初始化项目，由ModelHub XC社区提供模型

Model: RLHFlow/LLaMA3-SFT
Source: Original Platform

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README.md

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+---
+library_name: transformers
+tags: []
+---
+
+This is the SFT checkpoint used for the project [RLHFlow/Online-RLHF](https://github.com/RLHFlow/Online-RLHF)
+
+* **Paper**: [RLHF Workflow: From Reward Modeling to Online RLHF](https://arxiv.org/pdf/2405.07863) (Published in TMLR, 2024)
+* **Authors**: Hanze Dong*, Wei Xiong*, Bo Pang*, Haoxiang Wang*, Han Zhao, Yingbo Zhou, Nan Jiang, Doyen Sahoo, Caiming Xiong, Tong Zhang
+* **Code**: https://github.com/RLHFlow/Online-RLHF 
+
+The model is trained from [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on a mixture of diverse open-source high-quality data for 1 epoch with detailed parameters in the report. It has not been trained by RLHF and can serve as a good starting point for the RLHF research.
+
+
+## Academic Benchmarks
+
+We use ToRA script to evaluate GSM8K and MATH, Evalplut for HumanEval, and lm-evaluation-harness for other benchmarks. The model is evaluated in zero-shot setting so the results here may be slightly different from that reported in the technical report.
+
+| **Model**                  | **Size** | **Method**      | **LC AlpacaEval** | **MT-Bench** | **GSM-8K** | **MMLU** | **HumanEval** | **TruthfulQA** | **ARC** | **MBPP** |
+|----------------------------|----------|-----------------|------------|------------|------------|----------|---------------|----------------|---------|----------|
+| LLaMA-3-8B-it              | 8B       | RS+DPO+PPO      |22.9|8.16| 79.6       | 66.0     | 61.6          | 43.9           | 59.5    | 61.1     |
+| Ours (SFT baseline)        | 8B       | SFT             |10.2|7.69| 74.2  |   30.0  | 64.6     | 63.4          | 53.5           | 58.6    | 
+| Ours (Iterative RLHF)      | 8B       | Iterative DPO   |37.2|8.46| 80.7       | 65.3     | 64.6          | 60.4           | 64.3    | 60.8     |
+
+
+## Citation
+Please cite our techical report if you find our model is useful for your research or product.
+```
+@misc{dong2024rlhf,
+      title={RLHF Workflow: From Reward Modeling to Online RLHF}, 
+      author={Hanze Dong and Wei Xiong and Bo Pang and Haoxiang Wang and Han Zhao and Yingbo Zhou and Nan Jiang and Doyen Sahoo and Caiming Xiong and Tong Zhang},
+      year={2024},
+      eprint={2405.07863},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG}
+}
+
+```

config.json

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+  "architectures": [
+    "LlamaForCausalLM"
+  ],
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+  "hidden_act": "silu",
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+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 8192,
+  "model_type": "llama",
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+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
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+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.40.0.dev0",
+  "use_cache": false,
+  "vocab_size": 128256
+}

configuration.json

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generation_config.json

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+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": 128001,
+  "transformers_version": "4.40.0.dev0"
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