chess-llama-smol-1024

amazingvince/chess-llama-smol-1024

chess-llama-smol-1024 is a text generation model from amazingvince. Use it when you need the model to write or continue text. It is set up for transformers.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Text Generation
Library: transformers
Model type: llama
Access: Public
Created: Jan 7, 2024
Updated: Jan 9, 2024
SHA: dc9c2f44

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Base models

chess-llama/config.json

Model card

From the Hugging Face model README

mini-1024

This model is a fine-tuned version of chess-llama/config.json on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.4399
Accuracy: 0.7228

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: 4e-05
train_batch_size: 8
eval_batch_size: 8
seed: 8326
distributed_type: multi-GPU
gradient_accumulation_steps: 8
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-06
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.01
num_epochs: 1.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
2.1877	0.0	200	2.1894	0.5763
1.612	0.01	400	1.5928	0.6109
1.2645	0.01	600	1.2604	0.6379
1.0711	0.01	800	1.0720	0.6547
0.953	0.02	1000	0.9513	0.6628
0.9024	0.02	1200	0.8965	0.6678
0.8682	0.02	1400	0.8618	0.6712
0.8366	0.03	1600	0.8343	0.6741
0.8127	0.03	1800	0.8146	0.6763
0.7916	0.03	2000	0.7940	0.6784
0.781	0.04	2200	0.7815	0.6799
0.7647	0.04	2400	0.7692	0.6813
0.7617	0.04	2600	0.7589	0.6825
0.7523	0.05	2800	0.7466	0.6842
0.7394	0.05	3000	0.7373	0.6852
0.7297	0.05	3200	0.7279	0.6864
0.712	0.06	3400	0.7206	0.6871
0.716	0.06	3600	0.7120	0.6884
0.6994	0.06	3800	0.7044	0.6893
0.6885	0.07	4000	0.6969	0.6903
0.6887	0.07	4200	0.6916	0.6908
0.6812	0.07	4400	0.6840	0.6917
0.6799	0.08	4600	0.6774	0.6927
0.6672	0.08	4800	0.6718	0.6935
0.6658	0.08	5000	0.6653	0.6939
0.6455	0.09	5200	0.6609	0.6948
0.661	0.09	5400	0.6569	0.6953
0.648	0.09	5600	0.6505	0.6960
0.6453	0.1	5800	0.6458	0.6967
0.6374	0.1	6000	0.6407	0.6973
0.6351	0.1	6200	0.6363	0.6977
0.6273	0.11	6400	0.6328	0.6983
0.6234	0.11	6600	0.6292	0.6987
0.6204	0.12	6800	0.6247	0.6992
0.6179	0.12	7000	0.6217	0.6994
0.6122	0.12	7200	0.6169	0.7001
0.6096	0.13	7400	0.6132	0.7006
0.6046	0.13	7600	0.6101	0.7011
0.5997	0.13	7800	0.6072	0.7016
0.5988	0.14	8000	0.6047	0.7015
0.5995	0.14	8200	0.6011	0.7022
0.6017	0.14	8400	0.5985	0.7024
0.5962	0.15	8600	0.5944	0.7028
0.5857	0.15	8800	0.5919	0.7034
0.5829	0.15	9000	0.5903	0.7034
0.5862	0.16	9200	0.5856	0.7040
0.5786	0.16	9400	0.5834	0.7044
0.5785	0.16	9600	0.5813	0.7044
0.5819	0.17	9800	0.5788	0.7049
0.5804	0.17	10000	0.5768	0.7051
0.5755	0.17	10200	0.5748	0.7053
0.57	0.18	10400	0.5728	0.7057
0.567	0.18	10600	0.5699	0.7059
0.5629	0.18	10800	0.5672	0.7063
0.5615	0.19	11000	0.5648	0.7066
0.5628	0.19	11200	0.5633	0.7067
0.5628	0.19	11400	0.5610	0.7070
0.5551	0.2	11600	0.5588	0.7075
0.5572	0.2	11800	0.5558	0.7078
0.5543	0.2	12000	0.5559	0.7076
0.5512	0.21	12200	0.5536	0.7080
0.5491	0.21	12400	0.5517	0.7081
0.5455	0.21	12600	0.5494	0.7085
0.5494	0.22	12800	0.5480	0.7085
0.5438	0.22	13000	0.5461	0.7087
0.5492	0.22	13200	0.5449	0.7090
0.5385	0.23	13400	0.5432	0.7092
0.5399	0.23	13600	0.5411	0.7094
0.5416	0.23	13800	0.5406	0.7095
0.5316	0.24	14000	0.5379	0.7099
0.5305	0.24	14200	0.5367	0.7102
0.5349	0.24	14400	0.5337	0.7106
0.5313	0.25	14600	0.5329	0.7104
0.5336	0.25	14800	0.5324	0.7107
0.529	0.25	15000	0.5306	0.7107
0.5283	0.26	15200	0.5291	0.7109
0.5241	0.26	15400	0.5277	0.7111
0.5298	0.26	15600	0.5265	0.7113
0.5199	0.27	15800	0.5255	0.7113
0.5303	0.27	16000	0.5237	0.7116
0.5184	0.27	16200	0.5228	0.7118
0.5171	0.28	16400	0.5206	0.7122
0.525	0.28	16600	0.5205	0.7122
0.5191	0.28	16800	0.5191	0.7123
0.5161	0.29	17000	0.5182	0.7124
0.5205	0.29	17200	0.5160	0.7126
0.5157	0.29	17400	0.5156	0.7128
0.5071	0.3	17600	0.5140	0.7129
0.5151	0.3	17800	0.5129	0.7130
0.5127	0.3	18000	0.5124	0.7130
0.5098	0.31	18200	0.5112	0.7133
0.5099	0.31	18400	0.5104	0.7134
0.5056	0.31	18600	0.5084	0.7135
0.5093	0.32	18800	0.5078	0.7138
0.5033	0.32	19000	0.5069	0.7139
0.5013	0.32	19200	0.5063	0.7139
0.5087	0.33	19400	0.5049	0.7140
0.5041	0.33	19600	0.5037	0.7144
0.4994	0.34	19800	0.5035	0.7144
0.5025	0.34	20000	0.5027	0.7144
0.5005	0.34	20200	0.5020	0.7144
0.4972	0.35	20400	0.5012	0.7147
0.5047	0.35	20600	0.5005	0.7145
0.4986	0.35	20800	0.4995	0.7148
0.497	0.36	21000	0.4982	0.7150
0.4986	0.36	21200	0.4971	0.7151
0.4918	0.36	21400	0.4967	0.7152
0.5001	0.37	21600	0.4961	0.7153
0.4934	0.37	21800	0.4952	0.7154
0.4948	0.37	22000	0.4947	0.7155
0.4878	0.38	22200	0.4930	0.7157
0.4913	0.38	22400	0.4926	0.7157
0.487	0.38	22600	0.4921	0.7158
0.4919	0.39	22800	0.4913	0.7158
0.4904	0.39	23000	0.4907	0.7160
0.4863	0.39	23200	0.4903	0.7161
0.4858	0.4	23400	0.4896	0.7160
0.487	0.4	23600	0.4891	0.7163
0.4869	0.4	23800	0.4879	0.7163
0.4851	0.41	24000	0.4869	0.7164
0.4903	0.41	24200	0.4867	0.7164
0.4845	0.41	24400	0.4856	0.7166
0.4797	0.42	24600	0.4852	0.7168
0.4799	0.42	24800	0.4850	0.7168
0.4751	0.42	25000	0.4843	0.7168
0.4745	0.43	25200	0.4836	0.7170
0.4811	0.43	25400	0.4827	0.7170
0.4805	0.43	25600	0.4828	0.7171
0.483	0.44	25800	0.4821	0.7171
0.4825	0.44	26000	0.4815	0.7172
0.4749	0.44	26200	0.4803	0.7173
0.477	0.45	26400	0.4796	0.7174
0.476	0.45	26600	0.4792	0.7176
0.4776	0.45	26800	0.4790	0.7175
0.4811	0.46	27000	0.4780	0.7177
0.4761	0.46	27200	0.4776	0.7177
0.4727	0.46	27400	0.4771	0.7178
0.4737	0.47	27600	0.4761	0.7179
0.4722	0.47	27800	0.4760	0.7180
0.4713	0.47	28000	0.4753	0.7182
0.4711	0.48	28200	0.4747	0.7182
0.4738	0.48	28400	0.4750	0.7182
0.4764	0.48	28600	0.4738	0.7183
0.4725	0.49	28800	0.4727	0.7184
0.468	0.49	29000	0.4728	0.7184
0.4727	0.49	29200	0.4719	0.7185
0.4662	0.5	29400	0.4721	0.7186
0.4655	0.5	29600	0.4711	0.7186
0.4759	0.5	29800	0.4709	0.7187
0.4647	0.51	30000	0.4706	0.7187
0.4738	0.51	30200	0.4703	0.7187
0.4751	0.51	30400	0.4694	0.7190
0.4677	0.52	30600	0.4690	0.7189
0.4605	0.52	30800	0.4687	0.7189
0.466	0.52	31000	0.4686	0.7189
0.4626	0.53	31200	0.4678	0.7191
0.4616	0.53	31400	0.4672	0.7192
0.4698	0.53	31600	0.4665	0.7192
0.4599	0.54	31800	0.4664	0.7193
0.4618	0.54	32000	0.4660	0.7192
0.459	0.55	32200	0.4659	0.7192
0.4608	0.55	32400	0.4654	0.7194
0.4623	0.55	32600	0.4651	0.7194
0.4654	0.56	32800	0.4646	0.7196
0.4585	0.56	33000	0.4638	0.7195
0.4597	0.56	33200	0.4636	0.7196
0.4568	0.57	33400	0.4631	0.7198
0.4634	0.57	33600	0.4630	0.7197
0.4648	0.57	33800	0.4624	0.7197
0.4609	0.58	34000	0.4621	0.7198
0.4619	0.58	34200	0.4620	0.7197
0.4603	0.58	34400	0.4614	0.7199
0.4566	0.59	34600	0.4611	0.7199
0.4581	0.59	34800	0.4604	0.7201
0.4583	0.59	35000	0.4599	0.7202
0.4607	0.6	35200	0.4597	0.7202
0.4533	0.6	35400	0.4592	0.7202
0.4619	0.6	35600	0.4591	0.7202
0.46	0.61	35800	0.4585	0.7203
0.4516	0.61	36000	0.4582	0.7203
0.457	0.61	36200	0.4582	0.7203
0.4544	0.62	36400	0.4576	0.7204
0.4515	0.62	36600	0.4569	0.7205
0.4573	0.62	36800	0.4568	0.7206
0.4517	0.63	37000	0.4565	0.7206
0.4529	0.63	37200	0.4559	0.7207
0.4562	0.63	37400	0.4560	0.7207
0.4586	0.64	37600	0.4556	0.7207
0.4561	0.64	37800	0.4549	0.7208
0.4566	0.64	38000	0.4548	0.7207
0.4487	0.65	38200	0.4545	0.7207
0.452	0.65	38400	0.4542	0.7209
0.4529	0.65	38600	0.4540	0.7209
0.4473	0.66	38800	0.4542	0.7209
0.4479	0.66	39000	0.4533	0.7210
0.4494	0.66	39200	0.4530	0.7211
0.4502	0.67	39400	0.4527	0.7211
0.4496	0.67	39600	0.4523	0.7211
0.4492	0.67	39800	0.4520	0.7213
0.4491	0.68	40000	0.4518	0.7211
0.4499	0.68	40200	0.4514	0.7212
0.4477	0.68	40400	0.4514	0.7213
0.4448	0.69	40600	0.4511	0.7213
0.4526	0.69	40800	0.4506	0.7214
0.4425	0.69	41000	0.4504	0.7214
0.4506	0.7	41200	0.4501	0.7214
0.4492	0.7	41400	0.4498	0.7216
0.4481	0.7	41600	0.4495	0.7215
0.451	0.71	41800	0.4494	0.7216
0.4479	0.71	42000	0.4493	0.7215
0.4546	0.71	42200	0.4489	0.7216
0.4439	0.72	42400	0.4489	0.7217
0.4454	0.72	42600	0.4487	0.7217
0.4508	0.72	42800	0.4484	0.7217
0.448	0.73	43000	0.4483	0.7217
0.447	0.73	43200	0.4479	0.7217
0.4508	0.73	43400	0.4477	0.7217
0.4397	0.74	43600	0.4473	0.7218
0.4453	0.74	43800	0.4473	0.7219
0.4479	0.74	44000	0.4469	0.7219
0.4421	0.75	44200	0.4466	0.7220
0.4479	0.75	44400	0.4464	0.7220
0.4492	0.75	44600	0.4463	0.7220
0.4466	0.76	44800	0.4460	0.7221
0.4543	0.76	45000	0.4458	0.7221
0.4452	0.77	45200	0.4456	0.7221
0.4456	0.77	45400	0.4454	0.7221
0.4455	0.77	45600	0.4452	0.7221
0.4405	0.78	45800	0.4451	0.7221
0.4449	0.78	46000	0.4448	0.7223
0.4433	0.78	46200	0.4447	0.7223
0.445	0.79	46400	0.4447	0.7223
0.447	0.79	46600	0.4444	0.7223
0.4405	0.79	46800	0.4444	0.7222
0.4434	0.8	47000	0.4443	0.7222
0.4385	0.8	47200	0.4440	0.7223
0.442	0.8	47400	0.4439	0.7223
0.4402	0.81	47600	0.4437	0.7224
0.4368	0.81	47800	0.4437	0.7224
0.4392	0.81	48000	0.4435	0.7223
0.439	0.82	48200	0.4434	0.7225
0.4407	0.82	48400	0.4431	0.7225
0.4484	0.82	48600	0.4430	0.7225
0.4419	0.83	48800	0.4430	0.7224
0.4453	0.83	49000	0.4426	0.7225
0.4415	0.83	49200	0.4425	0.7225
0.4424	0.84	49400	0.4425	0.7225
0.4389	0.84	49600	0.4423	0.7226
0.4377	0.84	49800	0.4421	0.7226
0.4388	0.85	50000	0.4420	0.7227
0.4409	0.85	50200	0.4419	0.7225
0.442	0.85	50400	0.4417	0.7227
0.4371	0.86	50600	0.4417	0.7227
0.4384	0.86	50800	0.4415	0.7226
0.4402	0.86	51000	0.4415	0.7227
0.4375	0.87	51200	0.4414	0.7227
0.4367	0.87	51400	0.4413	0.7227
0.4447	0.87	51600	0.4412	0.7227
0.4434	0.88	51800	0.4411	0.7227
0.4357	0.88	52000	0.4411	0.7228
0.4404	0.88	52200	0.4410	0.7228
0.4369	0.89	52400	0.4409	0.7228
0.4348	0.89	52600	0.4409	0.7228
0.4394	0.89	52800	0.4408	0.7227
0.437	0.9	53000	0.4407	0.7227
0.438	0.9	53200	0.4407	0.7228
0.4421	0.9	53400	0.4406	0.7228
0.4421	0.91	53600	0.4405	0.7228
0.4361	0.91	53800	0.4405	0.7228
0.4367	0.91	54000	0.4404	0.7228
0.4371	0.92	54200	0.4403	0.7228
0.4349	0.92	54400	0.4403	0.7229
0.4432	0.92	54600	0.4403	0.7229
0.4355	0.93	54800	0.4402	0.7228
0.4402	0.93	55000	0.4402	0.7229
0.4403	0.93	55200	0.4401	0.7229
0.4445	0.94	55400	0.4401	0.7229
0.4336	0.94	55600	0.4401	0.7228
0.431	0.94	55800	0.4401	0.7229
0.4343	0.95	56000	0.4400	0.7229
0.4298	0.95	56200	0.4400	0.7229
0.43	0.95	56400	0.4400	0.7229
0.4446	0.96	56600	0.4400	0.7229
0.4417	0.96	56800	0.4400	0.7229
0.4431	0.96	57000	0.4400	0.7229
0.4353	0.97	57200	0.4399	0.7229
0.4351	0.97	57400	0.4399	0.7229
0.4398	0.97	57600	0.4399	0.7229
0.4368	0.98	57800	0.4399	0.7229
0.4379	0.98	58000	0.4399	0.7229
0.4353	0.99	58200	0.4399	0.7229
0.4397	0.99	58400	0.4399	0.7229
0.4401	0.99	58600	0.4399	0.7229
0.4366	1.0	58800	0.4399	0.7229
0.434	1.0	59000	0.4399	0.7228

Framework versions

Transformers 4.37.0.dev0
Pytorch 2.1.2+cu121
Datasets 2.15.0
Tokenizers 0.15.0

Task: Text Generation
Library: transformers
Type: llama
Created: Jan 7, 2024
Updated: Jan 9, 2024