---
language:
- en
pipeline_tag: text-generation
library_name: transformers
tags:
- LLM
- Universal-NER
- NER
inference: false
---

# Quantized version of Universal-NER/UniNER-7B-all

[Universal-NER/UniNER-7B-all](https://huggingface.co/Universal-NER/UniNER-7B-all) quantized to 4bit with GPTQ and stored with 1GB shard size.

## Model Description

The model [Universal-NER/UniNER-7B-all](https://huggingface.co/Universal-NER/UniNER-7B-all) was quantized to 4bit, group_size 128, and ascending_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:

```python
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.

```python
prompt = prompt_template.format_map({"input_text": "Cologne is a great city in Germany - maybe even the greatest ;)", "entity_name": "city"})
```