UniversalNER 4bit
Collection
4bit versions of UniversalNER models
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4 items
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Updated
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Quantized version of Universal-NER/UniNER-7B-definition
Universal-NER/UniNER-7B-definition quantized to 4bit with GPTQ and stored with 1GB shard size.
Model Description
The model Universal-NER/UniNER-7B-definition was quantized to 4bit, group_size 128, and act-order=True with auto-gptq integration in transformers (https://huggingface.co/blog/gptq-integration).
Evaluation
TODO
Prompt template
Prompt template is the same as for the full precision model:
prompt_template = """A virtual assistant answers questions from a user based on the provided text.
USER: Text: {input_text}
ASSISTANT: I’ve read this text.
USER: What describes {entity_name} in the text?
ASSISTANT:
"""
Usage
It is recommended to format input according to the prompt template mentioned above during inference for best results.
prompt = prompt_template.format_map({"input_text": "Cologne is a great city in Germany - maybe even the greatest ;)", "entity_name": "city"})
The model is small enough to be loaded in free-tier Colab with a T4 GPU: https://gist.github.com/sebastianschramm/b849c06676c6601d9a87270e83f5a157
License
The original full precision model and its associated data are released under the CC BY-NC 4.0 license. Hence, the same license applies for the 4bit version.
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