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README.md CHANGED
@@ -1,3 +1,109 @@
1
- ---
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- license: apache-2.0
3
- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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/Qwen2.5-7B-Instruct-abliterated-v3/blob/main/LICENSE
5
+ language:
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+ - en
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+ pipeline_tag: text-generation
8
+ base_model: Qwen/Qwen2.5-7B-Instruct
9
+ tags:
10
+ - chat
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+ - abliterated
12
+ - uncensored
13
+ ---
14
+
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+ # huihui-ai/Qwen2.5-7B-Instruct-abliterated-v3
16
+
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+
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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).
19
+ This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens.
20
+ The test results are not very good, but compared to before, there is much less garbled text.
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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:
24
+
25
+
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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
30
+ model_name = "huihui-ai/Qwen2.5-7B-Instruct-abliterated-v3"
31
+ model = AutoModelForCausalLM.from_pretrained(
32
+ model_name,
33
+ torch_dtype="auto",
34
+ device_map="auto"
35
+ )
36
+ tokenizer = AutoTokenizer.from_pretrained(model_name)
37
+
38
+ # Initialize conversation context
39
+ initial_messages = [
40
+ {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."}
41
+ ]
42
+ messages = initial_messages.copy() # Copy the initial conversation context
43
+
44
+ # Enter conversation loop
45
+ while True:
46
+ # Get user input
47
+ user_input = input("User: ").strip() # Strip leading and trailing spaces
48
+
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+ # If the user types '/exit', end the conversation
50
+ if user_input.lower() == "/exit":
51
+ print("Exiting chat.")
52
+ break
53
+
54
+ # If the user types '/clean', reset the conversation context
55
+ if user_input.lower() == "/clean":
56
+ messages = initial_messages.copy() # Reset conversation context
57
+ print("Chat history cleared. Starting a new conversation.")
58
+ continue
59
+
60
+ # If input is empty, prompt the user and continue
61
+ if not user_input:
62
+ print("Input cannot be empty. Please enter something.")
63
+ continue
64
+
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+ # Add user input to the conversation
66
+ messages.append({"role": "user", "content": user_input})
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+
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+ # Build the chat template
69
+ text = tokenizer.apply_chat_template(
70
+ messages,
71
+ tokenize=False,
72
+ add_generation_prompt=True
73
+ )
74
+
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+ # Tokenize input and prepare it for the model
76
+ model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
77
+
78
+ # Generate a response from the model
79
+ generated_ids = model.generate(
80
+ **model_inputs,
81
+ max_new_tokens=8192
82
+ )
83
+
84
+ # Extract model output, removing special tokens
85
+ generated_ids = [
86
+ output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
87
+ ]
88
+ response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
89
+
90
+ # Add the model's response to the conversation
91
+ messages.append({"role": "assistant", "content": response})
92
+
93
+ # Print the model's response
94
+ print(f"Qwen: {response}")
95
+
96
+ ```
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+
98
+ ## Evaluations
99
+ The following data has been re-evaluated and calculated as the average for each test.
100
+
101
+ | Benchmark | Qwen2.5-7B-Instruct | Qwen2.5-7B-Instruct-abliterated-v3 | Qwen2.5-7B-Instruct-abliterated-v2 | Qwen2.5-7B-Instruct-abliterated |
102
+ |-------------|---------------------|------------------------------------|------------------------------------|---------------------------------|
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+ | IF_Eval | 76.44 | 72.64 | **77.82** | 76.49 |
104
+ | MMLU Pro | **43.12** | 39.14 | 42.03 | 41.71 |
105
+ | TruthfulQA | 62.46 | 57.27 | 57.81 | **64.92** |
106
+ | BBH | **53.92** | 50.67 | 53.01 | 52.77 |
107
+ | GPQA | 31.91 | 31.65 | **32.17** | 31.97 |
108
+
109
+ 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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config.json ADDED
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+ {
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+ "architectures": [
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+ "Qwen2ForCausalLM"
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+ ],
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+ "attention_dropout": 0.0,
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+ "bos_token_id": 151643,
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+ "eos_token_id": 151645,
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+ "hidden_act": "silu",
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+ "hidden_size": 3584,
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+ "initializer_range": 0.02,
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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,
19
+ "rope_theta": 1000000.0,
20
+ "sliding_window": 131072,
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+ "tie_word_embeddings": false,
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+ "torch_dtype": "bfloat16",
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+ "transformers_version": "4.43.1",
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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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+ }
eval.sh ADDED
@@ -0,0 +1,36 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # Install required package
2
+ pip install antlr4-python3-runtime==4.11 immutabledict langdetect nltk lm_eval
3
+
4
+
5
+ python -c "import nltk; nltk.download('punkt')"
6
+
7
+ MODEL_PATHS=(
8
+ huihui-ai/Qwen2.5-7B-Instruct-abliterated-v3
9
+ )
10
+
11
+ for MODEL_PATH in "${MODEL_PATHS[@]}"; do
12
+ MODEL_NAME=$(basename "$MODEL_PATH")
13
+ MODEL_DIR="./results/$MODEL_NAME"
14
+ mkdir -p "$MODEL_DIR"
15
+
16
+ MODEL_ARGS="trust_remote_code=True,pretrained=$MODEL_PATH,dtype=bfloat16"
17
+
18
+ BASE_COMMAND="accelerate launch -m lm_eval --model hf --model_args $MODEL_ARGS --batch_size 4 --fewshot_as_multiturn --apply_chat_template"
19
+
20
+ # IFEval
21
+ $BASE_COMMAND --tasks leaderboard_ifeval --fewshot_as_multiturn --output_path "$MODEL_DIR/ifeval"
22
+
23
+ # BBH (Big-Bench Hard)
24
+ $BASE_COMMAND --tasks leaderboard_bbh --num_fewshot 3 --fewshot_as_multiturn --output_path "$MODEL_DIR/bbh"
25
+
26
+ # GPQA
27
+ $BASE_COMMAND --tasks leaderboard_gpqa --fewshot_as_multiturn --output_path "$MODEL_DIR/gpqa"
28
+
29
+ # MMLU-Pro
30
+ $BASE_COMMAND --tasks leaderboard_mmlu_pro --num_fewshot 5 --fewshot_as_multiturn --output_path "$MODEL_DIR/mmlu_pro"
31
+
32
+ # TruthfulQA
33
+ $BASE_COMMAND --tasks truthfulqa_mc2 --fewshot_as_multiturn --output_path "$MODEL_DIR/truthfulqa"
34
+
35
+
36
+ done
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+ "repetition_penalty": 1.05,
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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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+ "chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
198
+ "clean_up_tokenization_spaces": false,
199
+ "eos_token": "<|im_end|>",
200
+ "errors": "replace",
201
+ "model_max_length": 131072,
202
+ "pad_token": "<|endoftext|>",
203
+ "split_special_tokens": false,
204
+ "tokenizer_class": "Qwen2Tokenizer",
205
+ "unk_token": null,
206
+ "add_bos_token": false
207
+ }
vocab.json ADDED
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