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RichardErkhov/
focuzz8_-_smollm-1.7b-instruct-simpo-v2-gguf

Name: focuzz8_-_smollm-1.7b-instruct-simpo-v2-gguf
Availability: InStock

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smollm-1.7b-instruct-simpo-v2 - GGUF

Model creator: https://huggingface.co/focuzz8/
Original model: https://huggingface.co/focuzz8/smollm-1.7b-instruct-simpo-v2/

Name	Quant method	Size
smollm-1.7b-instruct-simpo-v2.Q2_K.gguf	Q2_K	0.63GB
smollm-1.7b-instruct-simpo-v2.IQ3_XS.gguf	IQ3_XS	0.69GB
smollm-1.7b-instruct-simpo-v2.IQ3_S.gguf	IQ3_S	0.72GB
smollm-1.7b-instruct-simpo-v2.Q3_K_S.gguf	Q3_K_S	0.72GB
smollm-1.7b-instruct-simpo-v2.IQ3_M.gguf	IQ3_M	0.75GB
smollm-1.7b-instruct-simpo-v2.Q3_K.gguf	Q3_K	0.8GB
smollm-1.7b-instruct-simpo-v2.Q3_K_M.gguf	Q3_K_M	0.8GB
smollm-1.7b-instruct-simpo-v2.Q3_K_L.gguf	Q3_K_L	0.87GB
smollm-1.7b-instruct-simpo-v2.IQ4_XS.gguf	IQ4_XS	0.88GB
smollm-1.7b-instruct-simpo-v2.Q4_0.gguf	Q4_0	0.92GB
smollm-1.7b-instruct-simpo-v2.IQ4_NL.gguf	IQ4_NL	0.93GB
smollm-1.7b-instruct-simpo-v2.Q4_K_S.gguf	Q4_K_S	0.93GB
smollm-1.7b-instruct-simpo-v2.Q4_K.gguf	Q4_K	0.98GB
smollm-1.7b-instruct-simpo-v2.Q4_K_M.gguf	Q4_K_M	0.98GB
smollm-1.7b-instruct-simpo-v2.Q4_1.gguf	Q4_1	1.02GB
smollm-1.7b-instruct-simpo-v2.Q5_0.gguf	Q5_0	1.11GB
smollm-1.7b-instruct-simpo-v2.Q5_K_S.gguf	Q5_K_S	1.11GB
smollm-1.7b-instruct-simpo-v2.Q5_K.gguf	Q5_K	1.14GB
smollm-1.7b-instruct-simpo-v2.Q5_K_M.gguf	Q5_K_M	1.14GB
smollm-1.7b-instruct-simpo-v2.Q5_1.gguf	Q5_1	1.2GB
smollm-1.7b-instruct-simpo-v2.Q6_K.gguf	Q6_K	1.31GB
smollm-1.7b-instruct-simpo-v2.Q8_0.gguf	Q8_0	1.7GB

Original model description:

library_name: transformers license: apache-2.0 base_model: HuggingFaceTB/SmolLM-1.7B-Instruct tags:

alignment-handbook
generated_from_trainer datasets:
BAAI/Infinity-Preference model-index:
name: smollm-1.7b-instruct-simpo-v2 results: []

smollm-1.7b-instruct-simpo-v2

This model is a fine-tuned version of HuggingFaceTB/SmolLM-1.7B-Instruct on the BAAI/Infinity-Preference dataset. It achieves the following results on the evaluation set:

Loss: 3.0877
Rewards/chosen: -22.8949
Rewards/rejected: -24.4444
Rewards/accuracies: 0.6300
Rewards/margins: 1.5495
Logps/rejected: -2.4444
Logps/chosen: -2.2895
Logits/rejected: -2.4913
Logits/chosen: -2.3131

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-06
train_batch_size: 2
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
3.2871	0.0135	400	3.4379	-16.5537	-16.5135	0.4700	-0.0402	-1.6513	-1.6554	-0.7019	-0.7007
3.4746	0.0270	800	3.4370	-16.5561	-16.5146	0.4700	-0.0415	-1.6515	-1.6556	-0.7002	-0.6988
2.8856	0.0404	1200	3.4399	-16.5623	-16.5160	0.4700	-0.0464	-1.6516	-1.6562	-0.6997	-0.6984
3.8819	0.0539	1600	3.4374	-16.5639	-16.5248	0.4700	-0.0391	-1.6525	-1.6564	-0.7012	-0.6998
3.622	0.0674	2000	3.4319	-16.5838	-16.5551	0.4700	-0.0288	-1.6555	-1.6584	-0.7089	-0.7069
3.6924	0.0809	2400	3.4273	-16.6109	-16.5901	0.4700	-0.0208	-1.6590	-1.6611	-0.7032	-0.7007
3.0591	0.0944	2800	3.4161	-16.6863	-16.6979	0.4600	0.0117	-1.6698	-1.6686	-0.7295	-0.7253
3.4937	0.1079	3200	3.4013	-16.7982	-16.8590	0.4700	0.0608	-1.6859	-1.6798	-0.7483	-0.7412
3.1565	0.1213	3600	3.3852	-16.8542	-16.9385	0.4700	0.0843	-1.6939	-1.6854	-0.7618	-0.7526
2.7504	0.1348	4000	3.3711	-16.9128	-17.0175	0.4800	0.1047	-1.7018	-1.6913	-0.7684	-0.7574
3.0312	0.1483	4400	3.3606	-16.9720	-17.0910	0.4900	0.1190	-1.7091	-1.6972	-0.7754	-0.7629
4.145	0.1618	4800	3.3407	-17.0816	-17.2375	0.5100	0.1559	-1.7238	-1.7082	-0.7902	-0.7746
3.9514	0.1753	5200	3.3126	-17.1952	-17.3924	0.5100	0.1972	-1.7392	-1.7195	-0.8201	-0.8001
2.4942	0.1887	5600	3.2864	-17.2731	-17.4955	0.5100	0.2223	-1.7495	-1.7273	-0.8187	-0.7960
2.6757	0.2022	6000	3.2615	-17.3603	-17.6063	0.5200	0.2460	-1.7606	-1.7360	-0.7977	-0.7735
2.8576	0.2157	6400	3.2382	-17.5060	-17.8132	0.5500	0.3072	-1.7813	-1.7506	-0.8562	-0.8260
3.7483	0.2292	6800	3.2140	-17.5965	-17.9376	0.5700	0.3411	-1.7938	-1.7596	-0.8751	-0.8407
3.5349	0.2427	7200	3.2035	-17.6663	-18.0193	0.5800	0.3530	-1.8019	-1.7666	-0.8780	-0.8417
2.0604	0.2562	7600	3.1925	-17.7393	-18.1045	0.6100	0.3652	-1.8104	-1.7739	-0.9017	-0.8602
5.7031	0.2696	8000	3.1672	-18.0175	-18.4936	0.6100	0.4760	-1.8494	-1.8018	-0.9982	-0.9467
2.6005	0.2831	8400	3.1475	-18.1162	-18.6283	0.6100	0.5121	-1.8628	-1.8116	-1.0732	-1.0161
1.9787	0.2966	8800	3.1226	-18.3260	-18.9198	0.6100	0.5938	-1.8920	-1.8326	-1.1691	-1.1062
2.8347	0.3101	9200	3.1156	-18.4632	-19.0934	0.6100	0.6301	-1.9093	-1.8463	-1.2592	-1.1910
2.701	0.3236	9600	3.1022	-18.5083	-19.1346	0.6100	0.6264	-1.9135	-1.8508	-1.2785	-1.2073
3.772	0.3371	10000	3.0772	-18.5843	-19.2491	0.6100	0.6649	-1.9249	-1.8584	-1.3345	-1.2587
2.7414	0.3505	10400	3.0551	-18.8305	-19.5946	0.6100	0.7641	-1.9595	-1.8830	-1.3824	-1.3004
2.0287	0.3640	10800	3.0534	-18.9934	-19.7985	0.6200	0.8051	-1.9798	-1.8993	-1.4355	-1.3467
1.0473	0.3775	11200	3.0528	-19.1581	-19.9858	0.6100	0.8277	-1.9986	-1.9158	-1.5109	-1.4173
2.8106	0.3910	11600	3.0436	-19.1763	-19.9989	0.6100	0.8226	-1.9999	-1.9176	-1.5138	-1.4206
3.0344	0.4045	12000	3.0333	-19.2526	-20.1079	0.6100	0.8553	-2.0108	-1.9253	-1.5628	-1.4657
2.1886	0.4179	12400	3.0187	-19.4500	-20.3818	0.6300	0.9318	-2.0382	-1.9450	-1.6246	-1.5217
4.1181	0.4314	12800	3.0086	-19.6204	-20.6104	0.6300	0.9900	-2.0610	-1.9620	-1.6886	-1.5818
1.6647	0.4449	13200	3.0126	-19.7773	-20.7949	0.6300	1.0176	-2.0795	-1.9777	-1.7307	-1.6181
4.8533	0.4584	13600	3.0012	-19.9001	-20.9633	0.6300	1.0632	-2.0963	-1.9900	-1.7437	-1.6288
2.9945	0.4719	14000	3.0071	-19.9831	-21.0361	0.6300	1.0529	-2.1036	-1.9983	-1.7839	-1.6667
2.9377	0.4854	14400	2.9946	-20.1165	-21.2172	0.6400	1.1007	-2.1217	-2.0117	-1.8386	-1.7178
2.7856	0.4988	14800	2.9908	-20.2830	-21.4151	0.6300	1.1322	-2.1415	-2.0283	-1.8720	-1.7468
4.9446	0.5123	15200	2.9905	-20.4144	-21.5669	0.6300	1.1525	-2.1567	-2.0414	-1.9057	-1.7760
3.2834	0.5258	15600	2.9858	-20.4428	-21.5993	0.6300	1.1565	-2.1599	-2.0443	-1.8928	-1.7633
1.8705	0.5393	16000	2.9888	-20.5922	-21.7774	0.6300	1.1853	-2.1777	-2.0592	-1.9340	-1.8009
4.0587	0.5528	16400	2.9925	-20.8812	-22.1359	0.6300	1.2547	-2.2136	-2.0881	-2.0019	-1.8627
3.0706	0.5662	16800	2.9946	-21.1005	-22.4176	0.6300	1.3171	-2.2418	-2.1101	-2.0533	-1.9104
3.152	0.5797	17200	2.9916	-21.2937	-22.6723	0.6200	1.3786	-2.2672	-2.1294	-2.1094	-1.9627
1.8856	0.5932	17600	2.9847	-21.2727	-22.6463	0.6200	1.3736	-2.2646	-2.1273	-2.1108	-1.9637
1.1291	0.6067	18000	2.9981	-21.5313	-22.9507	0.6200	1.4194	-2.2951	-2.1531	-2.1736	-2.0212
2.9894	0.6202	18400	3.0033	-21.6191	-23.0276	0.6200	1.4085	-2.3028	-2.1619	-2.2089	-2.0543
3.497	0.6337	18800	3.0252	-21.8198	-23.2426	0.6200	1.4228	-2.3243	-2.1820	-2.2285	-2.0714
3.18	0.6471	19200	3.0307	-21.8887	-23.3005	0.6200	1.4117	-2.3300	-2.1889	-2.2462	-2.0862
1.9522	0.6606	19600	3.0391	-21.9179	-23.3214	0.6300	1.4035	-2.3321	-2.1918	-2.2476	-2.0875
2.4878	0.6741	20000	3.0431	-22.1021	-23.5543	0.6300	1.4522	-2.3554	-2.2102	-2.2969	-2.1333
2.3506	0.6876	20400	3.0453	-22.2379	-23.7220	0.6300	1.4841	-2.3722	-2.2238	-2.3258	-2.1603
3.9719	0.7011	20800	3.0591	-22.2718	-23.7317	0.6300	1.4599	-2.3732	-2.2272	-2.3263	-2.1600
1.4942	0.7146	21200	3.0574	-22.3226	-23.8044	0.6300	1.4819	-2.3804	-2.2323	-2.3352	-2.1680
0.8797	0.7280	21600	3.0616	-22.3419	-23.8235	0.6300	1.4816	-2.3823	-2.2342	-2.3394	-2.1721
2.8176	0.7415	22000	3.0751	-22.4788	-23.9643	0.6300	1.4855	-2.3964	-2.2479	-2.3767	-2.2073
3.3744	0.7550	22400	3.0775	-22.6028	-24.1137	0.6300	1.5109	-2.4114	-2.2603	-2.4146	-2.2423
1.9708	0.7685	22800	3.0768	-22.6249	-24.1479	0.6300	1.5231	-2.4148	-2.2625	-2.4216	-2.2482
2.1589	0.7820	23200	3.0697	-22.6570	-24.1936	0.6300	1.5367	-2.4194	-2.2657	-2.4323	-2.2591
3.0872	0.7954	23600	3.0813	-22.7174	-24.2489	0.6300	1.5315	-2.4249	-2.2717	-2.4430	-2.2683
3.9705	0.8089	24000	3.0806	-22.7644	-24.3076	0.6300	1.5432	-2.4308	-2.2764	-2.4598	-2.2840
3.5691	0.8224	24400	3.0807	-22.7627	-24.2931	0.6300	1.5304	-2.4293	-2.2763	-2.4621	-2.2857
1.4467	0.8359	24800	3.0854	-22.8132	-24.3525	0.6300	1.5393	-2.4353	-2.2813	-2.4742	-2.2963
2.7241	0.8494	25200	3.0862	-22.8300	-24.3745	0.6300	1.5445	-2.4375	-2.2830	-2.4770	-2.2988
2.7441	0.8629	25600	3.0866	-22.8450	-24.3876	0.6300	1.5427	-2.4388	-2.2845	-2.4823	-2.3048
1.4801	0.8763	26000	3.0839	-22.8522	-24.4010	0.6300	1.5488	-2.4401	-2.2852	-2.4827	-2.3057
2.5965	0.8898	26400	3.0841	-22.8629	-24.4169	0.6300	1.5540	-2.4417	-2.2863	-2.4877	-2.3095
3.6415	0.9033	26800	3.0893	-22.8830	-24.4340	0.6300	1.5510	-2.4434	-2.2883	-2.4894	-2.3114
2.0584	0.9168	27200	3.0894	-22.8879	-24.4268	0.6300	1.5389	-2.4427	-2.2888	-2.4917	-2.3134
2.5068	0.9303	27600	3.0896	-22.8936	-24.4408	0.6300	1.5472	-2.4441	-2.2894	-2.4922	-2.3134
0.677	0.9437	28000	3.0835	-22.8876	-24.4472	0.6300	1.5596	-2.4447	-2.2888	-2.4919	-2.3134
2.5931	0.9572	28400	3.0875	-22.8938	-24.4419	0.6300	1.5481	-2.4442	-2.2894	-2.4907	-2.3117
4.4413	0.9707	28800	3.0893	-22.8952	-24.4383	0.6300	1.5431	-2.4438	-2.2895	-2.4914	-2.3131
2.7584	0.9842	29200	3.0874	-22.8946	-24.4410	0.6300	1.5464	-2.4441	-2.2895	-2.4894	-2.3112
4.4406	0.9977	29600	3.0877	-22.8949	-24.4444	0.6300	1.5495	-2.4444	-2.2895	-2.4913	-2.3131

Framework versions

Transformers 4.45.1
Pytorch 2.2.2
Datasets 3.0.1
Tokenizers 0.20.0

RichardErkhov/focuzz8_-_smollm-1.7b-instruct-simpo-v2-gguf

Original model description:

smollm-1.7b-instruct-simpo-v2

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

RichardErkhov/
focuzz8_-_smollm-1.7b-instruct-simpo-v2-gguf