stealth-finance-v2-dpo-adapter

This model is a fine-tuned version of TomGrc/FusionNet_7Bx2_MoE_v0.1 on the jan-hq/distilabel_dpo_pairs_binarized, the argilla/OpenHermes2.5-dpo-binarized-alpha, the jan-hq/capybara_dpo_binarized, the jan-hq/bagel_dpo_binarized, the jan-hq/ultrafeedback_preferences_cleaned_binarized, the jan-hq/openmath_instruct_dpo_binarized, the jan-hq/distil_math_dpo_binarized and the jan-hq/evol_codealpaca_dpo_binarized datasets. It achieves the following results on the evaluation set:

• Loss: 0.1290
• Rewards/chosen: -0.1799
• Rewards/rejected: -6.0696
• Rewards/accuracies: 0.8597
• Rewards/margins: 5.8897
• Logps/rejected: -324.0384
• Logps/chosen: -275.3572
• Logits/rejected: -0.7749
• Logits/chosen: -0.7773

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: 5e-05
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• gradient_accumulation_steps: 32
• total_train_batch_size: 64
• total_eval_batch_size: 2
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.3593	1.0	3280	0.1290	-0.1799	-6.0696	0.8597	5.8897	-324.0384	-275.3572	-0.7749	-0.7773

Framework versions

• PEFT 0.8.2
• Transformers 4.37.2
• Pytorch 2.1.2+cu121
• Datasets 2.14.6
• Tokenizers 0.15.0