
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# model

This model is a fine-tuned version of [musiclang/musiclang-v2-xl](https://huggingface.co/musiclang/musiclang-v2-xl) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2930

## 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: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 500
- training_steps: 0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.4098        | 0.03  | 2000  | 0.3828          |
| 0.3901        | 0.07  | 4000  | 0.3684          |
| 0.3737        | 0.1   | 6000  | 0.3569          |
| 0.3652        | 0.13  | 8000  | 0.3489          |
| 0.356         | 0.17  | 10000 | 0.3393          |
| 0.35          | 0.2   | 12000 | 0.3342          |
| 0.3443        | 0.23  | 14000 | 0.3282          |
| 0.3371        | 0.26  | 16000 | 0.3235          |
| 0.3361        | 0.3   | 18000 | 0.3201          |
| 0.3301        | 0.33  | 20000 | 0.3160          |
| 0.3253        | 0.36  | 22000 | 0.3131          |
| 0.327         | 0.4   | 24000 | 0.3109          |
| 0.3225        | 0.43  | 26000 | 0.3089          |
| 0.3156        | 0.46  | 28000 | 0.3066          |
| 0.3147        | 0.5   | 30000 | 0.3045          |
| 0.3182        | 0.53  | 32000 | 0.3026          |
| 0.3129        | 0.56  | 34000 | 0.3017          |
| 0.3132        | 0.59  | 36000 | 0.3008          |
| 0.3109        | 0.63  | 38000 | 0.2987          |
| 0.3092        | 0.66  | 40000 | 0.2972          |
| 0.3091        | 0.69  | 42000 | 0.2963          |
| 0.3034        | 0.73  | 44000 | 0.2960          |
| 0.3061        | 0.76  | 46000 | 0.2956          |
| 0.3044        | 0.79  | 48000 | 0.2946          |
| 0.3036        | 0.83  | 50000 | 0.2940          |
| 0.3003        | 0.86  | 52000 | 0.2939          |
| 0.303         | 0.89  | 54000 | 0.2934          |
| 0.3007        | 0.93  | 56000 | 0.2932          |
| 0.3009        | 0.96  | 58000 | 0.2930          |
| 0.3           | 0.99  | 60000 | 0.2930          |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1

{"tags": ["generated_from_trainer"], "base_model": "musiclang/musiclang-v2-xl", "model-index": [{"name": "model", "results": []}]}
transformers pytorch tensorboard safetensors gpt2 text-generation generated_from_trainer base_model:musiclang/musiclang-v2-xl autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-musiclang/musiclang-v2-xl #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 

model
=====


This model is a fine-tuned version of musiclang/musiclang-v2-xl on the None dataset.
It achieves the following results on the evaluation set:


* Loss: 0.2930


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: 24
* eval\_batch\_size: 24
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine\_with\_restarts
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 0
* mixed\_precision\_training: Native AMP


### Training results



### Framework versions


* Transformers 4.37.2
* Pytorch 2.2.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1



### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 24
* eval\_batch\_size: 24
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine\_with\_restarts
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.37.2
* Pytorch 2.2.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
TAGS
#transformers #pytorch #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-musiclang/musiclang-v2-xl #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 ### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 24
* eval\_batch\_size: 24
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine\_with\_restarts
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 0
* mixed\_precision\_training: Native AMP ### Training results ### Framework versions


* Transformers 4.37.2
* Pytorch 2.2.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1
TAGS
#transformers #pytorch #tensorboard #safetensors #gpt2 #text-generation #generated_from_trainer #base_model-musiclang/musiclang-v2-xl #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
### Training hyperparameters


The following hyperparameters were used during training:


* learning\_rate: 0.0001
* train\_batch\_size: 24
* eval\_batch\_size: 24
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine\_with\_restarts
* lr\_scheduler\_warmup\_steps: 500
* training\_steps: 0
* mixed\_precision\_training: Native AMP### Training results### Framework versions


* Transformers 4.37.2
* Pytorch 2.2.0+cu121
* Datasets 2.17.0
* Tokenizers 0.15.1