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ds_chat_sppo_hard_new_iter0_2024-09-18-16.33

This model is a fine-tuned version of deepseek-ai/deepseek-llm-7b-chat on the self-generate/topp09_temp07_ds_chat_original_cn_mining_oj_iter0-binarized and the self-generate/topp09_temp07_ds_chat_original_cn_rl_oj_iter0-binarized datasets. It achieves the following results on the evaluation set:

  • Loss: 0.4085
  • Rewards/chosen: 0.0627
  • Rewards/rejected: -0.1540
  • Rewards/accuracies: 0.8269
  • Rewards/margins: 0.2168
  • Logps/rejected: -172.5034
  • Logps/chosen: -143.7851
  • Logits/rejected: 0.5654
  • Logits/chosen: 0.5790
  • Debug/policy Chosen Logits: 0.5790
  • Debug/policy Rejected Logits: 0.5654
  • Debug/policy Chosen Logps: -143.7851
  • Debug/policy Rejected Logps: -172.5034
  • Debug/reference Chosen Logps: -150.0583
  • Debug/reference Rejected Logps: -157.1004

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 1e-06
  • train_batch_size: 8
  • eval_batch_size: 4
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 8
  • total_train_batch_size: 64
  • total_eval_batch_size: 32
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: constant
  • lr_scheduler_warmup_ratio: 0.1
  • lr_scheduler_warmup_steps: 100
  • num_epochs: 1.0

Training results

Training Loss Epoch Step Validation Loss Rewards/chosen Rewards/rejected Rewards/accuracies Rewards/margins Logps/rejected Logps/chosen Logits/rejected Logits/chosen Debug/policy Chosen Logits Debug/policy Rejected Logits Debug/policy Chosen Logps Debug/policy Rejected Logps Debug/reference Chosen Logps Debug/reference Rejected Logps
0.4215 0.7042 100 0.4085 0.0627 -0.1540 0.8269 0.2168 -172.5034 -143.7851 0.5654 0.5790 0.5790 0.5654 -143.7851 -172.5034 -150.0583 -157.1004

Framework versions

  • Transformers 4.42.0
  • Pytorch 2.3.0+cu121
  • Datasets 2.14.6
  • Tokenizers 0.19.1
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