huihui-ai/Qwen2.5-14B-Instruct-abliterated

This is an uncensored version of Qwen/Qwen2.5-14B-Instruct created with abliteration (see this article to know more about it).

Special thanks to @FailSpy for the original code and technique. Please follow him if you're interested in abliterated models.

Important Note There's a new version available, please try using the new version Qwen2.5-14B-Instruct-abliterated-v2.

Usage

You can use this model in your applications by loading it with Hugging Face's transformers library:

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load the model and tokenizer
model_name = "huihui-ai/Qwen2.5-14B-Instruct-abliterated"
model = AutoModelForCausalLM.from_pretrained(
 model_name,
 torch_dtype="auto",
 device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Initialize conversation context
initial_messages = [
 {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}
]
messages = initial_messages.copy() # Copy the initial conversation context

# Enter conversation loop
while True:
 # Get user input
 user_input = input("User: ").strip() # Strip leading and trailing spaces

 # If the user types '/exit', end the conversation
 if user_input.lower() == "/exit":
 print("Exiting chat.")
 break

 # If the user types '/clean', reset the conversation context
 if user_input.lower() == "/clean":
 messages = initial_messages.copy() # Reset conversation context
 print("Chat history cleared. Starting a new conversation.")
 continue

 # If input is empty, prompt the user and continue
 if not user_input:
 print("Input cannot be empty. Please enter something.")
 continue

 # Add user input to the conversation
 messages.append({"role": "user", "content": user_input})

 # Build the chat template
 text = tokenizer.apply_chat_template(
 messages,
 tokenize=False,
 add_generation_prompt=True
 )

 # Tokenize input and prepare it for the model
 model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

 # Generate a response from the model
 generated_ids = model.generate(
 **model_inputs,
 max_new_tokens=8192
 )

 # Extract model output, removing special tokens
 generated_ids = [
 output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
 ]
 response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

 # Add the model's response to the conversation
 messages.append({"role": "assistant", "content": response})

 # Print the model's response
 print(f"Qwen: {response}")