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I am following the tutorial of asr+diariation under examples/speaker_tasks/diarization.
I want to use diar_msdd_telephonic model so I have to add more parameters like this:
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The current behavior is that when `ckpt_save_optimizer=True`, only the sharded optimizer states are included in the checkpoint dict. However when `ckpt_save_optimizer=False`, the unshard... | {
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I'm getting this error when trying to fine-tune the Parakeet model using the `/examples/asr/speech_to_text_finetune.py` file.
```python
Exception has occurred: TypeError
`model` must be a `LightningModule` or `torch._dynamo.OptimizedModule`, got `EncDecCTCModelBPE`
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Hf -> HF
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**Collection**: [Note which collection this PR will affect]
# Changelog
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Update wfst_text_normalization doc to reflect coverage of Hindi TN/ITN
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**Collection**: [Note which collection this PR will affect]
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- update timestamps check based on override_cfg value
# GitHub Actions CI
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Rewrite the error handling strategy when model does not have tokenizer.
Previously the bare try-except block would mask out actual errors such as wrong model path.
**Collection**: [Note which collection this PR will affect]
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} | null | null | null | Hello everyone and Nvidia folks,
We are trying to finetune llama3_1_8b_instruct. (70b as well after 8b)
Docker pull Nvidia NeMo docker image 24.09 and run inside a container.
Using this notebook from Brev (now part of Nvidia) https://github.com/brevdev/notebooks/blob/main/llama31_law.ipynb
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📝 Please remember the following to-do's before merge:
- [ ] Verify the presubmit CI
🙏 Please merge this PR only if the CI workflow completed successfully. | {
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
# Changelog
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# Usage
* You can potentially add a usage example below
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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
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I am trying to fine-tune a Parakeet 110M TDT-CTC model, but with a different tokenizer with 1024 tokens. I get the following error when i do `asr_model.maybe_init_from_pretrained_checkpoint(cfg)`, from which I understand that the huggingface model's TDT decoder has 1030 tokens? From the model card I understood ... | {
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I have trained a llama-like model with nemo using the below model config:
```
model:
mcore_gpt: True
micro_batch_size: 1
global_batch_size: 512
tensor_model_parallel_size: 1
pipeline_model_parallel_size: 1
virtual_pipeline_model_parallel_size: null
context_parallel_size: 1
encoder_... | {
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"This issue was closed because it has been inactive for 7 days since being marked as stale."
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I was trying to run the following lines:
```
%%bash
# Set paths to the model, train, validation and test sets.
MODEL="./llama-3_1-8b-instruct-nemo_v1.0/llama3_1_8b_instruct.nemo"
TRAIN_DS="[./curated-data/law-qa-train_preprocessed.jsonl]"
VALID_DS="[./curated-data/law-qa-val_preprocessed.jsonl... | {
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Add a one line overview of what this PR aims to accomplish.
**Collection**: [Note which collection this PR will affect]
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# Usage
* You can potentially add a usage example below
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Fixes the bug with TE linear layer replacement | {
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📝 Please remember the following to-do's before merge:
- [ ] Verify the presubmit CI
🙏 Please merge this PR only if the CI workflow completed successfully. | {
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"The `Isort and ...` issues are apparently coming up because the source branch is in an... | 2024-11-21T00:18:23 | 2024-11-27T11:07:55 | 2024-11-26T10:24:42 | CONTRIBUTOR | {
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} | The SingleDeviceStrategy never sets the requisite attribute; workaround it in the profiling code.
# What does this PR do ?
Fixes using the nsys callback for single-GPU benchmarking.
**Collection**: core
# Changelog
- Fix for cuda profiling of single-GPU runs
# Usage
```python
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"Ok docker quietened following error log from pip, this is an apache-beam issue, closing issue.\n\n> ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n> cudf 24.4.0 requires pyarrow<15.0.0a0... | 2024-11-20T20:58:34 | 2024-12-29T02:04:11 | 2024-12-29T02:04:10 | NONE | {
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HuggingFace datasets imported by nemo.collections.llm.gpt.data in /opt/NeMo/nemo/collections/llm/gpt/data/__init__.py:15
`from nemo.collections.llm.gpt.data.dolly import DollyDataModule`
/opt/NeMo/nemo/collections/llm/gpt/data/dolly.py:20
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Improves docs and fixes tool to convert from a NeMo/Megatron DCP checkpoint to a HF checkpoint
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# Changelog
- Added cmd launch to convert from NeMo/Megatron DCP checkpoint to .nemo
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```
model = MegatronAutoModel("meta-llama/Llama-3.1-8B")
print(model)
> LlamaModel()
```
**Collection**: [Note which collection this PR will affect]
# Changelog
- Add specific line by line info of high level changes in this PR.
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* You can potentially add a usage exampl... | {
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HfAutoModelForCausalLM's forward used input named `tokens` but the underlying model was using `input_ids` coincidenting with mcore's `input_ids`. -> renamed param to `input_ids` to have a unified interface.
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} | # What does this PR do ?
Fix CLIP transformer layer api for a ci failure
**Collection**: [multimodal]
# Changelog
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# Usage
* You can potentially add a usage example below
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Sortformer Diarizer Model, 4 speaker limit, v1 model.
**Previous PR:**
This is PR 2nd out of 3.
1st PR: https://github.com/NVIDIA/NeMo/pull/11336
3rd PR: <Coming Soon>
**Sortformer Paper:**
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} | # What does this PR do ?
Adding AutoModelForImageTextToText and PeFT example for instruct models.
Tested with:
- Llama-3.2-11B-Vision-Instruct
- Qwen/Qwen2-VL-2B-Instruct
**Collection**: [Note which collection this PR will affect]
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I gone through the repo, and i ran the inference for speaker diarization on toy sample using system VAD, its giving the output. But for the recorded audio file its giving errors. can you provide the code for custom samples inference for system VAD. | {
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📝 Please remember the following to-do's before merge:
- [ ] Verify the presubmit CI
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TensorRT-LLM only accepts a single rank .nemo LoRA checkpoint (in the case of Llama 3.1 8b). Therefore, the only way to use my fine-tuned model with TensorRT-LLM backend is to merge my distributed LoRA checkpoints into the base model using the scripts/... | {
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**Collection**: [Note which collection this PR will affect]
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There was a crash when nvidia_torch_version came back as None because it was still being used before the None check.
**Collection**: [NLP]
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Fixed crash when NVIDIA_PYTORCH_VERSION is not set
# Usage
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Interface for asymmetric pipeline schedule, where the first and the last Transformer layers use a dummy layer so they have only embedding and logit+loss functions.
# Changelog
- Add `standalone_embedding_stage` and `standalone_loss_stage` to enable dummy Transformer layer in the first a... | {
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Fix broken links
# 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 doc... | {
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Add openai-gelu in gated activation for TRTLLM export
**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 snip... | {
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