this is the initial version for myself usage
example usage:
from llm_watermark import assembly_qwen3, huggingface_model
wm_model: huggingface_model = assembly_qwen3("8B")
prompt = "Write a short story about a robot learning to love."
messages = [{"role": "user", "content": prompt}]
wm_response = wm_model.generate(messages, do_watermark=True, max_new_tokens=256)
print("Watermarked Response:", wm_response)
print(wm_model.detect_watermark(wm_response))
print("----------------")
non_wm_response = wm_model.generate(messages, do_watermark=False, max_new_tokens=256)
print("Non-Watermarked Response:", non_wm_response)
print(wm_model.detect_watermark(non_wm_response))
Metadata
Release files for llm-watermark 0.1.18
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| llm_watermark-0.1.18.tar.gz | 14.8 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| llm_watermark-0.1.18-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 36.8 kB
Release files / llm_watermark-0.1.18.tar.gz
| Download URL | llm_watermark-0.1.18.tar.gz |
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| Size | 14.8 kB |
| Tags | Source |
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Release files / llm_watermark-0.1.18-py3-none-any.whl
| Download URL | llm_watermark-0.1.18-py3-none-any.whl |
|---|---|
| Size | 22.0 kB |
| Tags | Python 3 |
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