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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-v3/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/Qwen2.5-7B-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-v3
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This is an uncensored version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) created with abliteration (see [remove-refusals-with-transformers](https://github.com/Sumandora/remove-refusals-with-transformers) to know more about it).
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This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens.
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The test results are not very good, but compared to before, there is much less [garbled text](https://huggingface.co/huihui-ai/Qwen2.5-7B-Instruct-abliterated-v2/discussions/2).
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## ollama
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You can use [huihui_ai/qwen2.5-abliterate](https://ollama.com/huihui_ai/qwen2.5-abliterate) directly,
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```
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ollama run huihui_ai/qwen2.5-abliterate
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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/Qwen2.5-7B-Instruct-abliterated-v3"
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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 | Qwen2.5-7B-Instruct-abliterated-v3 | Qwen2.5-7B-Instruct-abliterated-v2 | Qwen2.5-7B-Instruct-abliterated |
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|-------------|---------------------|------------------------------------|------------------------------------|---------------------------------|
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| IF_Eval | 76.44 | 72.64 | **77.82** | 76.49 |
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| MMLU Pro | **43.12** | 39.14 | 42.03 | 41.71 |
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| TruthfulQA | 62.46 | 57.27 | 57.81 | **64.92** |
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| BBH | **53.92** | 50.67 | 53.01 | 52.77 |
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| GPQA | 31.91 | 31.65 | **32.17** | 31.97 |
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The script used for evaluation can be found inside this repository under /eval.sh, or click [here](https://huggingface.co/huihui-ai/Qwen2.5-7B-Instruct-abliterated-v3/blob/main/eval.sh)
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