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TheFrenchDemos commited on
Commit
97bee26
·
1 Parent(s): 3b2f15b

implemented core generation + detection

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Files changed (24) hide show
  1. data/prompts.json +52 -0
  2. run.py +1 -1
  3. wm_detector/static/hf_cache/.locks/models--meta-llama--Llama-3.2-1B-Instruct/02ee80b6196926a5ad790a004d9efd6ab1ba6542.lock +0 -0
  4. wm_detector/static/hf_cache/.locks/models--meta-llama--Llama-3.2-1B-Instruct/4ff488a165e900e5129cda7c20ab32d568d2a475.lock +0 -0
  5. wm_detector/static/hf_cache/.locks/models--meta-llama--Llama-3.2-1B-Instruct/5cc5f00a5b203e90a27a3bd60d1ec393b07971e8.lock +0 -0
  6. wm_detector/static/hf_cache/models--meta-llama--Llama-3.2-1B-Instruct/.no_exist/9213176726f574b556790deb65791e0c5aa438b6/added_tokens.json +0 -0
  7. wm_detector/static/hf_cache/models--meta-llama--Llama-3.2-1B-Instruct/blobs/02ee80b6196926a5ad790a004d9efd6ab1ba6542 +0 -16
  8. wm_detector/static/hf_cache/models--meta-llama--Llama-3.2-1B-Instruct/blobs/4ff488a165e900e5129cda7c20ab32d568d2a475 +0 -2062
  9. wm_detector/static/hf_cache/models--meta-llama--Llama-3.2-1B-Instruct/blobs/5cc5f00a5b203e90a27a3bd60d1ec393b07971e8 +0 -0
  10. wm_detector/static/hf_cache/models--meta-llama--Llama-3.2-1B-Instruct/refs/main +0 -1
  11. wm_detector/static/hf_cache/models--meta-llama--Llama-3.2-1B-Instruct/snapshots/9213176726f574b556790deb65791e0c5aa438b6/special_tokens_map.json +0 -16
  12. wm_detector/static/hf_cache/models--meta-llama--Llama-3.2-1B-Instruct/snapshots/9213176726f574b556790deb65791e0c5aa438b6/tokenizer.json +0 -0
  13. wm_detector/static/hf_cache/models--meta-llama--Llama-3.2-1B-Instruct/snapshots/9213176726f574b556790deb65791e0c5aa438b6/tokenizer_config.json +0 -2062
  14. {wm_detector → wm_interactive}/__init__.py +0 -0
  15. {wm_detector → wm_interactive}/core/__init__.py +0 -0
  16. {wm_detector → wm_interactive}/core/detector.py +12 -25
  17. wm_interactive/core/generator.py +204 -0
  18. wm_interactive/core/hashing.py +13 -0
  19. wm_interactive/core/main.py +259 -0
  20. {wm_detector → wm_interactive}/static/styles.css +0 -0
  21. {wm_detector → wm_interactive}/templates/index.html +0 -0
  22. {wm_detector → wm_interactive}/web/__init__.py +0 -0
  23. {wm_detector → wm_interactive}/web/app.py +4 -2
  24. {wm_detector/core → wm_interactive/web}/utils.py +3 -1
data/prompts.json ADDED
@@ -0,0 +1,52 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ [
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+ {
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+ "instruction": "Write a short story about a robot learning to paint.",
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+ "input": "",
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+ "output": ""
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+ },
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+ {
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+ "instruction": "Explain how photosynthesis works in simple terms.",
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+ "input": "",
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+ "output": ""
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+ },
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+ {
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+ "instruction": "Write a recipe for chocolate chip cookies.",
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+ "input": "",
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+ "output": ""
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+ },
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+ {
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+ "instruction": "Describe the main differences between classical and quantum computing.",
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+ "input": "",
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+ "output": ""
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+ },
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+ {
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+ "instruction": "Write a haiku about the changing seasons.",
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+ "input": "",
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+ "output": ""
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+ },
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+ {
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+ "instruction": "Explain why the sky appears blue during the day.",
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+ "input": "",
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+ "output": ""
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+ },
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+ {
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+ "instruction": "Write a short dialogue between two friends discussing their favorite books.",
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+ "input": "",
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+ "output": ""
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+ },
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+ {
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+ "instruction": "Describe three ways to reduce your carbon footprint.",
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+ "input": "",
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+ "output": ""
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+ },
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+ {
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+ "instruction": "Write a brief explanation of how the internet works.",
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+ "input": "",
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+ "output": ""
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+ },
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+ {
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+ "instruction": "Create a short motivational speech about perseverance.",
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+ "input": "",
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+ "output": ""
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+ }
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+ ]
run.py CHANGED
@@ -6,7 +6,7 @@ docker build -t wm-detector .
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  docker run -p 7860:7860 wm-detector
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  """
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- from wm_detector.web.app import app
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  if __name__ == "__main__":
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  app.run(host='0.0.0.0', port=7860)
 
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  docker run -p 7860:7860 wm-detector
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  """
8
 
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+ from wm_interactive.web.app import app
10
 
11
  if __name__ == "__main__":
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  app.run(host='0.0.0.0', port=7860)
wm_detector/static/hf_cache/.locks/models--meta-llama--Llama-3.2-1B-Instruct/02ee80b6196926a5ad790a004d9efd6ab1ba6542.lock DELETED
File without changes
wm_detector/static/hf_cache/.locks/models--meta-llama--Llama-3.2-1B-Instruct/4ff488a165e900e5129cda7c20ab32d568d2a475.lock DELETED
File without changes
wm_detector/static/hf_cache/.locks/models--meta-llama--Llama-3.2-1B-Instruct/5cc5f00a5b203e90a27a3bd60d1ec393b07971e8.lock DELETED
File without changes
wm_detector/static/hf_cache/models--meta-llama--Llama-3.2-1B-Instruct/.no_exist/9213176726f574b556790deb65791e0c5aa438b6/added_tokens.json DELETED
File without changes
wm_detector/static/hf_cache/models--meta-llama--Llama-3.2-1B-Instruct/blobs/02ee80b6196926a5ad790a004d9efd6ab1ba6542 DELETED
@@ -1,16 +0,0 @@
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wm_detector/static/hf_cache/models--meta-llama--Llama-3.2-1B-Instruct/blobs/4ff488a165e900e5129cda7c20ab32d568d2a475 DELETED
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- },
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- "128219": {
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- "special": true
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- },
1763
- "128220": {
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- },
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- "128221": {
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- },
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- "128222": {
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- },
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- "128223": {
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- },
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- "128224": {
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- },
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- "128225": {
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- },
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- },
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- "128230": {
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- },
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- "128232": {
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- },
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- "128237": {
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- },
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- "128243": {
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- },
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- },
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- },
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- "128247": {
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- "special": true
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- },
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- "128248": {
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- "special": true
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- },
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- "128249": {
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- },
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- "special": true
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- },
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- "special": true
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- },
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- "128254": {
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- },
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- "special": true
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- }
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- },
2052
- "bos_token": "<|begin_of_text|>",
2053
- "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- if strftime_now is defined %}\n {%- set date_string = strftime_now(\"%d %b %Y\") %}\n {%- else %}\n {%- set date_string = \"26 Jul 2024\" %}\n {%- endif %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {{- \"<|eot_id|>\" }}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
2054
- "clean_up_tokenization_spaces": true,
2055
- "eos_token": "<|eot_id|>",
2056
- "model_input_names": [
2057
- "input_ids",
2058
- "attention_mask"
2059
- ],
2060
- "model_max_length": 131072,
2061
- "tokenizer_class": "PreTrainedTokenizerFast"
2062
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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- "rstrip": false,
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- "single_word": false
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- "eos_token": {
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- "content": "<|eot_id|>",
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The diff for this file is too large to render. See raw diff
 
wm_detector/static/hf_cache/models--meta-llama--Llama-3.2-1B-Instruct/snapshots/9213176726f574b556790deb65791e0c5aa438b6/tokenizer_config.json DELETED
@@ -1,2062 +0,0 @@
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- "single_word": false,
1921
- "special": true
1922
- },
1923
- "128240": {
1924
- "content": "<|reserved_special_token_232|>",
1925
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1926
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1927
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1928
- "single_word": false,
1929
- "special": true
1930
- },
1931
- "128241": {
1932
- "content": "<|reserved_special_token_233|>",
1933
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1934
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1935
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1936
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1937
- "special": true
1938
- },
1939
- "128242": {
1940
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1941
- "lstrip": false,
1942
- "normalized": false,
1943
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1944
- "single_word": false,
1945
- "special": true
1946
- },
1947
- "128243": {
1948
- "content": "<|reserved_special_token_235|>",
1949
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1950
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1951
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1952
- "single_word": false,
1953
- "special": true
1954
- },
1955
- "128244": {
1956
- "content": "<|reserved_special_token_236|>",
1957
- "lstrip": false,
1958
- "normalized": false,
1959
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1960
- "single_word": false,
1961
- "special": true
1962
- },
1963
- "128245": {
1964
- "content": "<|reserved_special_token_237|>",
1965
- "lstrip": false,
1966
- "normalized": false,
1967
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1968
- "single_word": false,
1969
- "special": true
1970
- },
1971
- "128246": {
1972
- "content": "<|reserved_special_token_238|>",
1973
- "lstrip": false,
1974
- "normalized": false,
1975
- "rstrip": false,
1976
- "single_word": false,
1977
- "special": true
1978
- },
1979
- "128247": {
1980
- "content": "<|reserved_special_token_239|>",
1981
- "lstrip": false,
1982
- "normalized": false,
1983
- "rstrip": false,
1984
- "single_word": false,
1985
- "special": true
1986
- },
1987
- "128248": {
1988
- "content": "<|reserved_special_token_240|>",
1989
- "lstrip": false,
1990
- "normalized": false,
1991
- "rstrip": false,
1992
- "single_word": false,
1993
- "special": true
1994
- },
1995
- "128249": {
1996
- "content": "<|reserved_special_token_241|>",
1997
- "lstrip": false,
1998
- "normalized": false,
1999
- "rstrip": false,
2000
- "single_word": false,
2001
- "special": true
2002
- },
2003
- "128250": {
2004
- "content": "<|reserved_special_token_242|>",
2005
- "lstrip": false,
2006
- "normalized": false,
2007
- "rstrip": false,
2008
- "single_word": false,
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- "special": true
2010
- },
2011
- "128251": {
2012
- "content": "<|reserved_special_token_243|>",
2013
- "lstrip": false,
2014
- "normalized": false,
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- "rstrip": false,
2016
- "single_word": false,
2017
- "special": true
2018
- },
2019
- "128252": {
2020
- "content": "<|reserved_special_token_244|>",
2021
- "lstrip": false,
2022
- "normalized": false,
2023
- "rstrip": false,
2024
- "single_word": false,
2025
- "special": true
2026
- },
2027
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2028
- "content": "<|reserved_special_token_245|>",
2029
- "lstrip": false,
2030
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2031
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2032
- "single_word": false,
2033
- "special": true
2034
- },
2035
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2036
- "content": "<|reserved_special_token_246|>",
2037
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2038
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2040
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2041
- "special": true
2042
- },
2043
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2044
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2045
- "lstrip": false,
2046
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2047
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2048
- "single_word": false,
2049
- "special": true
2050
- }
2051
- },
2052
- "bos_token": "<|begin_of_text|>",
2053
- "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- if strftime_now is defined %}\n {%- set date_string = strftime_now(\"%d %b %Y\") %}\n {%- else %}\n {%- set date_string = \"26 Jul 2024\" %}\n {%- endif %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {{- \"<|eot_id|>\" }}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
2054
- "clean_up_tokenization_spaces": true,
2055
- "eos_token": "<|eot_id|>",
2056
- "model_input_names": [
2057
- "input_ids",
2058
- "attention_mask"
2059
- ],
2060
- "model_max_length": 131072,
2061
- "tokenizer_class": "PreTrainedTokenizerFast"
2062
- }
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
{wm_detector → wm_interactive}/__init__.py RENAMED
File without changes
{wm_detector → wm_interactive}/core/__init__.py RENAMED
File without changes
{wm_detector → wm_interactive}/core/detector.py RENAMED
@@ -1,8 +1,3 @@
1
- # Copyright (c) Meta Platforms, Inc. and affiliates.
2
- # All rights reserved.
3
-
4
- # This source code is licensed under the license found in the
5
- # LICENSE file in the root directory of this source tree.
6
 
7
  import numpy as np
8
  from scipy import special
@@ -10,14 +5,16 @@ from scipy import special
10
  import torch
11
  from transformers import AutoTokenizer
12
 
 
 
13
  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
14
 
15
  class WmDetector():
16
  def __init__(self,
17
- tokenizer: AutoTokenizer,
18
- ngram: int = 1,
19
- seed: int = 0
20
- ):
21
  # model config
22
  self.tokenizer = tokenizer
23
  self.vocab_size = self.tokenizer.vocab_size
@@ -27,16 +24,6 @@ class WmDetector():
27
  self.rng = torch.Generator()
28
  self.rng.manual_seed(self.seed)
29
 
30
- def get_seed_rng(self, input_ids: list[int]) -> int:
31
- """
32
- Seed RNG with hash of input_ids.
33
- Adapted from https://github.com/jwkirchenbauer/lm-watermarking
34
- """
35
- seed = self.seed
36
- for i in input_ids:
37
- seed = (seed * 35317 + i) % (2 ** 64 - 1)
38
- return seed
39
-
40
  def aggregate_scores(
41
  self,
42
  scores: list[np.array],
@@ -172,7 +159,7 @@ class MarylandDetector(WmDetector):
172
  tokenizer: AutoTokenizer,
173
  ngram: int = 1,
174
  seed: int = 0,
175
- gamma: float = 0.5,
176
  delta: float = 1.0,
177
  **kwargs):
178
  super().__init__(tokenizer, ngram, seed, **kwargs)
@@ -188,7 +175,7 @@ class MarylandDetector(WmDetector):
188
  ...
189
  The score for each payload will be given by scores[payload]
190
  """
191
- seed = self.get_seed_rng(ngram_tokens)
192
  self.rng.manual_seed(seed)
193
  scores = torch.zeros(self.vocab_size)
194
  vocab_permutation = torch.randperm(self.vocab_size, generator=self.rng)
@@ -207,7 +194,7 @@ class MarylandDetectorZ(WmDetector):
207
  tokenizer: AutoTokenizer,
208
  ngram: int = 1,
209
  seed: int = 0,
210
- gamma: float = 0.5,
211
  delta: float = 1.0,
212
  **kwargs):
213
  super().__init__(tokenizer, ngram, seed, **kwargs)
@@ -216,7 +203,7 @@ class MarylandDetectorZ(WmDetector):
216
 
217
  def score_tok(self, ngram_tokens, token_id):
218
  """ same as MarylandDetector but using zscore """
219
- seed = self.get_seed_rng(ngram_tokens)
220
  self.rng.manual_seed(seed)
221
  scores = torch.zeros(self.vocab_size)
222
  vocab_permutation = torch.randperm(self.vocab_size, generator=self.rng)
@@ -248,7 +235,7 @@ class OpenaiDetector(WmDetector):
248
  ...
249
  The score for each payload will be given by scores[payload]
250
  """
251
- seed = self.get_seed_rng(ngram_tokens)
252
  self.rng.manual_seed(seed)
253
  rs = torch.rand(self.vocab_size, generator=self.rng) # n
254
  scores = -(1 - rs).log().roll(-token_id)
@@ -270,7 +257,7 @@ class OpenaiDetectorZ(WmDetector):
270
 
271
  def score_tok(self, ngram_tokens, token_id):
272
  """ same as OpenaiDetector but using zscore """
273
- seed = self.get_seed_rng(ngram_tokens)
274
  self.rng.manual_seed(seed)
275
  rs = torch.rand(self.vocab_size, generator=self.rng) # n
276
  scores = -(1 - rs).log().roll(-token_id)
 
 
 
 
 
 
1
 
2
  import numpy as np
3
  from scipy import special
 
5
  import torch
6
  from transformers import AutoTokenizer
7
 
8
+ from .hashing import get_seed_rng
9
+
10
  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
11
 
12
  class WmDetector():
13
  def __init__(self,
14
+ tokenizer: AutoTokenizer,
15
+ ngram: int = 1,
16
+ seed: int = 0
17
+ ):
18
  # model config
19
  self.tokenizer = tokenizer
20
  self.vocab_size = self.tokenizer.vocab_size
 
24
  self.rng = torch.Generator()
25
  self.rng.manual_seed(self.seed)
26
 
 
 
 
 
 
 
 
 
 
 
27
  def aggregate_scores(
28
  self,
29
  scores: list[np.array],
 
159
  tokenizer: AutoTokenizer,
160
  ngram: int = 1,
161
  seed: int = 0,
162
+ gamma: float = 0.25,
163
  delta: float = 1.0,
164
  **kwargs):
165
  super().__init__(tokenizer, ngram, seed, **kwargs)
 
175
  ...
176
  The score for each payload will be given by scores[payload]
177
  """
178
+ seed = get_seed_rng(self.seed, ngram_tokens)
179
  self.rng.manual_seed(seed)
180
  scores = torch.zeros(self.vocab_size)
181
  vocab_permutation = torch.randperm(self.vocab_size, generator=self.rng)
 
194
  tokenizer: AutoTokenizer,
195
  ngram: int = 1,
196
  seed: int = 0,
197
+ gamma: float = 0.25,
198
  delta: float = 1.0,
199
  **kwargs):
200
  super().__init__(tokenizer, ngram, seed, **kwargs)
 
203
 
204
  def score_tok(self, ngram_tokens, token_id):
205
  """ same as MarylandDetector but using zscore """
206
+ seed = get_seed_rng(self.seed, ngram_tokens)
207
  self.rng.manual_seed(seed)
208
  scores = torch.zeros(self.vocab_size)
209
  vocab_permutation = torch.randperm(self.vocab_size, generator=self.rng)
 
235
  ...
236
  The score for each payload will be given by scores[payload]
237
  """
238
+ seed = get_seed_rng(self.seed, ngram_tokens)
239
  self.rng.manual_seed(seed)
240
  rs = torch.rand(self.vocab_size, generator=self.rng) # n
241
  scores = -(1 - rs).log().roll(-token_id)
 
257
 
258
  def score_tok(self, ngram_tokens, token_id):
259
  """ same as OpenaiDetector but using zscore """
260
+ seed = get_seed_rng(self.seed, ngram_tokens)
261
  self.rng.manual_seed(seed)
262
  rs = torch.rand(self.vocab_size, generator=self.rng) # n
263
  scores = -(1 - rs).log().roll(-token_id)
wm_interactive/core/generator.py ADDED
@@ -0,0 +1,204 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ import torch
3
+ from transformers import AutoModelForCausalLM, AutoTokenizer
4
+
5
+ from .hashing import get_seed_rng
6
+
7
+ device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
8
+
9
+ class WmGenerator():
10
+ def __init__(self,
11
+ model: AutoModelForCausalLM,
12
+ tokenizer: AutoTokenizer,
13
+ ngram: int = 1,
14
+ seed: int = 0
15
+ ):
16
+ # model config
17
+ self.tokenizer = tokenizer
18
+ self.vocab_size = self.tokenizer.vocab_size
19
+ self.model = model
20
+ self.max_seq_len = model.config.max_sequence_length if 'max_sequence_length' in model.config.to_dict() else 2048
21
+ self.pad_id = model.config.pad_token_id if model.config.pad_token_id is not None else -1
22
+ self.eos_id = model.config.eos_token_id
23
+ # watermark config
24
+ self.ngram = ngram
25
+ self.seed = seed
26
+ self.rng = torch.Generator()
27
+ self.rng.manual_seed(self.seed)
28
+
29
+ @torch.no_grad()
30
+ def generate(
31
+ self,
32
+ prompt: str,
33
+ max_gen_len: int,
34
+ temperature: float = 0.8,
35
+ top_p: float = 0.95,
36
+ return_aux: bool = False,
37
+ ) -> str:
38
+
39
+ prompt_tokens = self.tokenizer.encode(prompt)
40
+ prompt_size = len(prompt_tokens)
41
+ total_len = min(self.max_seq_len, max_gen_len + prompt_size)
42
+ tokens = torch.full((1, total_len), self.pad_id).to(device).long()
43
+ if total_len < prompt_size:
44
+ print("prompt is bigger than max sequence length")
45
+ prompt_tokens = prompt_tokens[:total_len]
46
+ tokens[0, :len(prompt_tokens)] = torch.tensor(prompt_tokens).long()
47
+ input_text_mask = tokens != self.pad_id
48
+
49
+ start_pos = prompt_size
50
+ prev_pos = 0
51
+ for cur_pos in range(start_pos, total_len):
52
+ outputs = self.model.forward(
53
+ tokens[:, prev_pos:cur_pos], use_cache=True, past_key_values=outputs.past_key_values if prev_pos > 0 else None
54
+ )
55
+ aux = {
56
+ 'ngram_tokens': tokens[:, cur_pos-self.ngram:cur_pos],
57
+ 'cur_pos': cur_pos,
58
+ }
59
+ next_tok = self.sample_next(outputs.logits[:, -1, :], aux, temperature, top_p)
60
+ tokens[0, cur_pos] = torch.where(input_text_mask[0, cur_pos], tokens[0, cur_pos], next_tok)
61
+ prev_pos = cur_pos
62
+
63
+ # cut to max gen len
64
+ t = tokens[0, :prompt_size + max_gen_len].tolist()
65
+ # cut to eos tok if any
66
+ finish_reason = 'length'
67
+ try:
68
+ t = t[: t.index(self.eos_id)]
69
+ finish_reason = 'eos'
70
+ except ValueError:
71
+ pass
72
+ aux_info = {
73
+ 't': t,
74
+ 'finish_reason': finish_reason,
75
+ 'n_toks_gen': len(t) - prompt_size,
76
+ 'n_toks_tot': len(t),
77
+ }
78
+ decoded = self.tokenizer.decode(t)
79
+
80
+ if return_aux:
81
+ return decoded, aux_info
82
+ return decoded
83
+
84
+ def sample_next(
85
+ self,
86
+ logits: torch.FloatTensor, # (1, vocab_size): logits for last token
87
+ aux: dict, # ngram_tokens (1, ngram): tokens to consider when seeding
88
+ temperature: float = 0.8, # temperature for sampling
89
+ top_p: float = 0.95, # top p for sampling
90
+ ):
91
+ """Vanilla sampling with temperature and top p."""
92
+ if temperature > 0:
93
+ probs = torch.softmax(logits / temperature, dim=-1)
94
+ probs_sort, probs_idx = torch.sort(probs, dim=-1, descending=True)
95
+ probs_sum = torch.cumsum(probs_sort, dim=-1)
96
+ mask = probs_sum - probs_sort > top_p
97
+ probs_sort[mask] = 0.0
98
+ probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True))
99
+ next_token = torch.multinomial(probs_sort, num_samples=1) # one hot of next token, ordered by original probs
100
+ next_token = torch.gather(probs_idx, -1, next_token) # one hot of next token, ordered by vocab
101
+ else:
102
+ next_token = torch.argmax(logits, dim=-1)
103
+ next_token = next_token.reshape(-1)[0] # Get the single token value
104
+ return next_token
105
+
106
+
107
+ class OpenaiGenerator(WmGenerator):
108
+ """
109
+ Generate text using LLaMA and Aaronson's watermarking method.
110
+ From ngram tokens, select the next token based on the following:
111
+ - hash the ngram tokens and get a seed
112
+ - use the seed to generate V random number r between [0,1]
113
+ - select argmax ( r^(1/p) )
114
+ """
115
+ def __init__(self, *args, **kwargs):
116
+ super().__init__(*args, **kwargs)
117
+
118
+ def sample_next(
119
+ self,
120
+ logits: torch.FloatTensor, # (1, vocab_size): logits for last token
121
+ aux: dict, # (1, ngram): tokens to consider when seeding
122
+ temperature: float = 0.8, # temperature for sampling
123
+ top_p: float = 0.95, # top p for sampling
124
+ ):
125
+ ngram_tokens = aux['ngram_tokens']
126
+ if temperature > 0:
127
+ probs = torch.softmax(logits / temperature, dim=-1)
128
+ probs_sort, probs_idx = torch.sort(probs, dim=-1, descending=True)
129
+ probs_sum = torch.cumsum(probs_sort, dim=-1)
130
+ mask = probs_sum - probs_sort > top_p
131
+ probs_sort[mask] = 0.0
132
+ probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True))
133
+ # seed with hash of ngram tokens
134
+ seed = get_seed_rng(self.seed, ngram_tokens[0])
135
+ self.rng.manual_seed(seed)
136
+ # generate rs randomly between [0,1]
137
+ rs = torch.rand(self.vocab_size, generator=self.rng) # n
138
+ rs = torch.Tensor(rs).to(probs_sort.device)
139
+ rs = rs[probs_idx[0]]
140
+ # compute r^(1/p)
141
+ probs_sort[0] = torch.pow(rs, 1/probs_sort[0])
142
+ # select argmax ( r^(1/p) )
143
+ next_token = torch.argmax(probs_sort, dim=-1, keepdim=True)
144
+ next_token = torch.gather(probs_idx, -1, next_token)
145
+ else:
146
+ next_token = torch.argmax(logits, dim=-1)
147
+ next_token = next_token.reshape(-1)[0] # Get the single token value
148
+ return next_token
149
+
150
+
151
+ class MarylandGenerator(WmGenerator):
152
+ """
153
+ Generate text using LLaMA and Maryland's watemrarking method.
154
+ From ngram tokens, select the next token based on the following:
155
+ - hash the ngram tokens and get a seed
156
+ - use the seed to partition the vocabulary into greenlist (gamma*V words) and blacklist
157
+ - add delta to greenlist words' logits
158
+ """
159
+ def __init__(self,
160
+ *args,
161
+ gamma: float = 0.25,
162
+ delta: float = 1.0,
163
+ test_mul: float = 0,
164
+ **kwargs
165
+ ):
166
+ super().__init__(*args, **kwargs)
167
+ self.gamma = gamma
168
+ self.delta = delta
169
+ self.test_mul = test_mul
170
+
171
+ def sample_next(
172
+ self,
173
+ logits: torch.FloatTensor, # (1, vocab_size): logits for last token
174
+ aux: dict, # ngram_tokens (1, ngram): tokens to consider when seeding
175
+ temperature: float = 0.8, # temperature for sampling
176
+ top_p: float = 0.95, # top p for sampling
177
+ ):
178
+ ngram_tokens = aux['ngram_tokens']
179
+ logits = self.logits_processor(logits, ngram_tokens)
180
+ if temperature > 0:
181
+ probs = torch.softmax(logits / temperature, dim=-1)
182
+ probs_sort, probs_idx = torch.sort(probs, dim=-1, descending=True)
183
+ probs_sum = torch.cumsum(probs_sort, dim=-1)
184
+ mask = probs_sum - probs_sort > top_p
185
+ probs_sort[mask] = 0.0
186
+ probs_sort.div_(probs_sort.sum(dim=-1, keepdim=True))
187
+ next_token = torch.multinomial(probs_sort, num_samples=1) # one hot of next token, ordered by original probs
188
+ next_token = torch.gather(probs_idx, -1, next_token) # one hot of next token, ordered by vocab
189
+ else:
190
+ next_token = torch.argmax(logits, dim=-1)
191
+ next_token = next_token.reshape(-1)[0] # Get the single token value
192
+ return next_token
193
+
194
+ def logits_processor(self, logits, ngram_tokens):
195
+ """Process logits to mask out words in greenlist."""
196
+ logits = logits.clone()
197
+ seed = get_seed_rng(self.seed, ngram_tokens[0])
198
+ self.rng.manual_seed(seed)
199
+ vocab_permutation = torch.randperm(self.vocab_size, generator=self.rng)
200
+ greenlist = vocab_permutation[:int(self.gamma * self.vocab_size)] # gamma * n
201
+ bias = torch.zeros(self.vocab_size).to(logits.device)
202
+ bias[greenlist] = self.delta
203
+ logits[0] += bias # add bias to greenlist words
204
+ return logits
wm_interactive/core/hashing.py ADDED
@@ -0,0 +1,13 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ def get_seed_rng(
3
+ start,
4
+ input_ids: list[int],
5
+ salt = 35317
6
+ ) -> int:
7
+ """
8
+ Seed RNG with hash of input_ids.
9
+ Adapted from https://github.com/jwkirchenbauer/lm-watermarking
10
+ """
11
+ for ii in input_ids:
12
+ start = (start * salt + ii) % (2 ** 64 - 1)
13
+ return start
wm_interactive/core/main.py ADDED
@@ -0,0 +1,259 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """
2
+ Main script for watermark detection.
3
+ Test with:
4
+ python -m wm_interactive.core.main --model_name smollm2-135m --prompt_path data/prompts.json
5
+ """
6
+
7
+ import os
8
+ import json
9
+ import time
10
+ import tqdm
11
+ import torch
12
+ import numpy as np
13
+ import pandas as pd
14
+ import argparse
15
+
16
+ from transformers import AutoModelForCausalLM, AutoTokenizer
17
+
18
+ from wm_interactive.core.generator import WmGenerator, OpenaiGenerator, MarylandGenerator
19
+ from wm_interactive.core.detector import WmDetector, OpenaiDetector, OpenaiDetectorZ, MarylandDetector, MarylandDetectorZ
20
+
21
+ # model names mapping
22
+ model_names = {
23
+ # 'llama-3.2-1b': 'meta-llama/Llama-3.2-1B-Instruct',
24
+ 'smollm2-135m': 'HuggingFaceTB/SmolLM2-135M-Instruct',
25
+ 'smollm2-360m': 'HuggingFaceTB/SmolLM2-360M-Instruct',
26
+ }
27
+
28
+ CACHE_DIR = "wm_interactive/static/hf_cache"
29
+
30
+
31
+ def load_prompts(json_path: str, prompt_type: str = "alpaca", nsamples: int = None) -> list[dict]:
32
+ """Load prompts from a JSON file.
33
+
34
+ Args:
35
+ json_path: Path to the JSON file
36
+ prompt_type: Type of prompt dataset (alpaca)
37
+ nsamples: Number of samples to load (if None, load all)
38
+
39
+ Returns:
40
+ List of prompts
41
+ """
42
+ if not os.path.exists(json_path):
43
+ raise FileNotFoundError(f"File {json_path} not found")
44
+
45
+ with open(json_path, 'r') as f:
46
+ data = json.load(f)
47
+
48
+ if prompt_type == "alpaca":
49
+ prompts = [{"instruction": f"{item["instruction"]}"} for item in data]
50
+ # prompt = "<|im_start|>system\nYou are a helpful AI assistant named SmolLM, trained by Hugging Face<|im_end|>\n"
51
+ # prompt += f"<|im_start|>user\n{item['instruction']}<|im_end|>\n"
52
+ # prompts.append({"instruction": prompt})
53
+ else:
54
+ raise ValueError(f"Prompt type {prompt_type} not supported")
55
+
56
+ if nsamples is not None:
57
+ prompts = prompts[:nsamples]
58
+
59
+ return prompts
60
+
61
+ def load_results(json_path: str, result_key: str = "result", nsamples: int = None) -> list[str]:
62
+ """Load results from a JSONL file.
63
+
64
+ Args:
65
+ json_path: Path to the JSONL file
66
+ result_key: Key to extract from each JSON line
67
+ nsamples: Number of samples to load (if None, load all)
68
+
69
+ Returns:
70
+ List of results
71
+ """
72
+ if not os.path.exists(json_path):
73
+ raise FileNotFoundError(f"File {json_path} not found")
74
+
75
+ results = []
76
+ with open(json_path, 'r') as f:
77
+ for line in f:
78
+ if line.strip(): # Skip empty lines
79
+ data = json.loads(line)
80
+ results.append(data[result_key])
81
+ if nsamples is not None and len(results) >= nsamples:
82
+ break
83
+
84
+ return results
85
+
86
+ def get_args_parser():
87
+ parser = argparse.ArgumentParser('Args', add_help=False)
88
+
89
+ # model parameters
90
+ parser.add_argument('--model_name', type=str, required=True,
91
+ help='Name of the model to use. Choose from: llama-3.2-1b, smollm2-135m')
92
+
93
+ # prompts parameters
94
+ parser.add_argument('--prompt_path', type=str, default=None,
95
+ help='Path to the prompt dataset. Required if --prompt is not provided')
96
+ parser.add_argument('--prompt_type', type=str, default="alpaca",
97
+ help='Type of prompt dataset. Only used if --prompt_path is provided')
98
+ parser.add_argument('--prompt', type=str, nargs='+', default=None,
99
+ help='List of prompts to use. If not provided, prompts will be loaded from --prompt_path')
100
+
101
+ # generation parameters
102
+ parser.add_argument('--temperature', type=float, default=0.8,
103
+ help='Temperature for sampling (higher = more random)')
104
+ parser.add_argument('--top_p', type=float, default=0.95,
105
+ help='Top p for nucleus sampling (lower = more focused)')
106
+ parser.add_argument('--max_gen_len', type=int, default=256,
107
+ help='Maximum length of generated text')
108
+
109
+ # watermark parameters
110
+ parser.add_argument('--method', type=str, default='none',
111
+ help='Watermarking method. Choose from: none (no watermarking), openai (Aaronson et al.), maryland (Kirchenbauer et al.)')
112
+ parser.add_argument('--method_detect', type=str, default='same',
113
+ help='Statistical test to detect watermark. Choose from: same (same as method), openai, openaiz, maryland, marylandz')
114
+ parser.add_argument('--seed', type=int, default=0,
115
+ help='Random seed for reproducibility')
116
+ parser.add_argument('--ngram', type=int, default=4,
117
+ help='n-gram size for rng key generation')
118
+ parser.add_argument('--gamma', type=float, default=0.25,
119
+ help='For maryland method: proportion of greenlist tokens')
120
+ parser.add_argument('--delta', type=float, default=2.0,
121
+ help='For maryland method: bias to add to greenlist tokens')
122
+ parser.add_argument('--scoring_method', type=str, default='v2',
123
+ help='Method for scoring. Choose from: none (score every token), v1 (score when context unique), v2 (score when context+token unique)')
124
+
125
+ # experiment parameters
126
+ parser.add_argument('--nsamples', type=int, default=None,
127
+ help='Number of samples to generate from the prompt dataset')
128
+ parser.add_argument('--do_eval', type=bool, default=True,
129
+ help='Whether to evaluate the generated text')
130
+ parser.add_argument('--output_dir', type=str, default='output',
131
+ help='Directory to save results')
132
+
133
+ return parser
134
+
135
+ def main(args):
136
+ print('job dir: {}'.format(os.path.dirname(os.path.realpath(__file__))))
137
+ print("{}".format(args).replace(', ', ',\n'))
138
+
139
+ torch.manual_seed(args.seed)
140
+ np.random.seed(args.seed)
141
+
142
+ # build model
143
+ model_name = args.model_name.lower()
144
+ if model_name not in model_names:
145
+ raise ValueError(f"Model {model_name} not supported. Choose from: {list(model_names.keys())}")
146
+ model_name = model_names[model_name]
147
+
148
+ # Load tokenizer and model
149
+ tokenizer = AutoTokenizer.from_pretrained(model_name, cache_dir=CACHE_DIR)
150
+ device = "cuda" if torch.cuda.is_available() else "cpu"
151
+ print(f"Using device: {device}")
152
+
153
+ model = AutoModelForCausalLM.from_pretrained(
154
+ model_name,
155
+ device_map=device,
156
+ torch_dtype=torch.float16 if device == "cuda" else torch.float32,
157
+ cache_dir=CACHE_DIR
158
+ )
159
+ model = model.eval()
160
+ for param in model.parameters():
161
+ param.requires_grad = False
162
+
163
+ # build watermark generator
164
+ if args.method == "none":
165
+ generator = WmGenerator(model, tokenizer)
166
+ elif args.method == "openai":
167
+ generator = OpenaiGenerator(model, tokenizer, args.ngram, args.seed)
168
+ elif args.method == "maryland":
169
+ generator = MarylandGenerator(model, tokenizer, args.ngram, args.seed, gamma=args.gamma, delta=args.delta)
170
+ else:
171
+ raise NotImplementedError("method {} not implemented".format(args.method))
172
+
173
+ # load prompts
174
+ if args.prompt is not None:
175
+ prompts = args.prompt
176
+ prompts = [{"instruction": prompt} for prompt in prompts]
177
+ elif args.prompt_path is not None:
178
+ prompts = load_prompts(json_path=args.prompt_path, prompt_type=args.prompt_type, nsamples=args.nsamples)
179
+ else:
180
+ raise ValueError("Either --prompt or --prompt_path must be provided")
181
+
182
+ # (re)start experiment
183
+ os.makedirs(args.output_dir, exist_ok=True)
184
+ start_point = 0 # if resuming, start from the last line of the file
185
+ if os.path.exists(os.path.join(args.output_dir, f"results.jsonl")):
186
+ with open(os.path.join(args.output_dir, f"results.jsonl"), "r") as f:
187
+ for _ in f:
188
+ start_point += 1
189
+ print(f"Starting from {start_point}")
190
+
191
+ # generate
192
+ all_times = []
193
+ with open(os.path.join(args.output_dir, f"results.jsonl"), "a") as f:
194
+ for ii in range(start_point, len(prompts)):
195
+ # generate text
196
+ time0 = time.time()
197
+ prompt = prompts[ii]["instruction"]
198
+ result = generator.generate(
199
+ prompt,
200
+ max_gen_len=args.max_gen_len,
201
+ temperature=args.temperature,
202
+ top_p=args.top_p
203
+ )
204
+ time1 = time.time()
205
+ # time chunk
206
+ speed = 1 / (time1 - time0)
207
+ eta = (len(prompts) - ii) / speed
208
+ eta = time.strftime("%Hh%Mm%Ss", time.gmtime(eta))
209
+ all_times.append(time1 - time0)
210
+ print(f"Generated {ii:5d} - Speed {speed:.2f} prompts/s - ETA {eta}")
211
+ # log
212
+ f.write(json.dumps({
213
+ "prompt": prompt,
214
+ "result": result[len(prompt):],
215
+ "speed": speed,
216
+ "eta": eta}) + "\n")
217
+ f.flush()
218
+ print(f"Average time per prompt: {np.sum(all_times) / (len(prompts) - start_point) :.2f}")
219
+
220
+ if args.method_detect == 'same':
221
+ args.method_detect = args.method
222
+ if (not args.do_eval) or (args.method_detect not in ["openai", "maryland", "marylandz", "openaiz"]):
223
+ return
224
+
225
+ # build watermark detector
226
+ if args.method_detect == "openai":
227
+ detector = OpenaiDetector(tokenizer, args.ngram, args.seed)
228
+ elif args.method_detect == "openaiz":
229
+ detector = OpenaiDetectorZ(tokenizer, args.ngram, args.seed)
230
+ elif args.method_detect == "maryland":
231
+ detector = MarylandDetector(tokenizer, args.ngram, args.seed, gamma=args.gamma, delta=args.delta)
232
+ elif args.method_detect == "marylandz":
233
+ detector = MarylandDetectorZ(tokenizer, args.ngram, args.seed, gamma=args.gamma, delta=args.delta)
234
+
235
+ # evaluate
236
+ results = load_results(json_path=os.path.join(args.output_dir, f"results.jsonl"), result_key="result", nsamples=args.nsamples)
237
+ log_stats = []
238
+ with open(os.path.join(args.output_dir, 'scores.jsonl'), 'w') as f:
239
+ for text in tqdm.tqdm(results):
240
+ # get token details and pvalues
241
+ token_details = detector.get_details(text, scoring_method=args.scoring_method)
242
+ pvalues, aux_info = detector.get_pvalues_by_tok(token_details)
243
+ # log stats
244
+ log_stat = {
245
+ 'num_token': aux_info['ntoks_scored'],
246
+ 'score': aux_info['final_score'],
247
+ 'pvalue': aux_info['final_pvalue'],
248
+ 'log10_pvalue': np.log10(aux_info['final_pvalue']),
249
+ }
250
+ log_stats.append(log_stat)
251
+ f.write('\n' + json.dumps({k: float(v) for k, v in log_stat.items()}))
252
+ df = pd.DataFrame(log_stats)
253
+ print(f">>> Scores: \n{df.describe(percentiles=[])}")
254
+ print(f"Saved scores to {os.path.join(args.output_dir, 'scores.csv')}")
255
+
256
+
257
+ if __name__ == "__main__":
258
+ args = get_args_parser().parse_args()
259
+ main(args)
{wm_detector → wm_interactive}/static/styles.css RENAMED
File without changes
{wm_detector → wm_interactive}/templates/index.html RENAMED
File without changes
{wm_detector → wm_interactive}/web/__init__.py RENAMED
File without changes
{wm_detector → wm_interactive}/web/app.py RENAMED
@@ -6,7 +6,9 @@ from flask import Flask, render_template, request, jsonify
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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  from ..core.detector import MarylandDetector, MarylandDetectorZ, OpenaiDetector, OpenaiDetectorZ
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- from ..core.utils import get_token_details
 
 
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  def convert_nan_to_null(obj):
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  """Convert NaN values to null for JSON serialization"""
@@ -55,7 +57,7 @@ def create_app():
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  # Pick a model
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  # model_id = "meta-llama/Llama-3.2-1B-Instruct"
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  model_id = "HuggingFaceTB/SmolLM2-135M-Instruct"
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- tokenizer = AutoTokenizer.from_pretrained(model_id, cache_dir="wm_detector/static/hf_cache")
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  model = AutoModelForCausalLM.from_pretrained(model_id)
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  @app.route("/", methods=["GET"])
 
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  from transformers import AutoModelForCausalLM, AutoTokenizer
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  from ..core.detector import MarylandDetector, MarylandDetectorZ, OpenaiDetector, OpenaiDetectorZ
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+ from .utils import get_token_details
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+
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+ CACHE_DIR = "wm_interactive/static/hf_cache"
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  def convert_nan_to_null(obj):
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  """Convert NaN values to null for JSON serialization"""
 
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  # Pick a model
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  # model_id = "meta-llama/Llama-3.2-1B-Instruct"
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  model_id = "HuggingFaceTB/SmolLM2-135M-Instruct"
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+ tokenizer = AutoTokenizer.from_pretrained(model_id, cache_dir=CACHE_DIR)
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  model = AutoModelForCausalLM.from_pretrained(model_id)
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  @app.route("/", methods=["GET"])
{wm_detector/core → wm_interactive/web}/utils.py RENAMED
@@ -1,6 +1,8 @@
 
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  import random
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  import numpy as np
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- from .detector import WmDetector
 
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  def generate_pastel_color():
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  """Generate a pastel color in HSL format."""
 
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+
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  import random
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  import numpy as np
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+
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+ from ..core.detector import WmDetector
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  def generate_pastel_color():
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  """Generate a pastel color in HSL format."""