model_hh_usp4_200 – AI Model by guoyu-zhang | AlphaNeural AI

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model_hh_usp4_200

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meta-llama/Llama-2-7b-chat-hf

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model_hh_usp4_200

This model is a fine-tuned version of meta-llama/Llama-2-7b-chat-hf on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 2.2047
Rewards/chosen: -10.4203
Rewards/rejected: -11.3883
Rewards/accuracies: 0.6100
Rewards/margins: 0.9680
Logps/rejected: -126.6996
Logps/chosen: -122.1098
Logits/rejected: -0.0920
Logits/chosen: -0.0765

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: 0.0005
train_batch_size: 4
eval_batch_size: 1
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 100
training_steps: 1000

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
0.0	8.0	100	2.1928	-10.7974	-11.7956	0.6200	0.9982	-127.1522	-122.5288	-0.0977	-0.0806
0.0	16.0	200	2.2073	-10.6145	-11.6045	0.6200	0.9900	-126.9398	-122.3256	-0.0957	-0.0785
0.0	24.0	300	2.1834	-10.5576	-11.5387	0.6100	0.9810	-126.8667	-122.2624	-0.0926	-0.0766
0.0	32.0	400	2.2206	-10.5218	-11.4563	0.6000	0.9345	-126.7752	-122.2226	-0.0924	-0.0768
0.0	40.0	500	2.1989	-10.4576	-11.4408	0.6100	0.9832	-126.7580	-122.1513	-0.0920	-0.0763
0.0	48.0	600	2.1897	-10.4344	-11.3970	0.6000	0.9626	-126.7093	-122.1255	-0.0915	-0.0758
0.0	56.0	700	2.1723	-10.3994	-11.3863	0.6000	0.9869	-126.6974	-122.0866	-0.0916	-0.0760
0.0	64.0	800	2.1910	-10.4312	-11.3832	0.6100	0.9520	-126.6939	-122.1220	-0.0918	-0.0760
0.0	72.0	900	2.1762	-10.4083	-11.3782	0.6100	0.9699	-126.6885	-122.0965	-0.0916	-0.0762
0.0	80.0	1000	2.2047	-10.4203	-11.3883	0.6100	0.9680	-126.6996	-122.1098	-0.0920	-0.0765

Framework versions

PEFT 0.10.0
Transformers 4.39.3
Pytorch 2.2.2+cu121
Datasets 2.18.0
Tokenizers 0.15.2