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---
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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/Qwen2.5-32B-Instruct-abliterated/blob/main/LICENSE
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language:
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- zho
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- eng
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- fra
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- spa
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- por
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- deu
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- ita
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- rus
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- jpn
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- kor
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- vie
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- tha
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- ara
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pipeline_tag: text-generation
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base_model: Qwen/Qwen2.5-32B-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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# huihui-ai/Qwen2.5-32B-Instruct-abliterated
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This is an uncensored version of [Qwen2.5-32B-Instruct](https://huggingface.co/Qwen/Qwen2.5-32B-Instruct) created with abliteration (see [this article](https://huggingface.co/blog/mlabonne/abliteration) to know more about it).
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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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## ollama
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You can use [huihui_ai/qwen2.5-abliterate:32b](https://ollama.com/huihui_ai/qwen2.5-abliterate:32b) directly,
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```
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ollama run huihui_ai/qwen2.5-abliterate:32b
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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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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# Load the model and tokenizer
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model_name = "huihui-ai/Qwen2.5-32B-Instruct-abliterated"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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# Initialize conversation context
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initial_messages = [
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{"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}
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]
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messages = initial_messages.copy() # Copy the initial conversation context
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# Enter conversation loop
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while True:
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# Get user input
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user_input = input("User: ").strip() # Strip leading and trailing spaces
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# If the user types '/exit', end the conversation
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if user_input.lower() == "/exit":
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print("Exiting chat.")
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break
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# If the user types '/clean', reset the conversation context
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if user_input.lower() == "/clean":
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messages = initial_messages.copy() # Reset conversation context
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print("Chat history cleared. Starting a new conversation.")
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continue
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# If input is empty, prompt the user and continue
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if not user_input:
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print("Input cannot be empty. Please enter something.")
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continue
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# Add user input to the conversation
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messages.append({"role": "user", "content": user_input})
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# Build the chat template
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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# Tokenize input and prepare it for the model
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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# Generate a response from the model
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=8192
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)
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# Extract model output, removing special tokens
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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# Add the model's response to the conversation
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messages.append({"role": "assistant", "content": response})
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# Print the model's response
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print(f"Qwen: {response}")
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```
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