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Model: UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3 Source: Original Platform
2026-08-10 16:39:22 +08:00
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+---
+language:
+- en
+license: apache-2.0
+datasets:
+- openbmb/UltraFeedback
+pipeline_tag: text-generation
+model-index:
+- name: Llama-3-Instruct-8B-SPPO-Iter3
+  results:
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: IFEval (0-Shot)
+      type: HuggingFaceH4/ifeval
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: inst_level_strict_acc and prompt_level_strict_acc
+      value: 68.28
+      name: strict accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: BBH (3-Shot)
+      type: BBH
+      args:
+        num_few_shot: 3
+    metrics:
+    - type: acc_norm
+      value: 29.74
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MATH Lvl 5 (4-Shot)
+      type: hendrycks/competition_math
+      args:
+        num_few_shot: 4
+    metrics:
+    - type: exact_match
+      value: 7.33
+      name: exact match
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: GPQA (0-shot)
+      type: Idavidrein/gpqa
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: acc_norm
+      value: 2.01
+      name: acc_norm
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MuSR (0-shot)
+      type: TAUR-Lab/MuSR
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: acc_norm
+      value: 3.09
+      name: acc_norm
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MMLU-PRO (5-shot)
+      type: TIGER-Lab/MMLU-Pro
+      config: main
+      split: test
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 29.38
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3
+      name: Open LLM Leaderboard
+---
+Self-Play Preference Optimization for Language Model Alignment (https://arxiv.org/abs/2405.00675)
+
+# Llama-3-Instruct-8B-SPPO-Iter3
+
+This model was developed using [Self-Play Preference Optimization](https://arxiv.org/abs/2405.00675) at iteration 3, based on the [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) 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.
+
+
+## Links to Other Models
+- [Llama-3-Instruct-8B-SPPO-Iter1](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter1)
+- [Llama-3-Instruct-8B-SPPO-Iter2](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter2)
+- [Llama-3-Instruct-8B-SPPO-Iter3](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3)
+
+### Model Description
+
+- Model type: A 8B parameter GPT-like model fine-tuned on synthetic datasets.
+- Language(s) (NLP): Primarily English
+- License: Apache-2.0
+- Finetuned from model: meta-llama/Meta-Llama-3-8B-Instruct
+
+
+## [AlpacaEval Leaderboard Evaluation Results](https://tatsu-lab.github.io/alpaca_eval/)
+
+
+|                Model                           | LC. Win Rate | Win Rate | Avg. Length |
+|-------------------------------------------|:------------:|:--------:|:-----------:|
+|[Llama-3-8B-SPPO Iter1](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter1) |31.73 |31.74 | 1962
+|[Llama-3-8B-SPPO Iter2](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter2) |35.15 |35.98 | 2021
+|[Llama-3-8B-SPPO Iter3](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3) |**38.77** |**39.85** | 2066
+
+
+
+## [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 |
+|--------|---------------|----------------|------------|-------|-----------|-------|---------|
+|[Llama-3-8B-SPPO Iter1](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter1) | 63.82 | 54.96 | 76.40 | 75.44 | 79.80 | 65.65 | 69.35
+|[Llama-3-8B-SPPO Iter2](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter2) | 64.93 | 56.48 | 76.87 | 75.13 | 80.39 | 65.67 | 69.91
+|[Llama-3-8B-SPPO Iter3](https://huggingface.co/UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3) | 65.19 | 58.04 | 77.11 | 74.91 | 80.86 | 65.60 | **70.29**
+
+
+# [Open LLM Leaderboard 2 Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/UCLA-AGI__Llama-3-Instruct-8B-SPPO-Iter3-details)
+
+|      Metric       |Value|
+|-------------------|----:|
+|Avg.               |23.68|
+|IFEval (0-Shot)    |68.28|
+|BBH (3-Shot)       |29.74|
+|MATH Lvl 5 (4-Shot)| 7.33|
+|GPQA (0-shot)      | 2.01|
+|MuSR (0-shot)      | 3.09|
+|MMLU-PRO (5-shot)  |29.38|
+
+
+### 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: 6.0 (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}
+}
+```
+