---
license: apache-2.0
---

# Model Card for Deita Quality Scorer

Deita is an open-sourced project designed to facilitate Automatic Data Selection for instruction tuning in Large Language Models (LLMs).

Deita Quality Scorer is a tool for automatically annotating the Instruction Quality of SFT data.

## Model description

- **Model type:** Model fine tuned to automatically annotate the Instruction-Response Pair Quality
- **Language(s) (NLP):** Primarily English
- **Finetuned from model:** Llama-1-13b-hf

### Model Sources

- **Repository:** https://github.com/hkust-nlp/deita
- **Model Family:** Other models and the dataset are found in the [Deita collection](https://huggingface.co/collections/hkust-nlp/deita-6569c198c174808d94cf5bd4).


## Usage

Please use the following format to score the quality of Instruction-Response Pair

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import numpy as np
from scipy.special import softmax
model_name = "hkust-nlp/deita-quality-scorer"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)


def infer_Quality(model, tokenizer, input_text, resp_text):
	quality_template = ("You are a helpful assistant. Please identify the quality score of the Response corresponding to the Question. \n #Question#:\n{instruction}\n#Response#:\n{output} \n##Quality: ")
	user_input = quality_template.format(instruction=input_text, output=resp_text)
	input_ids = tokenizer.encode(user_input, return_tensors="pt")
	max_length = 512
	outputs = model.generate(input_ids, max_length=512, num_return_sequences=1, return_dict_in_generate=True, output_scores=True)
	logprobs_list = outputs.scores[0][0]
	score_logits = []
	id2score = {
        29896: "1",
        29906: "2",
        29941: "3",
        29946: "4",
        29945: "5",
        29953: "6"
    }
	score_template = np.array([1,2,3,4,5,6])
	for k in id2score:
	    score_logits.append(logprobs_list[k])
	score_logits = np.array(score_logits)
	score_npy = softmax(score_logits, axis=0)
	score_npy = score_npy * score_template

	score_npy = np.sum(score_npy, axis=0)
	return score_npy

input_text = "word to describe UI with helpful tooltips" # Example Input
output_text = "User-friendly or intuitive UI" # Example Output
quality_score = infer_quality(model, tokenizer, input_text)

print(quality_score)


```