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README.md CHANGED
@@ -1,3 +1,97 @@
1
- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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/Qwen2.5-Code-7B-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/Qwen2.5-Code-7B-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/Qwen2.5-Code-1.5B-Instruct-abliterated
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+
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+
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+ This is an uncensored version of [Qwen2.5-Code-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Code-7B-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 AutoModelForCausalLM, AutoTokenizer
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+
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+ # Load the model and tokenizer
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+ model_name = "huihui-ai/Qwen2.5-Code-7B-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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+ ```
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+
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+ {
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+ "intermediate_size": 18944,
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+ "max_position_embeddings": 32768,
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+ "max_window_layers": 28,
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+ "model_type": "qwen2",
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+ "num_attention_heads": 28,
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+ "num_hidden_layers": 28,
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+ "num_key_value_heads": 4,
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+ "rms_norm_eps": 1e-06,
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+ "rope_theta": 1000000.0,
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+ "sliding_window": 131072,
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+ "tie_word_embeddings": false,
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+ "torch_dtype": "bfloat16",
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+ "transformers_version": "4.44.0",
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+ "use_cache": true,
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+ "use_sliding_window": false,
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+ "vocab_size": 152064
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+ }
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+ "temperature": 0.7,
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+ "top_p": 0.8,
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+ "top_k": 20,
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+ "transformers_version": "4.37.0"
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+ }
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+
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