renwei2024/nemo-github-issues huggingface dataset:
fix tokenizer restoration
State: closed | Pull request: yes
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Bugfix: export to trt-llm multi_block_mode flag
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?

Change flags in `export_to_trt_llm.py` according to [Update TRTLLM 0.12 #10215](https://github.com/NVIDIA/NeMo/pull/10215).

**Collection**: nlp

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# Additional Information
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Error in converting LLaMA3.1 nemo checkpoint into HF
State: closed | Pull request: no

The same as #10013 but in the other direction, that is, from `.nemo` to HF. It happens even in the 24.07 container.

**Describe the bug**

I'm trying to convert LLama3.1-8b nemo checkpoint into huggingface checkpoint using convert_llama_nemo_to_hf.py script. It's giving ``ValueError: `rope_scaling` must be a dictionary with two fields, `type` and `factor`, got {'factor': 8.0, 'low_freq_factor': 1.0, 'high_freq_factor': 4.0, 'original_max_position_embeddings': 8192, 'rope_type': 'llama3'}``

Find the complete trace below :

``` [rank0]: Traceback (most recent call last):
[rank0]:   File "/opt/NeMo/scripts/checkpoint_converters/convert_llama_nemo_to_hf.py", line 257, in <module>
[rank0]:     replace_hf_weights_and_tokenizer(
[rank0]:   File "/opt/NeMo/scripts/checkpoint_converters/convert_llama_nemo_to_hf.py", line 231, in replace_hf_weights_and_tokenizer
[rank0]:     model = AutoModelForCausalLM.from_pretrained(input_hf_path, local_files_only=True, torch_dtype=dtype,)
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/auto_factory.py", line 523, in from_pretrained
[rank0]:     config, kwargs = AutoConfig.from_pretrained(
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/configuration_auto.py", line 952, in from_pretrained
[rank0]:     return config_class.from_dict(config_dict, **unused_kwargs)
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/transformers/configuration_utils.py", line 761, in from_dict
[rank0]:     config = cls(**config_dict)
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/transformers/models/llama/configuration_llama.py", line 161, in __init__
[rank0]:     self._rope_scaling_validation()
[rank0]:   File "/usr/local/lib/python3.10/dist-packages/transformers/models/llama/configuration_llama.py", line 181, in 
_rope_scaling_validation
[rank0]:     raise ValueError(
[rank0]: ValueError: `rope_scaling` must be a dictionary with two fields, `type` and `factor`, got 
{'factor': 8.0, 'low_freq_factor': 1.0, 'high_freq_factor': 4.0, 'original_max_position_embeddings': 8192, 'rope_type': 'llama3'}
```

**Steps/Code to reproduce bug**

`python <path_to_NeMo>/scripts/checkpoint_converters/convert_llama_hf_to_nemo.py --<arguments>`

**Expected behavior**

Successful conversion of llama3.1-8b nemo checkpoints into hf checkpoint.

**Environment overview**

I'm using nvcr.io/nvidia/nemo:24.07 container (have tried 24.05.llama3.1 as well).


Sorry, I have not tried with the newest dev container. I will give it a try and I will close the issue if it gets resolved.

Yeah it works in the newest container.
nemo-ux: optim & model state restore test
State: closed | Pull request: yes
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Fix async checkpointing in nemo.lightning
State: closed | Pull request: yes
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can we add some test for async ckpt and make sure unfinished checkpoints are not present once finished?

> can we add some test for async ckpt and make sure unfinished checkpoints are not present once finished?

Yeah for sure, but I would like to address it in a separate PR since there's no easy way to add this test as of now.
Improve TE import guards
State: closed | Pull request: yes
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name: NLP

name: Multi Modal

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Add comment to address a frequently asked question
State: closed | Pull request: yes
Labels: name: NLP

name: Run CICD

# What does this PR do ?

Add comment to explain a mysterious minus 1 in sequence packing code.

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Add option to not generate attention mask for `MockGPTDataset`
State: closed | Pull request: yes
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# What does this PR do ?

Add option to not generate attention mask for `MockGPTDataset`. It leads to dataloader issue when sequence length is long, e.g. 64k.
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Closed in favor of https://github.com/NVIDIA/NeMo/pull/10242
Add NEST SSL to main
State: closed | Pull request: yes
Labels: name: core

name: ASR

name: Speaker Tasks

name: common

name: Multi Modal

name: Run CICD

# What does this PR do ?

Add NEST SSL to main

# Change Log

- Add NEST model, dataset and related modules.
- Add support for assigning dataset names via setting `validation_ds.name=[val1,val2]` in hydra (`model_utils.py`).
- Add support for validating with speaker verification metric (EER) in `label_models.py`, `audio_to_label.py`
- misc fixes and improvements

**Collection**: [asr]


Will model weights be open after merge?

> Will model weights be open after merge?

@sphmel Yes we will opensource the checkpoints as well, after PR is merged

@stevehuang52 PR is merged! Are checkpoints, and any usage guide available now? I can not find in docs, and this repo

Hi @sphmel, the docs and usage guide will be added by another [PR](https://github.com/NVIDIA/NeMo/pull/10804) (work in progress). As for checkpoint release, I'm sorry that some LibriLight audios used in training NEST are no longer available for public usage, and we have to re-train the model without those audios. I'll keep you posted as soon as we get new checkpoints. Thanks for your patience~!

@stevehuang52 Is there any update on checkpoint release? 

@sphmel the checkpoints are ready, just need some legal review process before release, I'll finish them this month.

@sphmel Sorry for the delay, the checkpoints are available on HuggingFace:
- https://huggingface.co/nvidia/ssl_en_nest_large_v1.0
- https://huggingface.co/nvidia/ssl_en_nest_xlarge_v1.0

@stevehuang52 Thank you! I really expect NEST's multilingual performance. Does new model perform as reported in paper? Also, how do you think about its performance on asian language?

@sphmel The performance should be very similar to those presented in the paper, when using the exact same setting. I didn't check all tasks, but on those that I checked the difference is within 0.02. We are still training the multilingual models and will release them later~
Make get_optim_config iterable
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Do you think we should change https://github.com/NVIDIA/NeMo/blob/main/nemo/lightning/pytorch/optim/megatron.py as well? Add a wrapper for the ChainedOptimizer

@BoxiangW  McoreDistributedOptimizer is a NeMo class not an Mcore class, and will hold whatever optimizer mcore returns as an attribute, would there be an issue in this scenario?
Remove virtual pipeline parallel Apex dependency
State: closed | Pull request: yes
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# What does this PR do ?

Removes dependency on Apex when using virtual pipeline parallel

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move to cpu only for log probs
State: closed | Pull request: yes
Labels: name: ASR

name: Run CICD

# What does this PR do ?

Fixes Device assertion error with transducer models when using partial_transcribe_audio 

**Collection**: ASR

# Changelog 
- move cpu only for logprobs case. 

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Flexible passing args to TensorRTLLM in nemo_export.py
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?

Enabling user to pass arguments in [nemo_export.py](https://github.com/NVIDIA/NeMo/blob/main/tests/export/nemo_export.py) to `TensorRT-LLM.export` as the build API is constantly changing and besides some parameters needs to be set on per-model basis.

**Collection**: NLP

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage

```python
python tests/export/nemo_export.py \
    --model_name=$MODEL_NAME \
    ...
    --trt_llm_export_kwargs='{"reduce_fusion": false, "gemm_plugin": "auto"}'
```
Default value or specyfing `--trt_llm_export_kwargs='{}'` results in empty kwargs.

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# Additional Information
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Support TE-DPA For Stable Diffusion
State: closed | Pull request: yes
Labels: name: Multi Modal

# What does this PR do ?

The same changes as https://github.com/NVIDIA/NeMo/pull/10288, but different target branch.

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- Add specific line by line info of high level changes in this PR.

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ERROR: Could not find a version that satisfies the requirement triton (from nemo-toolkit) (from versions: none)
State: closed | Pull request: no

Hi, I am trying to use canary-1b. As following the installing steps, I installed nemo and nemo-toolkit successfully. But when I try to import EncDecMultiTaskModel, it shows cannot find reference "EncDecMultiTaskModel" in "__init__.py". Then I printed the packages version, it shows the nemo version is 6.0.3 and nemo-toolkit version is 1.21.0. Therefore, I tried upgrading nemo-toolkit, but after I used "pip install nemo-toolkit==1.23.0", it shows 

"ERROR: Could not find a version that satisfies the requirement triton (from nemo-toolkit) (from versions: none)
ERROR: No matching distribution found for triton
"

Could you please help me to figure it out what happened and what should I do to deal with this problem? Thank you sooooo much!!

OMG I solved this. I installed triton again ([followed this answer](https://github.com/triton-lang/triton/issues/1057#issuecomment-2211657873)) then tried "pip install nemo-toolkit==1.23.0" again. I'm so sorry for bothering you for such a stupid question (T__T  ).
[TTS] Voicebox for Speech Editing and Zero-Shot TTS
State: closed | Pull request: yes
Labels: name: TTS

name: stale

# What does this PR do ?

- Voicebox implementation & training
- Speech editing & zero-shot TTS with Voicebox
- Evaluate RealEdit generated by Voicebox

**Collection**: tts

# Changelog 
- Add voicebox model in `nemo/collections/tts/models/voicebox.py`
- Create `scripts/voicebox_edit/test_edit.py` for speech editing & zero-shot TTS
- Create `tutorials/tts/Voicebox_MFA.ipynb` for training data preparation tutorial with Montreal Forced Aligner
- Create `tutorials/tts/Voicebox_Training.ipynb` for training usage tutorial
- Create `tutorials/tts/Voicebox_Edit.ipynb` for speech editing & zero-shot TTS usage tutorial

# Usage
Checkout the following files:
- `tutorials/tts/Voicebox_MFA.ipynb` for training data preparation tutorial with Montreal Forced Aligner
- `tutorials/tts/Voicebox_Training.ipynb` for training usage tutorial
- `tutorials/tts/Voicebox_Edit.ipynb` for speech editing & zero-shot TTS usage tutorial

# GitHub Actions CI

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# Before your PR is "Ready for review"
**Pre checks**:
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## Who can review?

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# Additional Information
* Related to # (issue)


This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.

This PR was closed because it has been inactive for 7 days since being marked as stale.

This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.

This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.

This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.

This PR was closed because it has been inactive for 7 days since being marked as stale.
SFT training getting nan loss when using PP=4, TP=4 and model params > 7b
State: closed | Pull request: no
Labels: name: bug

name: stale

**Describe the bug**
I am trying to finetune the OPT-13b model and getting nan loss at step=4 with following configuration.
PP=4
TP=4
MBS=4
Batch size=128

Running model less than or equal to 7b parameters with above configuration works fine.

What could be the issue and How can I fix it?


A clear and concise description of what the bug is.

**Steps/Code to reproduce bug**
Following llama SFT training guide. 
https://docs.nvidia.com/nemo-framework/user-guide/latest/playbooks/llama2sft.html


Please list *minimal* steps or code snippet for us to be able to reproduce the bug.

A  helpful guide on on how to craft a minimal bug report  http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports. 


**Expected behavior**

A clear and concise description of what you expected to happen.

**Environment overview (please complete the following information)**

 - Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)] Docker nemo:24.03.01
 - Method of NeMo install: [pip install or from source]. Please specify exact commands you used to install.
 - If method of install is [Docker], provide `docker pull` & `docker run` commands used

**Environment details**

If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version
- PyTorch version
- Python version

**Additional context**

Add any other context about the problem here.
Example: GPU model


Seeming this repo is not very active, 
My case is Llama 3 8b Instruct
I'm just change the learning rate to at small as possible (e.g 1e-8) and set the global batch size to 256 in-order to help the training progress stable.
Hope this will help.

This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days.

This issue was closed because it has been inactive for 7 days since being marked as stale.
Akoumparouli/nemo ux validate dataset asset accessibility
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?

Add a one line overview of what this PR aims to accomplish.

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# GitHub Actions CI

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To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".

# Before your PR is "Ready for review"
**Pre checks**:
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  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
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## Who can review?

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# Additional Information
* Related to # (issue)
add back HF Finetune script to CI
State: closed | Pull request: yes
Labels: name: ASR

name: CI

name: Run CICD

# What does this PR do ?

Add required `trust_remote_code` by default for HF finetune config

**Collection**: ASR

# Changelog 
- enable `trust_remote_code` by default for HF Datasets


# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [x] Bugfix
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# Additional Information
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Fix llama3 pretraining NeMo 2.0 script
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?

Add a one line overview of what this PR aims to accomplish.

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# GitHub Actions CI

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# Before your PR is "Ready for review"
**Pre checks**:
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  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
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# Additional Information
* Related to # (issue)
Support Energon as dataloader in NeVA
State: closed | Pull request: yes
Labels: name: NLP

name: Multi Modal

name: Run CICD

# What does this PR do ?
This PR adds experimental support for utilizing [Megatron-Energon](https://github.com/NVIDIA/Megatron-Energon) as a dataloader for NeVA training.

Co-authored:
@xuanzic 

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# GitHub Actions CI

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To re-run CI remove and add the label again.
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# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation

If you haven't finished some of the above items you can still open "Draft" PR.


## Who can review?

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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.

# Additional Information
* Related to # (issue)
hangman prototype
State: closed | Pull request: yes
Labels: name: stale

# What does this PR do ?

Add a one line overview of what this PR aims to accomplish.

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# GitHub Actions CI

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The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".

# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation

If you haven't finished some of the above items you can still open "Draft" PR.


## Who can review?

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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.

# Additional Information
* Related to # (issue)


This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.

closing this, maybe revisit in the future.
remove grad clipping from mixed_precision plugin
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?

Add a one line overview of what this PR aims to accomplish.

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# GitHub Actions CI

The Jenkins CI system has been replaced by GitHub Actions self-hosted runners.

The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".

# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation

If you haven't finished some of the above items you can still open "Draft" PR.


## Who can review?

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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.

# Additional Information
* Related to # (issue)
change condition to context_style
State: closed | Pull request: yes
Labels: name: stale

name: ASR

name: Run CICD

# What does this PR do ?

Change condition to check for cache-aware encoder

**Collection**: ASR

# Changelog 
- Check for context style 


# GitHub Actions CI

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# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
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  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
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- [ ] Documentation

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[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.

# Additional Information
* Related to # (issue)


This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days.

This PR was closed because it has been inactive for 7 days since being marked as stale.
TRT-LLM 0.12 + ModelOpt 0.17.0 updates
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?

Updating PTQ workflow for the latest stack.

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# GitHub Actions CI

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The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
To run CI on an untrusted fork, a NeMo user with write access must first click "Approve and run".

# Before your PR is "Ready for review"
**Pre checks**:
- [x] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [x] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
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  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
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## Who can review?

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# Additional Information
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[NeMo-UX] Integrating CLI
State: closed | Pull request: yes
Labels: name: Run CICD

# What does this PR do ?

Add a one line overview of what this PR aims to accomplish.

**Collection**: [Note which collection this PR will affect]

# Changelog 
- Add specific line by line info of high level changes in this PR.

# Usage
* You can potentially add a usage example below

```python
# Add a code snippet demonstrating how to use this 
```

# GitHub Actions CI

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The GitHub Actions CI will run automatically when the "Run CICD" label is added to the PR.
To re-run CI remove and add the label again.
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# Before your PR is "Ready for review"
**Pre checks**:
- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md)
- [ ] Did you write any new necessary tests?
- [ ] Did you add or update any necessary documentation?
- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
  - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
  
**PR Type**:
- [ ] New Feature
- [ ] Bugfix
- [ ] Documentation

If you haven't finished some of the above items you can still open "Draft" PR.


## Who can review?

Anyone in the community is free to review the PR once the checks have passed. 
[Contributor guidelines](https://github.com/NVIDIA/NeMo/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.

# Additional Information
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