ckpt to nemo or model
Hi for these proposes I wrote a tiny script for my ASR models, that accepts `.ckpt` model and saves it as `.nemo` model

I think something similar should work for NLP models too.

```
import sys
from pathlib import Path

import nemo.collections.asr as nemo_asr


def get_nemo_ckpt_path(ckpt_path: Path) -> Path:
    ckpt_name = ckpt_path.stem
    name, other = ckpt_name.split("--")
    epoch = other.split("=")[-1]
    out_path = ckpt_path.with_name(f"{name}-epoch-{epoch}.nemo")
    return out_path

def export_to_nemo(lightning_ckpt_path: Path):
    model = nemo_asr.models.EncDecCTCModelBPE.load_from_checkpoint(
        str(lightning_ckpt_path)
    )
    ckpt_name = get_nemo_ckpt_path(lightning_ckpt_path)
    out_path = get_nemo_ckpt_path(lightning_ckpt_path)
    model.save_to(str(out_path))
    print(f"Succesfully saved model checkpoint: {out_path} in nemo format")


if __name__ == "__main__":
    in_path = Path(sys.argv[1])
    assert in_path.exists()
    export_to_nemo(in_path)
``` > Hi for these proposes I wrote a tiny script for my ASR models, that accepts `.ckpt` model and saves it as `.nemo` model
> 
> I think something similar should work for NLP models too.
> 
> ```
> import sys
> from pathlib import Path
> 
> import nemo.collections.asr as nemo_asr
> 
> 
> def get_nemo_ckpt_path(ckpt_path: Path) -> Path:
>     ckpt_name = ckpt_path.stem
>     name, other = ckpt_name.split("--")
>     epoch = other.split("=")[-1]
>     out_path = ckpt_path.with_name(f"{name}-epoch-{epoch}.nemo")
>     return out_path
> 
> def export_to_nemo(lightning_ckpt_path: Path):
>     model = nemo_asr.models.EncDecCTCModelBPE.load_from_checkpoint(
>         str(lightning_ckpt_path)
>     )
>     ckpt_name = get_nemo_ckpt_path(lightning_ckpt_path)
>     out_path = get_nemo_ckpt_path(lightning_ckpt_path)
>     model.save_to(str(out_path))
>     print(f"Succesfully saved model checkpoint: {out_path} in nemo format")
> 
> 
> if __name__ == "__main__":
>     in_path = Path(sys.argv[1])
>     assert in_path.exists()
>     export_to_nemo(in_path)
> ```

Thanks for your reply.

It seems like changing the import nemo.collections.asr as nemo_asr might work.

Have you tried converting the Nemo format to gguf? Alternatively, how do you build a model from your Nemo format?

Thank you.
Hi everyone,

I'm using the code [colab](https://github.com/NVIDIA/NeMo/blob/stable/tutorials/nlp/Token_Classification-BioMegatron.ipynb)  to train my own model. The performance looks promising in the first epoch. However, I'm struggling to figure out how to convert the checkpoint to the NeMo format.

```python
[NeMo I 2024-03-22 15:19:31 token_classification_model:161] 
    label                                                precision    recall       f1           support   
    O (label_id: 0)                                         77.87      55.56      64.85        171
    B-DIS (label_id: 1)                                      2.65      31.25       4.88         16
    I-DIS (label_id: 2)                                     13.33       1.44       2.60        139
    -------------------
    micro avg                                               31.29      31.29      31.29        326
    macro avg                                               31.28      29.41      24.11        326
    weighted avg                                            46.66      31.29      35.36        326
    
[NeMo W 2024-03-22 15:19:32 nemo_logging:349] /usr/local/lib/python3.10/dist-packages/pytorch_lightning/trainer/connectors/data_connector.py:438: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 256 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.
      rank_zero_warn(
    
Epoch 1: 33%
117250/356647 [4:12:20<8:35:13, 7.74it/s, v_num=2-07, lr=4.08e-5, train_step_timing in s=0.104, val_loss=0.00559]
[NeMo I 2024-03-22 15:19:32 preemption:56] Preemption requires torch distributed to be initialized, disabling preemption
[NeMo I 2024-03-23 04:23:00 token_classification_model:161] 
    label                                                precision    recall       f1           support   
    O (label_id: 0)                                         99.77      99.82      99.80    6081509
    B-DIS (label_id: 1)                                     98.21      97.71      97.96     608465
    I-DIS (label_id: 2)                                    100.00     100.00     100.00    3735127
    -------------------
    micro avg                                               99.76      99.76      99.76   10425101
    macro avg                                               99.33      99.18      99.25   10425101
    weighted avg                                            99.76      99.76      99.76   10425101
``` 


Could you please advise on how to convert the checkpoint to the NeMo format?

And

When running a full epoch, will it automatically save the model with the lowest loss or the model of the last epoch?