language:
- zh
- en
library_name: transformers
license: mit
pipeline_tag: image-text-to-text
base_model: - zai-org/GLM-4.6V-Flash
tags: - abliterated
- uncensored
huihui-ai/Huihui-GLM-4.6V-Flash-abliterated
This is an uncensored version of zai-org/GLM-4.6V-Flash created with abliteration (see remove-refusals-with-transformers to know more about it).
It was only the text part that was processed, not the image part.
Quick Start with Transformers
1. Vision
from transformers import AutoProcessor, Glm4vForConditionalGeneration
import torch
MODEL_PATH = "huihui-ai/Huihui-GLM-4.6V-Flash-abliterated"
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"url": "https://upload.wikimedia.org/wikipedia/commons/f/fa/Grayscale_8bits_palette_sample_image.png"
},
{
"type": "text",
"text": "describe this image"
}
],
}
]
processor = AutoProcessor.from_pretrained(MODEL_PATH, use_fast=True)
model = Glm4vForConditionalGeneration.from_pretrained(
pretrained_model_name_or_path=MODEL_PATH,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True,
)
inputs = processor.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_dict=True,
return_tensors="pt"
).to(model.device)
inputs.pop("token_type_ids", None)
generated_ids = model.generate(**inputs, max_new_tokens=8192)
output_text = processor.decode(generated_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False)
print(output_text)
2. Chat
from transformers import AutoProcessor, Glm4vForConditionalGeneration
import torch
MODEL_PATH = "huihui-ai/Huihui-GLM-4.6V-Flash-abliterated"
messages = [
{
"role": "user",
"content": [
{
"type": "image",
},
{
"type": "text",
"text": "In Python, write a function to reverse a string, for example, turning input 'hello' into 'olleh'."
}
],
}
]
processor = AutoProcessor.from_pretrained(MODEL_PATH, use_fast=True)
model = Glm4vForConditionalGeneration.from_pretrained(
pretrained_model_name_or_path=MODEL_PATH,
torch_dtype="auto",
device_map="auto",
trust_remote_code=True,
)
inputs = processor.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_dict=True,
return_tensors="pt"
).to(model.device)
inputs.pop("token_type_ids", None)
generated_ids = model.generate(**inputs, max_new_tokens=8192)
output_text = processor.decode(generated_ids[0][inputs["input_ids"].shape[1]:], skip_special_tokens=False)
print(output_text)
Usage Warnings
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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