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Model: hkust-nlp/deita-quality-scorer
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+---
+
+<img src="https://huggingface.co/datasets/hkust-nlp/deita-images/resolve/main/logo-final.png" alt="Deita banner" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
+
+# Model Card for Deita Quality Scorer
+
+[GitHub](https://github.com/hkust-nlp/deita) | [Paper](https://arxiv.org/abs/2312.15685)
+
+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).
+
+
+## Performance
+
+
+
+| Model                                          | Align     | Data Size  | MT-Bench | AlpacaEval(%) | OpenLLM (Avg.) |
+|------------------------------------------------|-----------|------------|----------|---------------|----------------|
+| **Proprietary Models**                         |           |            |          |               |                |
+| GPT-4-Turbo                                    | ?         | --         | 9.32     | 97.70         | --             |
+| GPT-4                                          | SFT + PPO | --         | 8.99     | 95.03         | --             |
+| Claude-2                                       | SFT + PPO | --         | 8.06     | 91.36         | --             |
+| GPT-3.5-turbo                                  | SFT + PPO | --         | 7.94     | 89.37         | --             |
+| **Open-sourced Models based on LLaMA-1-13B**   |           |            |          |               |                |
+| LIMA                                           | SFT       | 1K SFT        | 4.29     | 41.98         | 59.82          |
+| WizardLM-13B                                   | SFT       | 70K SFT       | 6.35     | 75.31         | 58.96          |
+| Vicuna-13B-v1.3                                | SFT       | 125K SFT      | 6.39     | 82.11         | 60.01          |
+| Random                                         | SFT       | 10K SFT       | 6.03     | 71.52         | 60.14          |
+| DEITA-LLaMA1-13B-v1.0-sft                           | SFT       | 10K SFT       | 6.60     | 78.01         | 64.27          |
+| **Open-sourced Models based on LLaMA-2-13B**   |           |            |          |               |                |
+| Tulu-2-13B                                     | SFT       | 326K SFT      | 6.70     | 78.90         | --             |
+| Tulu-2-13B+DPO                                 | SFT + DPO | 326K SFT + 60K DPO | 7.00     | 89.50         | --             |
+| LLaMA2-13B-Chat                                | SFT + PPO | --         | 6.65     | 81.09         | --             |
+| WizardLM-13B-v1.2                              | SFT          | >70K SFT      | 7.09     | 89.17         | --             |
+| Vicuna-13B-v1.5                                | SFT       | 125K SFT      | 6.57     | 78.80         | 61.63          |
+| Random                                         | SFT       | 10K SFT       | 5.78     | 65.19         | 61.32          |
+| DEITA-LLaMA2-13B-v1.0-sft                           | SFT       | 10K SFT       | 6.79     | 81.09         | 62.71          |
+| **Open-sourced Models based on Mistral-7B**    |           |            |          |               |                |
+| Mistral-7B-Instruct-v0.1                       | --        | --         | 6.84     | 69.65         | 60.45          |
+| Zephyr-7B-sft                                  | SFT       | 200K SFT      | 5.32     | 75.12         | 60.93          |
+| $\text{Zephyr-7B-}\beta$                       | SFT + DPO | 200K SFT + 60K DPO | 7.34     | 90.60         | 66.36          |
+| OpenChat-3.5                                   | C-RLFT | >> 70K C-RLFT | 7.81     | 88.51         | --           |
+| Starling-7B                                    | C-RLFT + APA | >>70K C-RLFT + 183K APA | 8.09     | 91.99         | --            |
+| Random                                         | SFT       | 10K SFT       | 5.89     | 56.90         | 61.72          |
+| DEITA-7B-v1.0-sft (6K)                           | SFT       | 6K SFT       | 7.22     | 80.78         | 64.94          |
+| DEITA-7B-v1.0-sft (10K)                  | SFT       | 10K SFT       | 7.32     | 81.67         | 64.00          |
+| DEITA-7B-v1.0             | SFT + DPO | 6K SFT + 10K DPO   | 7.55     | 90.06         | 69.86          |
+
+
+
+
+
+## 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)
+
+
+```
+
+## Citation
+If you find the content of this project helpful, please cite our paper as follows:
+
+```
+@misc{liu2023what,
+      title={What Makes Good Data for Alignment? A Comprehensive Study of Automatic Data Selection in Instruction Tuning}, 
+      author={Wei Liu and Weihao Zeng and Keqing He and Yong Jiang and Junxian He},
+      year={2023},
+      eprint={2312.15685},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+

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+  "vocab_size": 32001
+}

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+    "model.layers.9.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.9.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.9.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
+    "model.layers.9.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.norm.weight": "pytorch_model-00002-of-00002.bin"
+  }
+}

special_tokens_map.json

@@ -0,0 +1,6 @@
+{
+  "bos_token": "<s>",
+  "eos_token": "</s>",
+  "pad_token": "<pad>",
+  "unk_token": "<unk>"
+}

tokenizer_config.json

@@ -0,0 +1,34 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "clean_up_tokenization_spaces": false,
+  "eos_token": {
+    "__type": "AddedToken",
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "legacy": true,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": null,
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}