This model is a fine-tuned version of deepseek-ai/deepseek-coder-1.3b-base on the generator dataset. It achieves the following results on the evaluation set:

Loss: 6.1354
Rewards/chosen: -37.3199
Rewards/rejected: -114.4451
Rewards/accuracies: 0.7599
Rewards/margins: 77.1251
Logps/rejected: -1316.1266
Logps/chosen: -541.0429
Logits/rejected: -13.2895
Logits/chosen: -11.3220

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: 0.001
train_batch_size: 2
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 64
total_train_batch_size: 128
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 200
num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.147	2.5744	100	6.1354	-37.3199	-114.4451	0.7599	77.1251	-1316.1266	-541.0429	-13.2895	-11.3220

Framework versions

Transformers 4.43.0.dev0
Pytorch 2.2.2+cu121
Datasets 2.19.2
Tokenizers 0.19.1

RichardErkhov/stojchet_-_dpo5k2-awq

Original model description:

dpo5k2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

RichardErkhov/
stojchet_-_dpo5k2-awq