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README.md
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---
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license:
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base_model: ZhangShenao/
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tags:
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- alignment-handbook
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- trl
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- dpo
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- generated_from_trainer
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- trl
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- generated_from_trainer
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datasets:
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- original
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model-index:
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- name:
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results: []
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# 0.0001_1SELM_Llama_iter_2
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## Model description
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More information needed
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## Intended uses & limitations
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##
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More information needed
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### Training hyperparameters
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The following hyperparameters were used during training:
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- train_batch_size: 4
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- eval_batch_size: 4
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 8
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 128
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- total_eval_batch_size: 32
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_ratio: 0.1
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- num_epochs: 1
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### Training results
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### Framework versions
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- Transformers 4.40.2
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license: mit
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base_model: ZhangShenao/SELM-Llama-3-8B-Instruct-iter-1
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tags:
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- alignment-handbook
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- dpo
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- trl
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- selm
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datasets:
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- HuggingFaceH4/ultrafeedback_binarized
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model-index:
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- name: SELM-Llama-3-8B-Instruct-iter-2
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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Self-Exploring Language Models: Active Preference Elicitation for Online Alignment.
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# SELM-Llama-3-8B-Instruct-iter-2
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This model is a fine-tuned version of [ZhangShenao/SELM-Llama-3-8B-Instruct-iter-1](https://huggingface.co/ZhangShenao/SELM-Llama-3-8B-Instruct-iter-1) using synthetic data based on on the HuggingFaceH4/ultrafeedback_binarized dataset.
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## Model description
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- Model type: A 8B parameter Llama3-instruct-based Self-Exploring Language Models (SELM).
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- License: MIT
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## Results
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| | AlpacaEval 2.0 (LC WR) | MT-Bench (Average) |
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|----------------------------------------|------------------------|--------------------|
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| [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) |        24.31 |       7.93 |
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| [SELM-Llama-3-8B-Instruct-iter-1](https://huggingface.co/ZhangShenao/SELM-Llama-3-8B-Instruct-iter-1) |        32.02 |       7.92 |
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| [SELM-Llama-3-8B-Instruct-iter-2](https://huggingface.co/ZhangShenao/SELM-Llama-3-8B-Instruct-iter-2) |        35.65 |       8.09 |
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| [SELM-Llama-3-8B-Instruct-iter-3](https://huggingface.co/ZhangShenao/SELM-Llama-3-8B-Instruct-iter-3) |        33.47 |       8.29 |
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### Training hyperparameters
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The following hyperparameters were used during training:
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- alpha: 0.0001
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- beta: 0.01
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- train_batch_size: 4
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 8
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 128
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- num_epochs: 1
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### Framework versions
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- Transformers 4.40.2
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