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README.md

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+---
+license: apache-2.0
+datasets:
+- hkust-nlp/deita-6k-v0
+language:
+- en
+---
+
+<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 7B V1.0
+
+[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 7B V1.0 is a fine-tuned + DPO version of Mistral-7B-v0.1 that was trained on **6K** automatically selected lightweight, high-quality alignment SFT data: [Deita 6K V0](https://huggingface.co/datasets/hkust-nlp/deita-6k-v0) and **10K** randomly sampled alignment preference data from Ultrafeedback.
+
+## Model description
+
+- **Model type:** Model trained on automatically selected lightweight, high-quality alignment SFT data and 10K randomly sampled alignment preference data.
+- **Language(s) (NLP):** Primarily English
+- **Finetuned from model:** Mistral-7B-v0.1
+
+### 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          |
+
+
+
+
+## Input Format
+
+The model is trained using the [vicuna_v1.1 template](https://github.com/lm-sys/FastChat/blob/main/fastchat/conversation.py)
+
+### SFT Format
+
+```
+A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: Hello! ASSISTANT: Hi!</s>USER: How are you? ASSISTANT:
+```
+
+### DPO Format
+
+```
+A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <prompt> ASSISTANT: <answer></s>
+```
+
+where \<answer\> can be a chosen answer or a rejected answer.
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2e-05
+- train_batch_size: 1
+- eval_batch_size: 1
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 4
+- gradient_accumulation_steps: 128
+- total_train_batch_size: 512
+- total_eval_batch_size: 4
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 6.0
+
+### Framework versions
+
+- Transformers 4.34.1
+- Pytorch 2.1.0+cu121
+- Datasets 2.14.6
+- Tokenizers 0.14.1
+
+
+## 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}
+}
+```
+

config.json

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+{
+  "_name_or_path": "outputs/mistral-7b-300k-6k-zero2-10krandom-9dpo-10krandom",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float32",
+  "transformers_version": "4.35.2",
+  "use_cache": false,
+  "vocab_size": 32000
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.35.2"
+}

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