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--- |
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license: apache-2.0 |
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base_model: RylanSchaeffer/EleutherAI_pythia-70m_tatsu-lab_alpaca_farm_sftseed1 |
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tags: |
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- trl |
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- reward-trainer |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: pythia-70m_tatsu-lab_alpaca_farm_sftsd1_policy_pythia-6.9b_gold_pythia-6.9b_rmsd3 |
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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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[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/rylan/switching-rms-rm/runs/elnujp8x) |
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# pythia-70m_tatsu-lab_alpaca_farm_sftsd1_policy_pythia-6.9b_gold_pythia-6.9b_rmsd3 |
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This model is a fine-tuned version of [RylanSchaeffer/EleutherAI_pythia-70m_tatsu-lab_alpaca_farm_sftseed1](https://huggingface.co/RylanSchaeffer/EleutherAI_pythia-70m_tatsu-lab_alpaca_farm_sftseed1) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.7102 |
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- Accuracy: 0.6002 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 3 |
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- gradient_accumulation_steps: 2 |
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- total_train_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: linear |
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- lr_scheduler_warmup_ratio: 0.025 |
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- num_epochs: 5 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | |
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|:-------------:|:------:|:----:|:---------------:|:--------:| |
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| No log | 0 | 0 | 1.0058 | 0.5213 | |
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| 0.9831 | 0.0648 | 100 | 0.9931 | 0.5198 | |
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| 0.9073 | 0.1295 | 200 | 0.9274 | 0.5283 | |
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| 0.834 | 0.1943 | 300 | 0.8757 | 0.5513 | |
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| 0.8691 | 0.2591 | 400 | 0.8491 | 0.5517 | |
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| 0.8562 | 0.3238 | 500 | 0.8132 | 0.5629 | |
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| 0.8401 | 0.3886 | 600 | 0.8004 | 0.5625 | |
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| 0.7914 | 0.4534 | 700 | 0.7770 | 0.5732 | |
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| 0.8092 | 0.5181 | 800 | 0.7720 | 0.5782 | |
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| 0.7245 | 0.5829 | 900 | 0.7720 | 0.5748 | |
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| 0.7888 | 0.6477 | 1000 | 0.7561 | 0.5917 | |
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| 0.7504 | 0.7124 | 1100 | 0.7483 | 0.5848 | |
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| 0.704 | 0.7772 | 1200 | 0.7477 | 0.5909 | |
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| 0.7506 | 0.8420 | 1300 | 0.7451 | 0.5928 | |
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| 0.7419 | 0.9067 | 1400 | 0.7419 | 0.5963 | |
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| 0.7339 | 0.9715 | 1500 | 0.7406 | 0.5940 | |
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| 0.7136 | 1.0363 | 1600 | 0.7366 | 0.5971 | |
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| 0.7079 | 1.1010 | 1700 | 0.7346 | 0.5967 | |
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| 0.7363 | 1.1658 | 1800 | 0.7325 | 0.5955 | |
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| 0.7446 | 1.2306 | 1900 | 0.7323 | 0.6036 | |
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| 0.7255 | 1.2953 | 2000 | 0.7222 | 0.6059 | |
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| 0.6924 | 1.3601 | 2100 | 0.7272 | 0.5944 | |
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| 0.7418 | 1.4249 | 2200 | 0.7243 | 0.5944 | |
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| 0.7071 | 1.4896 | 2300 | 0.7230 | 0.5948 | |
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| 0.6902 | 1.5544 | 2400 | 0.7167 | 0.6036 | |
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| 0.6993 | 1.6192 | 2500 | 0.7206 | 0.5994 | |
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| 0.7462 | 1.6839 | 2600 | 0.7179 | 0.6025 | |
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| 0.7 | 1.7487 | 2700 | 0.7154 | 0.5971 | |
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| 0.7169 | 1.8135 | 2800 | 0.7141 | 0.6005 | |
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| 0.72 | 1.8782 | 2900 | 0.7178 | 0.6059 | |
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| 0.6965 | 1.9430 | 3000 | 0.7128 | 0.6017 | |
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| 0.6311 | 2.0078 | 3100 | 0.7096 | 0.6071 | |
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| 0.7176 | 2.0725 | 3200 | 0.7143 | 0.6009 | |
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| 0.7022 | 2.1373 | 3300 | 0.7069 | 0.6009 | |
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| 0.7022 | 2.2021 | 3400 | 0.7124 | 0.5975 | |
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| 0.7996 | 2.2668 | 3500 | 0.7203 | 0.6009 | |
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| 0.7277 | 2.3316 | 3600 | 0.7174 | 0.5975 | |
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| 0.7638 | 2.3964 | 3700 | 0.7105 | 0.6017 | |
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| 0.732 | 2.4611 | 3800 | 0.7128 | 0.6059 | |
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| 0.7315 | 2.5259 | 3900 | 0.7116 | 0.5959 | |
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| 0.6882 | 2.5907 | 4000 | 0.7131 | 0.6017 | |
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| 0.7406 | 2.6554 | 4100 | 0.7051 | 0.6036 | |
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| 0.7353 | 2.7202 | 4200 | 0.7105 | 0.6005 | |
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| 0.7109 | 2.7850 | 4300 | 0.7036 | 0.5994 | |
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| 0.713 | 2.8497 | 4400 | 0.7100 | 0.6032 | |
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| 0.7249 | 2.9145 | 4500 | 0.7145 | 0.5990 | |
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| 0.7191 | 2.9793 | 4600 | 0.7103 | 0.6021 | |
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| 0.72 | 3.0440 | 4700 | 0.7112 | 0.6078 | |
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| 0.6937 | 3.1088 | 4800 | 0.7139 | 0.6036 | |
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| 0.6832 | 3.1736 | 4900 | 0.7140 | 0.6044 | |
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| 0.77 | 3.2383 | 5000 | 0.7155 | 0.5990 | |
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| 0.674 | 3.3031 | 5100 | 0.7114 | 0.6086 | |
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| 0.7001 | 3.3679 | 5200 | 0.7099 | 0.6032 | |
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| 0.6587 | 3.4326 | 5300 | 0.7113 | 0.6017 | |
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| 0.7006 | 3.4974 | 5400 | 0.7077 | 0.6048 | |
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| 0.7317 | 3.5622 | 5500 | 0.7086 | 0.6132 | |
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| 0.6984 | 3.6269 | 5600 | 0.7130 | 0.6009 | |
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| 0.6938 | 3.6917 | 5700 | 0.7105 | 0.6040 | |
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| 0.6601 | 3.7565 | 5800 | 0.7131 | 0.6013 | |
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| 0.7239 | 3.8212 | 5900 | 0.7116 | 0.5978 | |
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| 0.7199 | 3.8860 | 6000 | 0.7091 | 0.6036 | |
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| 0.7523 | 3.9508 | 6100 | 0.7097 | 0.6009 | |
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| 0.7043 | 4.0155 | 6200 | 0.7110 | 0.6071 | |
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| 0.6879 | 4.0803 | 6300 | 0.7074 | 0.6017 | |
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| 0.7138 | 4.1451 | 6400 | 0.7072 | 0.6090 | |
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| 0.6976 | 4.2098 | 6500 | 0.7057 | 0.6067 | |
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| 0.7434 | 4.2746 | 6600 | 0.7069 | 0.6028 | |
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| 0.7492 | 4.3394 | 6700 | 0.7089 | 0.6052 | |
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| 0.6268 | 4.4041 | 6800 | 0.7105 | 0.6002 | |
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| 0.7092 | 4.4689 | 6900 | 0.7080 | 0.6044 | |
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| 0.6915 | 4.5337 | 7000 | 0.7099 | 0.6013 | |
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| 0.652 | 4.5984 | 7100 | 0.7120 | 0.6067 | |
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| 0.7358 | 4.6632 | 7200 | 0.7108 | 0.5994 | |
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| 0.7935 | 4.7280 | 7300 | 0.7082 | 0.6013 | |
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| 0.6902 | 4.7927 | 7400 | 0.7069 | 0.6040 | |
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| 0.7113 | 4.8575 | 7500 | 0.7131 | 0.6009 | |
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| 0.6529 | 4.9223 | 7600 | 0.7098 | 0.6036 | |
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| 0.7117 | 4.9870 | 7700 | 0.7095 | 0.5986 | |
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### Framework versions |
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- Transformers 4.43.2 |
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- Pytorch 2.3.0+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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