--- language: - en license: apache-2.0 library_name: transformers tags: - chat - abliterated - uncensored base_model: Qwen/Qwen2.5-14B-Instruct license_link: https://huggingface.co/huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2/blob/main/LICENSE pipeline_tag: text-generation model-index: - name: Qwen2.5-14B-Instruct-abliterated-v2 results: - task: type: text-generation name: Text Generation dataset: name: IFEval (0-Shot) type: HuggingFaceH4/ifeval args: num_few_shot: 0 metrics: - type: inst_level_strict_acc and prompt_level_strict_acc value: 83.28 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: BBH (3-Shot) type: BBH args: num_few_shot: 3 metrics: - type: acc_norm value: 47.41 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MATH Lvl 5 (4-Shot) type: hendrycks/competition_math args: num_few_shot: 4 metrics: - type: exact_match value: 0.0 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GPQA (0-shot) type: Idavidrein/gpqa args: num_few_shot: 0 metrics: - type: acc_norm value: 11.19 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MuSR (0-shot) type: TAUR-Lab/MuSR args: num_few_shot: 0 metrics: - type: acc_norm value: 11.58 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU-PRO (5-shot) type: TIGER-Lab/MMLU-Pro config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 44.02 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2 name: Open LLM Leaderboard --- # huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2 This is an uncensored version of [Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) created with abliteration (see [this article](https://huggingface.co/blog/mlabonne/abliteration) to know more about it). Special thanks to [@FailSpy](https://huggingface.co/failspy) for the original code and technique. Please follow him if you're interested in abliterated models. **Important Note** This version is an improvement over the previous one [Qwen2.5-14B-Instruct-abliterated](https://huggingface.co/huihui-ai/Qwen2.5-14B-Instruct-abliterated). ## ollama You can use [huihui_ai/qwen2.5-abliterate:14b](https://ollama.com/huihui_ai/qwen2.5-abliterate:14b) directly, ``` ollama run huihui_ai/qwen2.5-abliterate:14b ``` ## Usage You can use this model in your applications by loading it with Hugging Face's `transformers` library: ```python from transformers import AutoModelForCausalLM, AutoTokenizer # Load the model and tokenizer model_name = "huihui-ai/Qwen2.5-14B-Instruct-abliterated-v2" 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}") ``` ## Evaluations Evaluation is ongoing, to be continued later. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_huihui-ai__Qwen2.5-14B-Instruct-abliterated-v2) | Metric |Value| |-------------------|----:| |Avg. |32.91| |IFEval (0-Shot) |83.28| |BBH (3-Shot) |47.41| |MATH Lvl 5 (4-Shot)| 0.00| |GPQA (0-shot) |11.19| |MuSR (0-shot) |11.58| |MMLU-PRO (5-shot) |44.02|