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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

jan-hq/stealth-finance-v2-dpo-adapter

stealth-finance-v2-dpo-adapter

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

jan-hq/
stealth-finance-v2-dpo-adapter