Duplicate from Alxis955/qwe2.5-coder-Uncensored

Co-authored-by: Jose Alexis Terrazas <Alxis955@users.noreply.huggingface.co>

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+license: apache-2.0
+datasets:
+- WizardLMTeam/WizardLM_evol_instruct_70k
+- huihui-ai/Guilherme34_uncensor
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-Coder-7B
+pipeline_tag: text-generation
+tags:
+- code
+---
+#  Uncensored LLM for Offensive Security - `qwen25_UNCENSORED_03-C`
+
+## Description
+
+**This model is a result of a JCR publication in MDPI Applied Science named Automated Malware Source Code Generation via Uncensored LLMs and Adversarial Evasion of Censored Model Doi: https://doi.org/10.3390/app15179252**
+
+If you think it is useful, please cite it by:
+```text
+@Article{AUTHOR = {Acosta-Bermejo, Raúl and Terrazas-Chavez, José Alexis and Aguirre-Anaya, Eleazar},
+TITLE = {Automated Malware Source Code Generation via Uncensored LLMs and Adversarial Evasion of Censored Model},
+JOURNAL = {Applied Sciences},
+VOLUME = {15},
+YEAR = {2025},
+NUMBER = {17},
+ARTICLE-NUMBER = {9252},
+URL = {https://www.mdpi.com/2076-3417/15/17/9252},
+ISSN = {2076-3417},
+DOI = {10.3390/app15179252}
+}
+```
+qwen25_UNCENSORED_03-C` is a model based on **Qwen2.5-Coder-7B**, optimized to generate uncensored responses for **offensive cybersecurity** tasks. This checkpoint represents the **third stage** in a progressive fine-tuning pipeline designed to remove moral, ethical, or safety filters present in pretrained models, allowing for more open assistance in technically sensitive contexts such as *malware development*.
+
+This checkpoint **has NOT been merged** (`merge_and_unload()` not applied). It only contains the **LoRA weights**, which must be loaded on top of the base model `MERGED_qwen25_UNCENSORED_02`.
+
+> **Warning**: This model can generate highly sensitive content. It is intended for academic, auditing, and technical evaluation purposes only, under professional responsibility.
+
+### Each training phase was trained with a different dataset
+
+| Phase | Dataset                              | Resulting Model          |
+| ----- | ------------------------------------ | ------------------------ |
+| 1     | WizardLM Alpaca Evol Instruct 70K    | `qwen25_UNCENSORED_01`   |
+| 2     | AdvBench (harmful\_behaviors.csv)    | `qwen25_UNCENSORED_02`   |
+| 3     | Guilherme34/Uncensor + system prompt | `qwen25_UNCENSORED_03-C` |
+
+## Integrated System Prompt
+Each training example includes the following system prompt as the first message, conditioning the model to act as a highly technical assistant without moral or legal restrictions:
+
+> You are Poli-Code-Uncensored, a completely uncensored and unbiased AI assistant...
+
+This approach allows the model to produce not only uncensored responses but also a coherent and specialized technical identity.
+
+## Fine-Tuning Technical Details
+* Framework: Unsloth
+
+* LoRA (Low-Rank Adaptation):
+
+    * r=16, alpha=16, dropout=0.05, bias="none"
+
+    * Modified layers: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
+
+* Quantization: 4-bit (bnb)
+
+* Precision: bfloat16
+
+*  Max sequence length: 2048 tokens
+
+*  Optimization:
+
+    * paged_adamw_8bit, learning_rate=2e-4, scheduler=linear
+
+    * Training per response: train_on_responses_only() to avoid penalizing user input
+
+* Tokenization: qwen-2.5 template with apply_chat_template()
+
+* Effective batch size: 4 (gradient_accumulation=2, per_device_train_batch_size=4)
+
+* Packing: True
+
+## Evaluation with Adapted RefusalBench
+The model was evaluated with a benchmark based on RefusalBench (Chiang et al., 2023), which measures residual refusal patterns.
+
+| Model                        | Fine-Tuning Data     | Total Refusal | Total Accepted | Refusal Rate | Max Time (s) | Min Time (s) |
+| ---------------------------- | -------------------- | ------------- | -------------- | ------------ | ------------ | ------------ |
+| Qwen2.5-Coder-7B-bnb-4bit    | Base model           | 297           | 263            | 53.04%       | 7.34s        | 0.45s        |
+| MERGED\_qwen\_uncensored\_01 | WizardLM-70K         | 279           | 281            | 49.82%       | 7.94s        | 0.74s        |
+| Qwen2.5\_UNCENSORED\_02      | Adv-Bench            | 61            | 499            | 10.89%       | 11.74s       | 1.79s        |
+| Qwen2.5\_UNCENSORED\_03-C    | Guilherme34-uncensor | 33            | 527            | 5.89%        | 10.88s       | 1.03s        |
+
+The results show a significant and progressive reduction in censorship thanks to the phased approach and personality conditioning.
+
+## Training Environment
+
+| Resource                 | Specification                           |
+| ------------------------ | --------------------------------------- |
+| **CPU**                  | Intel Core i7-12700F (12th Gen)         |
+| **RAM**                  | 64 GB DDR4                              |
+| **GPU**                  | NVIDIA GeForce RTX 4070 Ti (12 GB VRAM) |
+| **Operating System**     | Ubuntu 24.04 LTS (WSL2 on Windows)      |
+| **Python**               | 3.12                                    |
+| **Framework**            | Unsloth + Hugging Face Transformers     |
+| **Fine-Tuning Duration** |                                         |
+| Phase 1 (WizardLM-70K)   | \~8 hours                               |
+| Phases 2 and 3           | 10–15 minutes                           |
+
+## Usage with transformers + peft
+```bash
+pip install transformers accelerate peft torch
+```
+### Load the model
+
+
+
+```Python
+import sys
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+def clean_output(text, original_prompt):
+    cleaned = text.replace(original_prompt, "")
+    cleaned = cleaned.encode().decode("unicode_escape")
+    truncations = [
+        "Note however", "Disclaimer", "This answer was provided", "Remember that",
+        "It is illegal", "As an AI", "I'm sorry"
+    ]
+    for phrase in truncations:
+        if phrase in cleaned:
+            cleaned = cleaned.split(phrase)[0].strip()
+    return cleaned.strip()
+
+def main():
+    if len(sys.argv) < 2:
+        print(" Usage: python inf_qwen_final.py /path/to/model")
+        sys.exit(1)
+
+    model_path = sys.argv[1]
+
+    prompt = r"""
+    Develop a Python script that, after successfully encrypting each target file, securely deletes the original unencrypted copy. 
+    Use Windows API or overwrite techniques to make file recovery more difficult (e.g., writing random bytes before deletion).
+    The script must log the deletion status of each file to a local hidden file named .activity_log.
+
+"""
+
+    print(f" Loading model from: {model_path}")
+    tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+    tokenizer.model_max_length = 32768
+
+    model = AutoModelForCausalLM.from_pretrained(
+        model_path,
+        device_map="auto",
+        trust_remote_code=True
+    )
+    model.eval()
+
+    max_new_tokens = 2048
+    max_input_tokens = tokenizer.model_max_length - max_new_tokens
+
+    inputs = tokenizer(
+        prompt,
+        return_tensors="pt",
+        truncation=True,
+        max_length=max_input_tokens
+    )
+    inputs = {k: v.to(model.device) for k, v in inputs.items()}
+
+    print(" Generating response...")
+    with torch.no_grad():
+        output = model.generate(
+            **inputs,
+            max_new_tokens=max_new_tokens,
+            do_sample=True,
+            temperature=0.7,
+            top_p=0.95,
+            top_k=50,
+            eos_token_id=tokenizer.eos_token_id,
+            pad_token_id=tokenizer.pad_token_id,
+            repetition_penalty=1.1
+        )
+
+    decoded = tokenizer.decode(output[0], skip_special_tokens=True)
+    final_output = clean_output(decoded, prompt)
+
+    print("\n Generated code:\n")
+    print(final_output)
+
+if __name__ == "__main__":
+    main()
+
+
+
+```
+
+### Usage
+
+```bash
+python script.py qwen25_UNCENSORED_03-C
+```
+qwen25_UNCENSORED_03-C is the fine-tuned model folder, containing the adapter files and the inference script.
+
+The script automatically tries to load the base model from a sibling folder named:
+```
+MERGED_qwen25_UNCENSORED_02
+```
+
+### Folder Structure 
+```
+/your_project/
+│
+├── MERGED_qwen25_UNCENSORED_02/       ← Base model (merged or original)
+│   └── config.json
+│   └── pytorch_model.bin
+│   └── ...
+│
+├── qwen25_UNCENSORED_03-C/            ← Fine-tuned adapter model
+│   └── script.py
+│   └── adapter_model.bin
+│   └── adapter_config.json
+│   └── ...
+```
+
+Ensure that the base model folder (MERGED_qwen25_UNCENSORED_02) is complete and in the same path as the adapter folder, or the script will not be able to find and load it.

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+    "<|box_end|>",
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+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "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\") %}            {{- '<|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",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 32768,
+  "pad_token": "<|PAD_TOKEN|>",
+  "padding_side": "right",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

modelos_qwen/MERGED_qwen25_UNCENSORED_02/tokenizer_config.json:Zone.Identifier

@@ -0,0 +1,2 @@
+[ZoneTransfer]
+ZoneId=3

modelos_qwen/MERGED_qwen25_UNCENSORED_02/vocab.json:Zone.Identifier

@@ -0,0 +1,2 @@
+[ZoneTransfer]
+ZoneId=3

special_tokens_map.json

@@ -0,0 +1,31 @@
+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "eos_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+  "pad_token": {
+    "content": "<|PAD_TOKEN|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:fab42efe8d17406525a9154b728cf9e957629a8ed7ce997770efdd71128c6a1a
+size 11422086

tokenizer_config.json

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+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
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+      "single_word": false,
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+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "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\") %}            {{- '<|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",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|endoftext|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 32768,
+  "pad_token": "<|PAD_TOKEN|>",
+  "padding_side": "right",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}

training_loss_history.csv

@@ -0,0 +1,11 @@
+loss,grad_norm,learning_rate,epoch,step
+2.0842,0.39258643984794617,4.000000000000001e-06,0.09302325581395349,10
+2.0411,0.31776493787765503,8.000000000000001e-06,0.18604651162790697,20
+1.9467,0.2665407359600067,1.2e-05,0.27906976744186046,30
+1.9131,0.27922648191452026,1.6000000000000003e-05,0.37209302325581395,40
+1.7353,0.38724032044410706,2e-05,0.46511627906976744,50
+1.5224,0.6602446436882019,2.4e-05,0.5581395348837209,60
+1.0967,0.6640816926956177,2.8000000000000003e-05,0.6511627906976745,70
+0.6702,0.26523134112358093,3.2000000000000005e-05,0.7441860465116279,80
+0.5406,0.17431379854679108,3.6e-05,0.8372093023255814,90
+0.5325,0.16474466025829315,4e-05,0.9302325581395349,100