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RichardErkhov/
CharlesLi_-_OpenELM-1_1B-DPO-full-max-6-reward-gguf

Name: CharlesLi_-_OpenELM-1_1B-DPO-full-max-6-reward-gguf
Availability: InStock

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Quantization made by Richard Erkhov.

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OpenELM-1_1B-DPO-full-max-6-reward - GGUF

Model creator: https://huggingface.co/CharlesLi/
Original model: https://huggingface.co/CharlesLi/OpenELM-1_1B-DPO-full-max-6-reward/

Name	Quant method	Size
OpenELM-1_1B-DPO-full-max-6-reward.Q2_K.gguf	Q2_K	0.39GB
OpenELM-1_1B-DPO-full-max-6-reward.IQ3_XS.gguf	IQ3_XS	0.44GB
OpenELM-1_1B-DPO-full-max-6-reward.IQ3_S.gguf	IQ3_S	0.46GB
OpenELM-1_1B-DPO-full-max-6-reward.Q3_K_S.gguf	Q3_K_S	0.46GB
OpenELM-1_1B-DPO-full-max-6-reward.IQ3_M.gguf	IQ3_M	0.49GB
OpenELM-1_1B-DPO-full-max-6-reward.Q3_K.gguf	Q3_K	0.52GB
OpenELM-1_1B-DPO-full-max-6-reward.Q3_K_M.gguf	Q3_K_M	0.52GB
OpenELM-1_1B-DPO-full-max-6-reward.Q3_K_L.gguf	Q3_K_L	0.56GB
OpenELM-1_1B-DPO-full-max-6-reward.IQ4_XS.gguf	IQ4_XS	0.55GB
OpenELM-1_1B-DPO-full-max-6-reward.Q4_0.gguf	Q4_0	0.58GB
OpenELM-1_1B-DPO-full-max-6-reward.IQ4_NL.gguf	IQ4_NL	0.58GB
OpenELM-1_1B-DPO-full-max-6-reward.Q4_K_S.gguf	Q4_K_S	0.58GB
OpenELM-1_1B-DPO-full-max-6-reward.Q4_K.gguf	Q4_K	0.63GB
OpenELM-1_1B-DPO-full-max-6-reward.Q4_K_M.gguf	Q4_K_M	0.63GB
OpenELM-1_1B-DPO-full-max-6-reward.Q4_1.gguf	Q4_1	0.64GB
OpenELM-1_1B-DPO-full-max-6-reward.Q5_0.gguf	Q5_0	0.7GB
OpenELM-1_1B-DPO-full-max-6-reward.Q5_K_S.gguf	Q5_K_S	0.7GB
OpenELM-1_1B-DPO-full-max-6-reward.Q5_K.gguf	Q5_K	0.73GB
OpenELM-1_1B-DPO-full-max-6-reward.Q5_K_M.gguf	Q5_K_M	0.73GB
OpenELM-1_1B-DPO-full-max-6-reward.Q5_1.gguf	Q5_1	0.76GB
OpenELM-1_1B-DPO-full-max-6-reward.Q6_K.gguf	Q6_K	0.83GB
OpenELM-1_1B-DPO-full-max-6-reward.Q8_0.gguf	Q8_0	1.07GB

Original model description:

library_name: transformers tags:

trl
dpo
alignment-handbook
generated_from_trainer model-index:
name: OpenELM-1_1B-DPO-full-max-6-reward results: []

OpenELM-1_1B-DPO-full-max-6-reward

This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.8090
Rewards/chosen: -15.25
Rewards/rejected: -16.875
Rewards/accuracies: 0.5859
Rewards/margins: 1.6719
Logps/rejected: -1984.0
Logps/chosen: -1840.0
Logits/rejected: 0.0815
Logits/chosen: -1.9688

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: 8
eval_batch_size: 16
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 2
total_train_batch_size: 64
total_eval_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
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
0.5933	0.1047	100	0.6757	-1.1562	-1.3125	0.5801	0.1611	-420.0	-434.0	-11.5625	-11.875
0.5649	0.2094	200	0.6957	-2.125	-2.375	0.6074	0.2451	-524.0	-532.0	-10.125	-10.5625
0.5247	0.3141	300	0.7605	-4.4688	-4.9688	0.6094	0.4980	-784.0	-764.0	-7.6875	-8.4375
0.5128	0.4188	400	0.7887	-3.7188	-4.25	0.5977	0.5195	-712.0	-688.0	-13.25	-13.875
0.5048	0.5236	500	0.7560	-4.25	-4.8438	0.6309	0.6055	-772.0	-744.0	-10.6875	-11.8125
0.4935	0.6283	600	0.7500	-4.6562	-5.0938	0.5781	0.4473	-800.0	-784.0	-14.1875	-14.875
0.4879	0.7330	700	0.7732	-5.0938	-5.7812	0.6230	0.6797	-868.0	-828.0	-12.5	-13.8125
0.4911	0.8377	800	0.7706	-5.0	-5.625	0.625	0.6406	-852.0	-816.0	-13.375	-14.25
0.4586	0.9424	900	0.9273	-7.5312	-8.3125	0.6113	0.7773	-1120.0	-1072.0	-9.0	-10.6875
0.1423	1.0471	1000	1.1068	-8.75	-9.6875	0.5879	0.9609	-1256.0	-1192.0	-7.0625	-9.125
0.1457	1.1518	1100	1.1011	-8.125	-9.0625	0.5801	0.9141	-1192.0	-1128.0	-10.75	-12.375
0.1344	1.2565	1200	1.0089	-8.375	-9.375	0.5996	0.9883	-1224.0	-1152.0	-5.3438	-7.4062
0.1369	1.3613	1300	1.0540	-9.4375	-10.625	0.6016	1.1797	-1352.0	-1264.0	-5.5312	-7.5938
0.1225	1.4660	1400	1.1049	-9.5625	-10.625	0.6035	1.0859	-1352.0	-1272.0	-5.5938	-7.375
0.1276	1.5707	1500	1.1785	-11.0	-12.25	0.6074	1.2344	-1512.0	-1416.0	-1.0625	-3.0625
0.1177	1.6754	1600	1.1486	-9.5	-10.75	0.6094	1.25	-1368.0	-1272.0	-3.8594	-5.9062
0.1007	1.7801	1700	1.1275	-9.5	-10.5625	0.5840	1.0625	-1344.0	-1272.0	-7.75	-9.3125
0.1186	1.8848	1800	1.1385	-9.9375	-11.0	0.5703	1.0547	-1392.0	-1312.0	-5.7188	-7.4375
0.1098	1.9895	1900	1.2803	-11.9375	-13.25	0.5879	1.3359	-1616.0	-1512.0	-2.7031	-4.6875
0.0179	2.0942	2000	1.7014	-14.5	-16.0	0.5820	1.5938	-1896.0	-1768.0	-1.5078	-3.6406
0.0165	2.1990	2100	1.7262	-14.4375	-16.125	0.5801	1.6797	-1904.0	-1760.0	-1.9531	-4.0625
0.0158	2.3037	2200	1.7524	-14.25	-15.8125	0.5762	1.5703	-1872.0	-1744.0	-1.2344	-3.3594
0.0199	2.4084	2300	1.7305	-14.4375	-15.9375	0.5840	1.5391	-1888.0	-1760.0	-0.6211	-2.6875
0.0172	2.5131	2400	1.7391	-14.5625	-16.125	0.5820	1.6016	-1904.0	-1776.0	-0.3164	-2.3906
0.0162	2.6178	2500	1.8456	-15.5	-17.25	0.5898	1.7031	-2008.0	-1872.0	0.1270	-1.9219
0.0128	2.7225	2600	1.7974	-15.0625	-16.75	0.5879	1.6797	-1960.0	-1824.0	-0.1289	-2.2031
0.0168	2.8272	2700	1.8012	-15.1875	-16.875	0.5879	1.6719	-1976.0	-1840.0	0.0459	-2.0156
0.0171	2.9319	2800	1.8090	-15.25	-16.875	0.5859	1.6719	-1984.0	-1840.0	0.0815	-1.9688

Framework versions

Transformers 4.45.1
Pytorch 2.3.0
Datasets 3.0.1
Tokenizers 0.20.0

RichardErkhov/CharlesLi_-_OpenELM-1_1B-DPO-full-max-6-reward-gguf

Original model description:

OpenELM-1_1B-DPO-full-max-6-reward

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

RichardErkhov/
CharlesLi_-_OpenELM-1_1B-DPO-full-max-6-reward-gguf