Drastic difference between .nemo and HF checkpoint
I have compared a bunch of things between the 2 models and looks like there is a large difference between the logits of the 2 models.

```
nemo_model = MegatronGPTModel.restore_from(
    nemo_path,
    trainer=dummy_trainer,
    override_config_path=model_config,
    map_location=map_location
)

# Load HuggingFace model
hf_model = AutoModelForCausalLM.from_pretrained(
    hf_path,
    local_files_only=True,
    torch_dtype=torch.bfloat16 # nemo_model.dtype
)

# Load tokenizer
tokenizer = LlamaTokenizer.from_pretrained(tokenizer_path, legacy=False)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.pad_token_id = tokenizer.eos_token_id

# Move models to device
nemo_model = nemo_model.to(device)
hf_model = hf_model.to(device)

# Set both models to eval mode
nemo_model.eval()
hf_model.eval()

# Create random input ids
input_ids = torch.randint(
    100, 1000,
    (test_batch_size, test_seq_length),
    device=device
)
attention_mask = torch.ones_like(input_ids)

with torch.no_grad():
    # NeMo forward pass
    nemo_output = nemo_model(
        tokens=input_ids,
        text_position_ids=torch.arange(test_seq_length, device=device),
        attention_mask=attention_mask,
        labels=None
    )
    
    # HF forward pass
    hf_output = hf_model(
        input_ids=input_ids,
        attention_mask=attention_mask,
        output_hidden_states=True,
        return_dict=True
    ).logits

# Compare logits
logits_match = torch.allclose(
    nemo_output,
    hf_output,
    rtol=rtol,
    atol=atol
)

metrics['logits_max_diff'] = float(
    torch.max(torch.abs(nemo_output - hf_output)).cpu()
)
```

Output:
```
Conversion test results:
Logits match: False (max diff: 4.91e+00)
  Parameters match: True (max diff: 0.00e+00)
  Generation match: 0.0
    Sample generation comparison:
      Input text: '<s>[INST] Hello [/INST]\n'
      NeMo output: "<s>[INST] Hello [/INST]\n Hello. It's nice to meet you. Is there something I can help you with or"
      HF output: '<s> [INST] Hello [/INST]\n Hello. ನಿಮ್ಮನ್ನ ಭೇಟಿ ಮಾಡಿ ಸಂತೋಷ ಆಯ್ತು. ನಿಮಗೆ ಏನ'
Number of parameters match: 1.0 (Nemo: 2525087744, HF: 2525087744)
❌ Conversion test failed!
```

I am not able to pinpoint why this is happening. Any pointers will be helpful. 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.
**Describe the bug**

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_seq_length: 8192
  max_position_embeddings: ${.encoder_seq_length}
  num_layers: 28
  hidden_size: 2048
  ffn_hidden_size: 11008
  num_attention_heads: 16
  init_method_std: 0.02
  use_scaled_init_method: True
  hidden_dropout: 0.0
  attention_dropout: 0.0
  ffn_dropout: 0.0
  kv_channels: null
  apply_query_key_layer_scaling: True
  normalization: 'rmsnorm'
  layernorm_epsilon: 1e-6
  do_layer_norm_weight_decay: False
  make_vocab_size_divisible_by: 128
  pre_process: True
  post_process: True
  persist_layer_norm: True
  bias: False
  activation: 'fast-swiglu'
  headscale: False
  transformer_block_type: 'pre_ln'
  openai_gelu: False
  normalize_attention_scores: True
  position_embedding_type: 'rope'
  rotary_percentage: 1.0
  attention_type: 'multihead'
  share_embeddings_and_output_weights: False
  overlap_p2p_comm: False
  batch_p2p_comm: True
  num_query_groups: 8
  rotary_base: 10000.0
```
The model works well when I run inference using the nemo checkpoint ([script](https://github.com/NVIDIA/NeMo/blob/main/scripts/deploy/nlp/query.py)). But the converted checkpoint ([script](https://github.com/NVIDIA/NeMo/blob/main/scripts/checkpoint_converters/convert_llama_nemo_to_hf.py)) drastically drops in performance. Any ideas why this might be happening? My only hunch is that `apply_query_key_layer_scaling=True` in nemo, which might not be the case in HF.

**Environment details**
https://docs.nvidia.com/nemo-framework/user-guide/latest/softwarecomponentversions.html#nemo-framework-24-05
