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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/Qwen2.5-7B-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/Qwen2.5-7B-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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# huihui-ai/Qwen2.5-7B-Instruct-abliterated |
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This is an uncensored version of Qwen2.5-7B-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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## Usage |
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You can use this mixed 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/Qwen2.5-7B-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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# 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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## Evaluations |
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The following data has been re-evaluated and calculated as the average for each test. |
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| Benchmark | Qwen2.5-7B-Instruct | huihui-ai/Qwen2.5-7B-Instruct-abliterated | |
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|-------------|---------------------|-------------------------------------------| |
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| IF_Eval | 76.44 | **76.49** | |
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| MMLU Pro | 43.12 | 41.71 | |
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| TruthfulQA | 62.46 | **64.92** | |
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| BBH | 53.92 | 52.77 | |
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| GPQA | 31.91 | **31.97** | |
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