MIDI_Transformer_Mistral_10k-vocab_100k-steps Open-source Model - Unleashing New Possibilities for Music Creation

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MIDI Transformer Mistral 10k Vocab 100k Steps

Developed by sunsetsobserver

This model is a fine-tuned version based on an unknown dataset, with unspecified task and architecture details.

Large Language Model

Transformers

#Low-accuracy model #Small-batch training #Long-cycle optimization

Downloads 44

Release Time : 2/21/2024

Model Overview

This model is a fine-tuned version based on an unknown dataset, achieving an accuracy of 0.0013 and a loss of 24.0950 on the evaluation set. Its specific functions and purposes remain unclear.

Model Features

Low accuracy

The model's accuracy on the evaluation set is only 0.0013, indicating poor performance.

High loss

The model's loss on the evaluation set is 24.0950, suggesting suboptimal performance.

Model Capabilities

Use Cases

🚀 runs

This is a fine - tuned model based on an unknown base model from trained on an unknown dataset. It achieves specific loss and accuracy results on the evaluation set, which can be used for relevant tasks.

🚀 Quick Start

This model is a fine - tuned version of on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 24.0950
Accuracy: 0.0013

📚 Documentation

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: 0.0001
train_batch_size: 16
eval_batch_size: 48
seed: 444
gradient_accumulation_steps: 3
total_train_batch_size: 48
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e - 08
lr_scheduler_type: cosine_with_restarts
lr_scheduler_warmup_ratio: 0.3
training_steps: 100000
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
8.2359	6.04	1000	8.2170	0.0070
7.7137	12.07	2000	7.7007	0.0064
6.5277	18.11	3000	6.5254	0.0000
6.0375	24.14	4000	6.0532	0.0000
5.6908	30.18	5000	5.7100	0.0001
5.4294	36.22	6000	5.4758	0.0002
5.2161	42.25	7000	5.2891	0.0006
5.0151	48.29	8000	5.1152	0.0021
4.8349	54.33	9000	4.9847	0.0020
4.6358	60.36	10000	4.8754	0.0022
4.4326	66.4	11000	4.7809	0.0021
4.2632	72.43	12000	4.7416	0.0017
4.0415	78.47	13000	4.7503	0.0016
3.8196	84.51	14000	4.8472	0.0014
3.6207	90.54	15000	5.0215	0.0014
3.3163	96.58	16000	5.2939	0.0014
3.0377	102.62	17000	5.6685	0.0014
2.7272	108.65	18000	6.1649	0.0013
2.4319	114.69	19000	6.7556	0.0013
2.1647	120.72	20000	7.3951	0.0013
1.9001	126.76	21000	8.0823	0.0013
1.6708	132.8	22000	8.8230	0.0013
1.4762	138.83	23000	9.5335	0.0013
1.2833	144.87	24000	10.1973	0.0013
1.1451	150.91	25000	10.8213	0.0013
1.0251	156.94	26000	11.4402	0.0013
0.9164	162.98	27000	11.9995	0.0013
0.8174	169.01	28000	12.5680	0.0013
0.6862	175.05	29000	13.0050	0.0013
0.5738	181.09	30000	13.4692	0.0013
0.4524	187.12	31000	13.9220	0.0013
0.4252	193.16	32000	14.3340	0.0013
0.3952	199.2	33000	14.7961	0.0013
0.3684	205.23	34000	15.2421	0.0013
0.3338	211.27	35000	15.6433	0.0013
0.307	217.3	36000	16.0182	0.0013
0.2951	223.34	37000	16.3087	0.0013
0.28	229.38	38000	16.6556	0.0013
0.2688	235.41	39000	16.9303	0.0013
0.2582	241.45	40000	17.2209	0.0013
0.238	247.48	41000	17.5311	0.0013
0.2261	253.52	42000	17.7731	0.0013
0.21	259.56	43000	18.0205	0.0013
0.2073	265.59	44000	18.2693	0.0013
0.1976	271.63	45000	18.4634	0.0013
0.1865	277.67	46000	18.7215	0.0012
0.1769	283.7	47000	18.9467	0.0013
0.1649	289.74	48000	19.1423	0.0013
0.1517	295.77	49000	19.3638	0.0013
0.1491	301.81	50000	19.5879	0.0013
0.1387	307.85	51000	19.7823	0.0013
0.1332	313.88	52000	19.9663	0.0013
0.1256	319.92	53000	20.1907	0.0013
0.1154	325.96	54000	20.3939	0.0013
0.1091	331.99	55000	20.5926	0.0013
0.0928	338.03	56000	20.8044	0.0013
0.0812	344.06	57000	20.9873	0.0013
0.0677	350.1	58000	21.1931	0.0013
0.0609	356.14	59000	21.3650	0.0013
0.058	362.17	60000	21.5868	0.0013
0.0532	368.21	61000	21.7740	0.0013
0.0481	374.25	62000	21.9339	0.0013
0.0358	380.28	63000	22.1660	0.0012
0.0117	386.32	64000	22.4226	0.0013
0.0768	392.35	65000	22.2193	0.0013
0.0339	398.39	66000	22.3833	0.0013
0.0191	404.43	67000	22.5927	0.0013
0.0493	410.46	68000	22.6069	0.0013
0.0115	416.5	69000	22.8652	0.0012
0.0111	422.54	70000	22.9982	0.0012
0.1182	428.57	71000	22.6628	0.0013
0.0118	434.61	72000	22.9036	0.0013
0.0111	440.64	73000	23.0692	0.0013
0.0106	446.68	74000	23.1857	0.0013
0.0386	452.72	75000	22.9263	0.0013
0.0109	458.75	76000	23.1548	0.0013
0.0109	464.79	77000	23.2761	0.0012
0.0108	470.82	78000	23.3763	0.0013
0.0131	476.86	79000	23.2048	0.0013
0.0108	482.9	80000	23.3772	0.0013
0.0106	488.93	81000	23.4733	0.0013
0.0106	494.97	82000	23.5654	0.0013
0.0242	501.01	83000	23.5459	0.0013
0.0104	507.04	84000	23.5695	0.0013
0.01	513.08	85000	23.6659	0.0013
0.0098	519.11	86000	23.7337	0.0013
0.0097	525.15	87000	23.7961	0.0013
0.0097	531.19	88000	23.8573	0.0013
0.0097	537.22	89000	23.9052	0.0013
0.0097	543.26	90000	23.9524	0.0013
0.0096	549.3	91000	23.9823	0.0013
0.0096	555.33	92000	24.0084	0.0013
0.0095	561.37	93000	24.0364	0.0013
0.0095	567.4	94000	24.0545	0.0013
0.0094	573.44	95000	24.0701	0.0013
0.0094	579.48	96000	24.0826	0.0013
0.0093	585.51	97000	24.0898	0.0013
0.0093	591.55	98000	24.0935	0.0013
0.0093	597.59	99000	24.0944	0.0013
0.0092	603.62	100000	24.0950	0.0013

Framework versions

Transformers 4.37.2
Pytorch 2.2.0+cu121
Datasets 2.17.0
Tokenizers 0.15.1

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