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README.md

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+---
+datasets:
+- openbmb/UltraFeedback
+language:
+- en
+license: apache-2.0
+pipeline_tag: text-generation
+tags:
+- autoquant
+- UCLA-AGI/Mistral7B-PairRM-SPPO-Iter3
+- gptq
+---
+Self-Play Preference Optimization for Language Model Alignment (https://arxiv.org/abs/2405.00675)
+
+# Mistral7B-PairRM-SPPO-Iter3
+
+This model was developed using [Self-Play Preference Optimization](https://arxiv.org/abs/2405.00675) at iteration 3, based on the [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) architecture as starting point. We utilized the prompt sets from the [openbmb/UltraFeedback](https://huggingface.co/datasets/openbmb/UltraFeedback) dataset, splited to 3 parts for 3 iterations by [snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset](https://huggingface.co/datasets/snorkelai/Snorkel-Mistral-PairRM-DPO-Dataset). All responses used are synthetic.
+
+**This is the model reported in the paper** , with K=5 (generate 5 responses per iteration). We attached the Arena-Hard eval results in this model page.
+
+## Links to Other Models
+- [Mistral7B-PairRM-SPPO-Iter1](https://huggingface.co/UCLA-AGI/Mistral7B-PairRM-SPPO-Iter1)
+- [Mistral7B-PairRM-SPPO-Iter2](https://huggingface.co/UCLA-AGI/Mistral7B-PairRM-SPPO-Iter2)
+- [Mistral7B-PairRM-SPPO-Iter3](https://huggingface.co/UCLA-AGI/Mistral7B-PairRM-SPPO-Iter3)
+- [Mistral7B-PairRM-SPPO](https://huggingface.co/UCLA-AGI/Mistral7B-PairRM-SPPO)
+
+
+
+### Model Description
+
+- Model type: A 7B parameter GPT-like model fine-tuned on synthetic datasets.
+- Language(s) (NLP): Primarily English
+- License: Apache-2.0
+- Finetuned from model: mistralai/Mistral-7B-Instruct-v0.2
+
+
+## [AlpacaEval Leaderboard Evaluation Results](https://tatsu-lab.github.io/alpaca_eval/)
+
+
+|                Model                           | LC. Win Rate | Win Rate | Avg. Length |
+|-------------------------------------------|:------------:|:--------:|:-----------:|
+| Mistral7B-PairRM-SPPO Iter 1              |     24.79    |   23.51  |     1855    |
+| Mistral7B-PairRM-SPPO Iter 2              |     26.89    |   27.62  |     2019    |
+| Mistral7B-PairRM-SPPO Iter 3              |     28.53    |   31.02  |     2163    |
+| Mistral7B-PairRM-SPPO Iter 1 (best-of-16) |     28.71    |   27.77  |     1901    |
+| Mistral7B-PairRM-SPPO Iter 2 (best-of-16) |     31.23    |   32.12  |     2035    |
+| Mistral7B-PairRM-SPPO Iter 3 (best-of-16) |     32.13    |   34.94  |     2174    |
+
+## [Arena-Hard Evaluation Results](https://github.com/lm-sys/arena-hard)
+
+Model | Score | 95% CI | average \# Tokens |
+|----------|-----------|--------------|-----------|
+Mistral7B-PairRM-SPPO-Iter3| 23.3 | (-1.8, 1.8)|578|
+
+## [Open LLM Leaderboard Evaluation Results](https://github.com/EleutherAI/lm-evaluation-harness)
+
+Results are reported by using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) v0.4.1
+
+|        | arc_challenge | truthfulqa_mc2 | winogrande | gsm8k | hellaswag | mmlu  | average |
+|--------|---------------|----------------|------------|-------|-----------|-------|---------|
+| Mistral7B-PairRM-SPPO Iter 1 | 65.02         | 69.4           | 77.82      | 43.82 | 85.11     | 58.84 | 66.67   |
+| Mistral7B-PairRM-SPPO Iter 2 | 65.53         | 69.55          | 77.03      | 44.35 | 85.29     | 58.72 | 66.75   |
+| Mistral7B-PairRM-SPPO Iter 3 | 65.36         | 69.97          | 76.8       | 42.68 | 85.16     | 58.45 | 66.4    |
+## [MT-Bench Evaluation Results](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge)
+
+|        | 1st Turn | 2nd Turn | Average |
+|--------|----------|----------|---------|
+| Mistral7B-PairRM-SPPO Iter 1 | 7.63     | 6.79     | 7.21    |
+| Mistral7B-PairRM-SPPO Iter 2 | 7.90     | 7.08     | 7.49    |
+| Mistral7B-PairRM-SPPO Iter 3 | 7.84     | 7.34     | 7.59    |
+
+### Training hyperparameters
+The following hyperparameters were used during training:
+
+- learning_rate: 5e-07
+- eta: 1000
+- per_device_train_batch_size: 8
+- gradient_accumulation_steps: 1
+- seed: 42
+- distributed_type: deepspeed_zero3
+- num_devices: 8
+- optimizer: RMSProp 
+- lr_scheduler_type: linear 
+- lr_scheduler_warmup_ratio: 0.1
+- num_train_epochs: 18.0 (stop at epoch=1.0)
+
+
+
+  
+## Citation
+```
+@misc{wu2024self,
+      title={Self-Play Preference Optimization for Language Model Alignment}, 
+      author={Wu, Yue and Sun, Zhiqing and Yuan, Huizhuo and Ji, Kaixuan and Yang, Yiming and Gu, Quanquan},
+      year={2024},
+      eprint={2405.00675},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG}
+}
+```

config.json

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+{
+  "_name_or_path": "UCLA-AGI/Mistral7B-PairRM-SPPO-Iter3",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "head_dim": 128,
+  "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,
+  "quantization_config": {
+    "batch_size": 1,
+    "bits": 4,
+    "block_name_to_quantize": null,
+    "cache_block_outputs": true,
+    "damp_percent": 0.1,
+    "dataset": "c4",
+    "desc_act": false,
+    "exllama_config": {
+      "version": 1
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+    "group_size": 64,
+    "max_input_length": null,
+    "model_seqlen": null,
+    "module_name_preceding_first_block": null,
+    "modules_in_block_to_quantize": null,
+    "pad_token_id": null,
+    "quant_method": "gptq",
+    "sym": true,
+    "tokenizer": null,
+    "true_sequential": true,
+    "use_cuda_fp16": false,
+    "use_exllama": true
+  },
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.43.4",
+  "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": "4.43.4"
+}

model.safetensors

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+size 4292200184

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.model

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+size 493443

tokenizer_config.json

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+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "add_prefix_space": null,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [],
+  "bos_token": "<s>",
+  "chat_template": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "legacy": true,
+  "model_max_length": 2048,
+  "pad_token": "</s>",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}