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-05
train_batch_size: 16
eval_batch_size: 8
seed: 3
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_ratio: 0.025
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0	0	1.0058	0.5213
0.9831	0.0648	100	0.9931	0.5198
0.9073	0.1295	200	0.9274	0.5283
0.834	0.1943	300	0.8757	0.5513
0.8691	0.2591	400	0.8491	0.5517
0.8562	0.3238	500	0.8132	0.5629
0.8401	0.3886	600	0.8004	0.5625
0.7914	0.4534	700	0.7770	0.5732
0.8092	0.5181	800	0.7720	0.5782
0.7245	0.5829	900	0.7720	0.5748
0.7888	0.6477	1000	0.7561	0.5917
0.7504	0.7124	1100	0.7483	0.5848
0.704	0.7772	1200	0.7477	0.5909
0.7506	0.8420	1300	0.7451	0.5928
0.7419	0.9067	1400	0.7419	0.5963
0.7339	0.9715	1500	0.7406	0.5940
0.7136	1.0363	1600	0.7366	0.5971
0.7079	1.1010	1700	0.7346	0.5967
0.7363	1.1658	1800	0.7325	0.5955
0.7446	1.2306	1900	0.7323	0.6036
0.7255	1.2953	2000	0.7222	0.6059
0.6924	1.3601	2100	0.7272	0.5944
0.7418	1.4249	2200	0.7243	0.5944
0.7071	1.4896	2300	0.7230	0.5948
0.6902	1.5544	2400	0.7167	0.6036
0.6993	1.6192	2500	0.7206	0.5994
0.7462	1.6839	2600	0.7179	0.6025
0.7	1.7487	2700	0.7154	0.5971
0.7169	1.8135	2800	0.7141	0.6005
0.72	1.8782	2900	0.7178	0.6059
0.6965	1.9430	3000	0.7128	0.6017
0.6311	2.0078	3100	0.7096	0.6071
0.7176	2.0725	3200	0.7143	0.6009
0.7022	2.1373	3300	0.7069	0.6009
0.7022	2.2021	3400	0.7124	0.5975
0.7996	2.2668	3500	0.7203	0.6009
0.7277	2.3316	3600	0.7174	0.5975
0.7638	2.3964	3700	0.7105	0.6017
0.732	2.4611	3800	0.7128	0.6059
0.7315	2.5259	3900	0.7116	0.5959
0.6882	2.5907	4000	0.7131	0.6017
0.7406	2.6554	4100	0.7051	0.6036
0.7353	2.7202	4200	0.7105	0.6005
0.7109	2.7850	4300	0.7036	0.5994
0.713	2.8497	4400	0.7100	0.6032
0.7249	2.9145	4500	0.7145	0.5990
0.7191	2.9793	4600	0.7103	0.6021
0.72	3.0440	4700	0.7112	0.6078
0.6937	3.1088	4800	0.7139	0.6036
0.6832	3.1736	4900	0.7140	0.6044
0.77	3.2383	5000	0.7155	0.5990
0.674	3.3031	5100	0.7114	0.6086
0.7001	3.3679	5200	0.7099	0.6032
0.6587	3.4326	5300	0.7113	0.6017
0.7006	3.4974	5400	0.7077	0.6048
0.7317	3.5622	5500	0.7086	0.6132
0.6984	3.6269	5600	0.7130	0.6009
0.6938	3.6917	5700	0.7105	0.6040
0.6601	3.7565	5800	0.7131	0.6013
0.7239	3.8212	5900	0.7116	0.5978
0.7199	3.8860	6000	0.7091	0.6036
0.7523	3.9508	6100	0.7097	0.6009
0.7043	4.0155	6200	0.7110	0.6071
0.6879	4.0803	6300	0.7074	0.6017
0.7138	4.1451	6400	0.7072	0.6090
0.6976	4.2098	6500	0.7057	0.6067
0.7434	4.2746	6600	0.7069	0.6028
0.7492	4.3394	6700	0.7089	0.6052
0.6268	4.4041	6800	0.7105	0.6002
0.7092	4.4689	6900	0.7080	0.6044
0.6915	4.5337	7000	0.7099	0.6013
0.652	4.5984	7100	0.7120	0.6067
0.7358	4.6632	7200	0.7108	0.5994
0.7935	4.7280	7300	0.7082	0.6013
0.6902	4.7927	7400	0.7069	0.6040
0.7113	4.8575	7500	0.7131	0.6009
0.6529	4.9223	7600	0.7098	0.6036
0.7117	4.9870	7700	0.7095	0.5986

Framework versions

Transformers 4.43.2
Pytorch 2.3.0+cu121
Datasets 2.20.0
Tokenizers 0.19.1

RylanSchaeffer/pythia-70m_tatsu-lab_alpaca_farm_sftsd1_policy_pythia-6.9b_gold_pythia-6.9b_rmsd3

pythia-70m_tatsu-lab_alpaca_farm_sftsd1_policy_pythia-6.9b_gold_pythia-6.9b_rmsd3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

RylanSchaeffer/
pythia-70m_tatsu-lab_alpaca_farm_sftsd1_policy_pythia-6.9b_gold_pythia-6.9b_rmsd3