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RichardErkhov/
CharlesLi_-_OpenELM-1_1B-DPO-full-max-10-reward-gguf

Name: CharlesLi_-_OpenELM-1_1B-DPO-full-max-10-reward-gguf
Availability: InStock

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Quantization made by Richard Erkhov.

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OpenELM-1_1B-DPO-full-max-10-reward - GGUF

Model creator: https://huggingface.co/CharlesLi/
Original model: https://huggingface.co/CharlesLi/OpenELM-1_1B-DPO-full-max-10-reward/

Name	Quant method	Size
OpenELM-1_1B-DPO-full-max-10-reward.Q2_K.gguf	Q2_K	0.39GB
OpenELM-1_1B-DPO-full-max-10-reward.Q3_K_S.gguf	Q3_K_S	0.46GB
OpenELM-1_1B-DPO-full-max-10-reward.Q3_K.gguf	Q3_K	0.52GB
OpenELM-1_1B-DPO-full-max-10-reward.Q3_K_M.gguf	Q3_K_M	0.52GB
OpenELM-1_1B-DPO-full-max-10-reward.Q3_K_L.gguf	Q3_K_L	0.56GB
OpenELM-1_1B-DPO-full-max-10-reward.IQ4_XS.gguf	IQ4_XS	0.55GB
OpenELM-1_1B-DPO-full-max-10-reward.Q4_0.gguf	Q4_0	0.58GB
OpenELM-1_1B-DPO-full-max-10-reward.IQ4_NL.gguf	IQ4_NL	0.58GB
OpenELM-1_1B-DPO-full-max-10-reward.Q4_K_S.gguf	Q4_K_S	0.58GB
OpenELM-1_1B-DPO-full-max-10-reward.Q4_K.gguf	Q4_K	0.63GB
OpenELM-1_1B-DPO-full-max-10-reward.Q4_K_M.gguf	Q4_K_M	0.63GB
OpenELM-1_1B-DPO-full-max-10-reward.Q4_1.gguf	Q4_1	0.64GB
OpenELM-1_1B-DPO-full-max-10-reward.Q5_0.gguf	Q5_0	0.7GB
OpenELM-1_1B-DPO-full-max-10-reward.Q5_K_S.gguf	Q5_K_S	0.7GB
OpenELM-1_1B-DPO-full-max-10-reward.Q5_K.gguf	Q5_K	0.73GB
OpenELM-1_1B-DPO-full-max-10-reward.Q5_K_M.gguf	Q5_K_M	0.73GB
OpenELM-1_1B-DPO-full-max-10-reward.Q5_1.gguf	Q5_1	0.76GB
OpenELM-1_1B-DPO-full-max-10-reward.Q6_K.gguf	Q6_K	0.83GB
OpenELM-1_1B-DPO-full-max-10-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-10-reward results: []

OpenELM-1_1B-DPO-full-max-10-reward

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

Loss: 1.5022
Rewards/chosen: -12.0
Rewards/rejected: -14.25
Rewards/accuracies: 0.5996
Rewards/margins: 2.2188
Logps/rejected: -1712.0
Logps/chosen: -1520.0
Logits/rejected: -1.7422
Logits/chosen: -3.6875

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
0.3683	0.1047	100	0.6799	-1.8125	-2.1719	0.6016	0.3555	-506.0	-500.0	-12.125	-12.4375
0.2926	0.2094	200	0.7127	-2.0312	-2.5156	0.6152	0.4863	-540.0	-520.0	-10.0	-10.5625
0.2695	0.3141	300	0.7960	-4.5938	-5.1562	0.5801	0.5781	-804.0	-776.0	-7.2812	-8.1875
0.245	0.4188	400	0.7903	-4.6562	-5.25	0.5801	0.5977	-812.0	-784.0	-8.75	-9.5625
0.2375	0.5236	500	0.9612	-6.75	-7.875	0.6113	1.125	-1080.0	-992.0	-7.4688	-8.6875
0.2534	0.6283	600	0.8573	-5.6562	-6.5	0.6133	0.8438	-940.0	-884.0	-8.75	-9.6875
0.2213	0.7330	700	0.8133	-4.7812	-5.7188	0.6387	0.9336	-860.0	-796.0	-5.75	-7.3125
0.2342	0.8377	800	0.8574	-5.5625	-6.4688	0.6055	0.9336	-936.0	-872.0	-7.2812	-8.5625
0.199	0.9424	900	0.8853	-7.1875	-8.1875	0.6074	0.9570	-1104.0	-1040.0	-4.6562	-6.0938
0.0529	1.0471	1000	1.1147	-8.5	-9.75	0.6055	1.2734	-1264.0	-1168.0	-4.4062	-6.2188
0.058	1.1518	1100	1.0443	-6.25	-7.25	0.5977	1.0	-1012.0	-940.0	-7.9375	-9.1875
0.0436	1.2565	1200	1.1756	-9.5625	-10.875	0.6133	1.3438	-1376.0	-1272.0	-1.3125	-3.0938
0.0353	1.3613	1300	1.2987	-8.75	-10.4375	0.5859	1.6875	-1328.0	-1192.0	-5.2812	-7.0625
0.0576	1.4660	1400	1.0486	-8.0625	-9.5625	0.6172	1.4609	-1240.0	-1128.0	-4.625	-6.4688
0.0444	1.5707	1500	1.1459	-8.875	-10.5	0.6113	1.6484	-1344.0	-1208.0	-1.9141	-3.9219
0.0475	1.6754	1600	1.1818	-8.5625	-10.125	0.5918	1.5547	-1304.0	-1176.0	-2.5938	-4.5625
0.0644	1.7801	1700	1.2222	-9.625	-11.25	0.6055	1.6562	-1416.0	-1280.0	-2.7344	-4.5938
0.0397	1.8848	1800	1.0832	-7.8125	-9.375	0.6172	1.5469	-1224.0	-1096.0	-3.3438	-5.375
0.0254	1.9895	1900	1.1882	-9.8125	-11.4375	0.6191	1.6719	-1432.0	-1296.0	-3.7344	-5.4688
0.0037	2.0942	2000	1.3353	-11.125	-13.125	0.6133	1.9766	-1600.0	-1432.0	-2.5938	-4.5312
0.0048	2.1990	2100	1.5185	-12.1875	-14.375	0.5996	2.2031	-1728.0	-1536.0	-2.7656	-4.7188
0.0045	2.3037	2200	1.5012	-12.4375	-14.625	0.6133	2.1875	-1752.0	-1560.0	-1.75	-3.6406
0.0108	2.4084	2300	1.5281	-12.3125	-14.5625	0.6074	2.2344	-1744.0	-1552.0	-1.8047	-3.75
0.0056	2.5131	2400	1.5154	-12.125	-14.3125	0.6074	2.2188	-1720.0	-1528.0	-1.6797	-3.625
0.0051	2.6178	2500	1.5115	-12.1875	-14.4375	0.6035	2.2188	-1728.0	-1536.0	-1.5234	-3.4531
0.0041	2.7225	2600	1.4846	-11.8125	-14.0625	0.5938	2.2031	-1696.0	-1504.0	-1.8047	-3.75
0.0049	2.8272	2700	1.5020	-12.0	-14.25	0.5977	2.2344	-1712.0	-1520.0	-1.7266	-3.6719
0.0063	2.9319	2800	1.5022	-12.0	-14.25	0.5996	2.2188	-1712.0	-1520.0	-1.7422	-3.6875

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-10-reward-gguf

Original model description:

OpenELM-1_1B-DPO-full-max-10-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-10-reward-gguf