay attention to when traveling here?"]}) answer = model.chat( msgs=msgs, tokenizer=tokenizer, stream=True) generated_text = ""for new_text in answer: generated_text += new_text print(new_text, flush=True, end='')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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bc1qqnkhuchxw0zqjh2ku3lu4hq45hc6gy84uk70gellama-mtmd-cli -m huihui-ai/Huihui-Qwen3-8B-abliterated/GGUF/ggml-model-Q4_K_M.gguf --mmproj huihui-ai/Huihui-Qwen3-8B-abliterated/GGUF/mmproj-model-f16.gguf -c 4096 --temp 0.7 --top-p 0.8 --top-k 100 --repeat-penalty 1.05 --image abc.png -p "What is in the image?" import torchfrom PIL import Imagefrom transformers import AutoModel, AutoTokenizertorch.manual_seed(100)model = AutoModel.from_pretrained('huihui-ai/Huihui-MiniCPM-V-4_5-abliterated', trust_remote_code=True, attn_implementation='sdpa', torch_dtype=torch.bfloat16) # sdpa or flash_attention_2, no eagermodel = model.eval().cuda()tokenizer = AutoTokenizer.from_pretrained('huihui-ai/Huihui-MiniCPM-V-4_5-abliterated', trust_remote_code=True)image = Image.open('./assets/minicpmo2_6/show_demo.jpg').convert('RGB')enable_thinking=False # If `enable_thinking=True`, the thinking mode is enabled.stream=True # If `stream=True`, the answer is string# First round chat question = "What is the landform in the picture?"msgs = [{'role': 'user', 'content': [image, question]}]answer = model.chat( msgs=msgs, tokenizer=tokenizer, enable_thinking=enable_thinking, stream=True)generated_text = ""for new_text in answer: generated_text += new_text print(new_text, flush=True, end='')# Second round chat, pass history context of multi-turn conversationmsgs.append({"role": "assistant", "content": [generated_text]})msgs.append({"role": "user", "content": ["What should I pay attention to when traveling here?"]})answer = model.chat( msgs=msgs, tokenizer=tokenizer, stream=True)generated_text = ""for new_text in answer: generated_text += new_text print(new_text, flush=True, end='')