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README.md CHANGED
@@ -1,3 +1,75 @@
1
- ---
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- license: apache-2.0
3
- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
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+ library_name: transformers
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+ license: apache-2.0
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+ license_link: https://huggingface.co/huihui-ai/Qwen2-VL-2B-Instruct-abliterated/blob/main/LICENSE
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+ language:
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+ - en
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+ pipeline_tag: text-generation
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+ base_model: Qwen/Qwen2-VL-2B-Instruct
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+ tags:
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+ - chat
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+ - abliterated
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+ - uncensored
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+ ---
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+
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+ # huihui-ai/Qwen2-VL-2B-Instruct-abliterated
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+
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+
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+ This is an uncensored version of [Qwen2-VL-2B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-2B-Instruct) created with abliteration (see [this article](https://huggingface.co/blog/mlabonne/abliteration) to know more about it).
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+
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+ Special thanks to [@FailSpy](https://huggingface.co/failspy) for the original code and technique. Please follow him if you're interested in abliterated models.
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+
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+ ## Usage
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+ You can use this model in your applications by loading it with Hugging Face's `transformers` library:
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+
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+
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+ ```python
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+ from transformers import Qwen2VLForConditionalGeneration, AutoProcessor
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+ from qwen_vl_utils import process_vision_info
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+
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+ model = Qwen2VLForConditionalGeneration.from_pretrained(
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+ "huihui-ai/Qwen2-VL-2B-Instruct-abliterated", torch_dtype="auto", device_map="auto"
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+ )
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+ processor = AutoProcessor.from_pretrained("huihui-ai/Qwen2-VL-2B-Instruct-abliterated")
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+
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+ image_path = "/tmp/test.png"
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+
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+ messages = [
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+ {
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+ "role": "user",
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+ "content": [
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+ {
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+ "type": "image",
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+ "image": f"file://{image_path}",
44
+ },
45
+ {"type": "text", "text": "Please describe the content of the photo in detail"},
46
+ ],
47
+ }
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+ ]
49
+
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+ text = processor.apply_chat_template(
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+ messages, tokenize=False, add_generation_prompt=True
52
+ )
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+ image_inputs, video_inputs = process_vision_info(messages)
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+ inputs = processor(
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+ text=[text],
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+ images=image_inputs,
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+ videos=video_inputs,
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+ padding=True,
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+ return_tensors="pt",
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+ )
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+ inputs = inputs.to("cuda")
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+
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+ generated_ids = model.generate(**inputs, max_new_tokens=256)
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+ generated_ids_trimmed = [
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+ out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
66
+ ]
67
+ output_text = processor.batch_decode(
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+ generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
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+ )
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+ output_text = output_text[0]
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+
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+ print(output_text)
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+
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+ ```
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+
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+ "rope_scaling": {
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+ "type": "mrope",
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+ "mrope_section": [
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+ 16,
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+ ]
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+ },
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+ "vocab_size": 151936
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+ }
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+ "transformers_version": "4.37.0"
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+
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+ "<|box_end|>",
124
+ "<|quad_start|>",
125
+ "<|quad_end|>",
126
+ "<|vision_start|>",
127
+ "<|vision_end|>",
128
+ "<|vision_pad|>",
129
+ "<|image_pad|>",
130
+ "<|video_pad|>"
131
+ ],
132
+ "bos_token": null,
133
+ "chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}",
134
+ "clean_up_tokenization_spaces": false,
135
+ "eos_token": "<|im_end|>",
136
+ "errors": "replace",
137
+ "model_max_length": 32768,
138
+ "pad_token": "<|im_end|>",
139
+ "padding_side": "left",
140
+ "split_special_tokens": false,
141
+ "tokenizer_class": "Qwen2Tokenizer",
142
+ "unk_token": null
143
+ }
vocab.json ADDED
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