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RichardErkhov/
nnheui_-_pythia-1.4b-dpo-full-gguf

Name: nnheui_-_pythia-1.4b-dpo-full-gguf
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Quantization made by Richard Erkhov.

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pythia-1.4b-dpo-full - GGUF

Model creator: https://huggingface.co/nnheui/
Original model: https://huggingface.co/nnheui/pythia-1.4b-dpo-full/

Name	Quant method	Size
pythia-1.4b-dpo-full.Q2_K.gguf	Q2_K	0.53GB
pythia-1.4b-dpo-full.Q3_K_S.gguf	Q3_K_S	0.61GB
pythia-1.4b-dpo-full.Q3_K.gguf	Q3_K	0.71GB
pythia-1.4b-dpo-full.Q3_K_M.gguf	Q3_K_M	0.71GB
pythia-1.4b-dpo-full.Q3_K_L.gguf	Q3_K_L	0.77GB
pythia-1.4b-dpo-full.IQ4_XS.gguf	IQ4_XS	0.74GB
pythia-1.4b-dpo-full.Q4_0.gguf	Q4_0	0.77GB
pythia-1.4b-dpo-full.IQ4_NL.gguf	IQ4_NL	0.78GB
pythia-1.4b-dpo-full.Q4_K_S.gguf	Q4_K_S	0.78GB
pythia-1.4b-dpo-full.Q4_K.gguf	Q4_K	0.85GB
pythia-1.4b-dpo-full.Q4_K_M.gguf	Q4_K_M	0.85GB
pythia-1.4b-dpo-full.Q4_1.gguf	Q4_1	0.85GB
pythia-1.4b-dpo-full.Q5_0.gguf	Q5_0	0.92GB
pythia-1.4b-dpo-full.Q5_K_S.gguf	Q5_K_S	0.92GB
pythia-1.4b-dpo-full.Q5_K.gguf	Q5_K	0.98GB
pythia-1.4b-dpo-full.Q5_K_M.gguf	Q5_K_M	0.98GB
pythia-1.4b-dpo-full.Q5_1.gguf	Q5_1	1.0GB
pythia-1.4b-dpo-full.Q6_K.gguf	Q6_K	1.08GB
pythia-1.4b-dpo-full.Q8_0.gguf	Q8_0	1.4GB

Original model description:

license: apache-2.0 base_model: nnheui/pythia-1.4b-sft-full tags:

alignment-handbook
trl
dpo
generated_from_trainer
trl
dpo
alignment-handbook
generated_from_trainer datasets:
HuggingFaceH4/ultrafeedback_binarized model-index:
name: pythia-1.4b-dpo-full results: []

pythia-1.4b-dpo-full

This model is a fine-tuned version of nnheui/pythia-1.4b-sft-full on the HuggingFaceH4/ultrafeedback_binarized dataset. It achieves the following results on the evaluation set:

Loss: 0.5991
Rewards/chosen: -1.875
Rewards/rejected: -2.6406
Rewards/accuracies: 0.7164
Rewards/margins: 0.7734
Logps/rejected: -604.0
Logps/chosen: -580.0
Logits/rejected: -1.4297
Logits/chosen: -1.4062
Logps/chosen Top Tokens: -0.0009
Logps/rejected Top Tokens: -0.0009
Logps/chosen Bottom Tokens: -13.9375
Logps/rejected Bottom Tokens: -13.8125

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-07
train_batch_size: 5
eval_batch_size: 5
seed: 42
distributed_type: multi-GPU
num_devices: 6
gradient_accumulation_steps: 4
total_train_batch_size: 120
total_eval_batch_size: 30
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 10

Training results

Training Loss	Epoch	Step	Logits/chosen	Logits/rejected	Logps/chosen	Logps/chosen Bottom Tokens	Logps/chosen Top Tokens	Logps/rejected	Logps/rejected Bottom Tokens	Logps/rejected Top Tokens	Validation Loss	Rewards/accuracies	Rewards/chosen	Rewards/margins	Rewards/rejected
0.678	0.1963	100	-1.0938	-1.1562	-396.0	-14.0625	-0.0009	-344.0	-14.0	-0.0009	0.6789	0.5881	-0.0275	0.0332	-0.0608
0.645	0.3925	200	-1.1562	-1.2031	-422.0	-14.375	-0.0009	-380.0	-14.3125	-0.0009	0.6489	0.6448	-0.2871	0.1367	-0.4238
0.6396	0.5888	300	-1.1875	-1.2344	-438.0	-14.375	-0.0007	-406.0	-14.3125	-0.0008	0.6304	0.6627	-0.4512	0.2275	-0.6797
0.6102	0.7851	400	-1.1875	-1.2344	-444.0	-14.3125	-0.0007	-414.0	-14.25	-0.0007	0.6268	0.6567	-0.5039	0.2578	-0.7617
0.6084	0.9814	500	-1.1953	-1.2422	-446.0	-14.375	-0.0007	-416.0	-14.3125	-0.0007	0.6259	0.6567	-0.5234	0.2617	-0.7852
0.6115	1.1776	600	0.6121	-0.5547	-0.8789	0.6806	0.3242	-426.0	-450.0	-1.2578	-1.2109	-0.0006	-0.0006	-14.25	-14.125
0.607	1.3739	700	0.6068	-0.6641	-1.0078	0.6985	0.3418	-438.0	-460.0	-1.2812	-1.2344	-0.0006	-0.0006	-14.1875	-14.125
0.5764	1.5702	800	0.5996	-0.75	-1.1406	0.6866	0.3887	-452.0	-468.0	-1.3125	-1.2656	-0.0007	-0.0007	-14.25	-14.125
0.5903	1.7664	900	0.5984	-0.5898	-0.9648	0.7045	0.3770	-434.0	-452.0	-1.3125	-1.2656	-0.0006	-0.0006	-14.25	-14.125
0.5697	1.9627	1000	0.5922	-0.7383	-1.1562	0.6866	0.4160	-454.0	-468.0	-1.3125	-1.2734	-0.0007	-0.0006	-14.0625	-14.0
0.5573	2.1590	1100	0.5854	-0.8203	-1.2812	0.6985	0.4570	-466.0	-476.0	-1.3281	-1.2891	-0.0006	-0.0006	-14.125	-14.0
0.5439	2.3553	1200	0.5845	-1.1016	-1.6172	0.6866	0.5078	-498.0	-504.0	-1.3672	-1.3281	-0.0007	-0.0006	-14.0625	-13.9375
0.5487	2.5515	1300	0.5801	-0.8906	-1.3828	0.6925	0.4980	-476.0	-482.0	-1.3828	-1.3438	-0.0007	-0.0006	-14.0625	-14.0
0.543	2.7478	1400	0.5785	-0.8672	-1.3516	0.7134	0.4863	-474.0	-480.0	-1.375	-1.3359	-0.0007	-0.0006	-14.0625	-13.9375
0.5382	2.9441	1500	0.5711	-1.1172	-1.6641	0.6955	0.5508	-504.0	-506.0	-1.3906	-1.3516	-0.0007	-0.0006	-14.125	-14.0
0.5117	3.1403	1600	0.5712	-1.25	-1.8281	0.7045	0.5742	-520.0	-520.0	-1.3984	-1.3594	-0.0007	-0.0006	-14.125	-14.0
0.4983	3.3366	1700	0.5703	-1.1641	-1.75	0.7015	0.5859	-512.0	-510.0	-1.4062	-1.3672	-0.0007	-0.0007	-14.125	-14.0
0.4976	3.5329	1800	0.5709	-1.2656	-1.8828	0.7254	0.6133	-524.0	-520.0	-1.4141	-1.375	-0.0007	-0.0007	-14.125	-14.0625
0.4956	3.7291	1900	0.5754	-1.2266	-1.8047	0.7164	0.5781	-516.0	-516.0	-1.4062	-1.3672	-0.0008	-0.0008	-14.0625	-13.9375
0.4996	3.9254	2000	0.5722	-1.2578	-1.8516	0.7045	0.6016	-524.0	-520.0	-1.4062	-1.375	-0.0008	-0.0008	-14.0625	-13.9375
0.4588	4.1217	2100	0.5748	-1.4141	-2.0312	0.7343	0.6211	-540.0	-536.0	-1.4062	-1.375	-0.0009	-0.0009	-14.0	-13.875
0.4555	4.3180	2200	0.5743	-1.2969	-1.9141	0.7164	0.6172	-528.0	-524.0	-1.4219	-1.3906	-0.0009	-0.0009	-13.9375	-13.8125
0.4625	4.5142	2300	0.5735	-1.3047	-1.9297	0.7134	0.625	-532.0	-524.0	-1.4141	-1.3828	-0.0008	-0.0008	-14.0	-13.875
0.469	4.7105	2400	0.5743	-1.4766	-2.1406	0.7194	0.6562	-552.0	-540.0	-1.4375	-1.3984	-0.0009	-0.0009	-14.0	-13.875
0.4796	4.9068	2500	0.5750	-1.3281	-1.9766	0.7134	0.6484	-536.0	-528.0	-1.4375	-1.3984	-0.0009	-0.0009	-14.0	-13.875
0.4082	5.1030	2600	0.5818	-1.6016	-2.2656	0.7194	0.6602	-564.0	-552.0	-1.4453	-1.4062	-0.0009	-0.0009	-14.0	-13.875
0.4193	5.2993	2700	0.5803	-1.4922	-2.1406	0.7194	0.6523	-552.0	-544.0	-1.4375	-1.3984	-0.0009	-0.0009	-14.0	-13.8125
0.419	5.4956	2800	0.5795	-1.625	-2.3281	0.7194	0.7031	-572.0	-556.0	-1.4375	-1.3984	-0.0009	-0.0009	-14.0	-13.875
0.4267	5.6919	2900	0.5780	-1.6875	-2.375	0.7134	0.6836	-576.0	-564.0	-1.4375	-1.4062	-0.0009	-0.0008	-13.9375	-13.8125
0.402	5.8881	3000	0.5828	-1.6484	-2.3594	0.7254	0.7109	-572.0	-560.0	-1.4453	-1.4062	-0.0009	-0.0009	-13.9375	-13.8125
0.3656	6.0844	3100	0.5844	-1.6875	-2.4062	0.7015	0.7227	-580.0	-564.0	-1.4375	-1.4062	-0.0009	-0.0009	-14.0	-13.875
0.3971	6.2807	3200	0.5873	-1.6094	-2.3281	0.7075	0.7148	-572.0	-556.0	-1.4453	-1.4141	-0.0009	-0.0009	-14.0	-13.8125
0.3923	6.4769	3300	0.5906	-1.6875	-2.4062	0.7075	0.7188	-580.0	-564.0	-1.4453	-1.4141	-0.0009	-0.0009	-14.0	-13.875
0.4011	6.6732	3400	0.5848	-1.7109	-2.4375	0.7254	0.7344	-584.0	-564.0	-1.4375	-1.4062	-0.0009	-0.0008	-14.0	-13.875
0.3838	6.8695	3500	0.5897	-1.75	-2.4844	0.7164	0.7305	-584.0	-568.0	-1.4297	-1.3984	-0.0009	-0.0008	-13.9375	-13.8125
0.3762	7.0658	3600	0.5910	-1.7812	-2.5312	0.7134	0.7422	-592.0	-572.0	-1.4375	-1.4062	-0.0009	-0.0008	-13.9375	-13.8125
0.3591	7.2620	3700	0.5895	-1.7812	-2.5312	0.7075	0.7578	-592.0	-572.0	-1.4375	-1.4062	-0.0009	-0.0009	-14.0	-13.875
0.3713	7.4583	3800	0.5956	-1.7734	-2.5312	0.7164	0.75	-592.0	-572.0	-1.4297	-1.3984	-0.0009	-0.0009	-13.9375	-13.8125
0.381	7.6546	3900	0.5948	-1.8672	-2.625	0.7164	0.7695	-600.0	-580.0	-1.4375	-1.4062	-0.0009	-0.0008	-13.9375	-13.8125
0.3639	7.8508	4000	0.5950	-1.8672	-2.625	0.7194	0.7578	-600.0	-580.0	-1.4375	-1.4062	-0.0009	-0.0009	-13.9375	-13.8125
0.3563	8.0471	4100	0.5939	-1.8281	-2.5781	0.7075	0.7539	-596.0	-576.0	-1.4297	-1.3984	-0.0009	-0.0009	-13.9375	-13.8125
0.3484	8.2434	4200	0.5969	-1.875	-2.6406	0.7045	0.7656	-600.0	-580.0	-1.4375	-1.4062	-0.0009	-0.0008	-14.0	-13.875
0.3359	8.4396	4300	0.5966	-1.8828	-2.6562	0.7045	0.7734	-604.0	-580.0	-1.4375	-1.4062	-0.0009	-0.0009	-13.9375	-13.8125
0.3639	8.6359	4400	0.5979	-1.8516	-2.5938	0.7075	0.7461	-596.0	-580.0	-1.4297	-1.3984	-0.0009	-0.0009	-13.9375	-13.8125
0.3563	8.8322	4500	0.5979	-1.8594	-2.625	0.7075	0.7617	-600.0	-580.0	-1.4297	-1.3984	-0.0009	-0.0009	-13.9375	-13.8125
0.353	9.0285	4600	0.5981	-1.8672	-2.625	0.6985	0.7617	-600.0	-580.0	-1.4297	-1.3984	-0.0009	-0.0008	-13.9375	-13.8125
0.3514	9.2247	4700	0.5979	-1.8594	-2.625	0.6985	0.7656	-600.0	-580.0	-1.4297	-1.3984	-0.0009	-0.0008	-13.9375	-13.8125
0.3434	9.4210	4800	0.5973	-1.8672	-2.6406	0.7015	0.7656	-600.0	-580.0	-1.4297	-1.4062	-0.0009	-0.0008	-13.9375	-13.8125
0.3492	9.6173	4900	0.5981	-1.875	-2.6406	0.7045	0.7578	-600.0	-580.0	-1.4297	-1.3984	-0.0009	-0.0008	-13.9375	-13.8125
0.3487	9.8135	5000	0.5967	-1.8672	-2.6406	0.7134	0.7734	-600.0	-580.0	-1.4375	-1.4062	-0.0009	-0.0008	-13.9375	-13.8125

Framework versions

Transformers 4.40.0
Pytorch 2.2.2+cu121
Datasets 2.19.0
Tokenizers 0.19.1

RichardErkhov/nnheui_-_pythia-1.4b-dpo-full-gguf

Original model description:

pythia-1.4b-dpo-full

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

RichardErkhov/
nnheui_-_pythia-1.4b-dpo-full-gguf