Remove quantization_config in config.json from original deepseek models (#753)

## What does this PR do?

**Type of change:**  Bug fix

**Overview:** DeepSeek original checkpoints may include a
`quantization_config` field in `config.json`
(describing the source checkpoint's quantization). When we export
ModelOpt quantization
configs to `hf_quant_config.json`, leaving the original
`quantization_config` in place can
    be confusing. Add a function to remove it.

## Usage
<!-- You can potentially add a usage example below. -->

```python
# Add a code snippet demonstrating how to use this
```

## Testing
<!-- Mention how have you tested your change if applicable. -->

## Before your PR is "*Ready for review*"
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`Draft` PR. -->

- **Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/Model-Optimizer/blob/main/CONTRIBUTING.md)**
and your commits are signed.
- **Is this change backward compatible?**: Yes <!--- If No, explain why.
-->
- **Did you write any new necessary tests?**: Yes/No
- **Did you add or update any necessary documentation?**: Yes/No
- **Did you update
[Changelog](https://github.com/NVIDIA/Model-Optimizer/blob/main/CHANGELOG.rst)?**:
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bw breaking changes. -->

## Additional Information
Resolve nvbug https://nvbugspro.nvidia.com/bug/5736665

---------

Signed-off-by: Zhiyu Cheng <zhiyuc@nvidia.com>

Author: Edwardf0t1

examples/deepseek/quantize_to_nvfp4.py

@@ -82,6 +82,20 @@ def _remap_key(key_dict: dict[str, Any]):
     key_dict.update(new_dict)
 
 
+def remove_quantization_config_from_original_config(export_dir: str) -> None:
+    """Remove `quantization_config` from exported HF `config.json`.
+
+    Assumes the exported checkpoint directory has a `config.json` containing `quantization_config`.
+    """
+    config_path = os.path.join(export_dir, "config.json")
+    with open(config_path) as f:
+        cfg = json.load(f)
+    del cfg["quantization_config"]
+    with open(config_path, "w") as f:
+        json.dump(cfg, f, indent=2, sort_keys=True)
+        f.write("\n")
+
+
 def load_and_preprocess_state_dict(modelopt_state_root, world_size=8):
     state_dict_list = [
         torch.load(f"{modelopt_state_root}/amax_dict_rank{rank}-mp{world_size}.pt")
@@ -302,3 +316,5 @@ def get_tensor(tensor_name):
         save_root=args.fp4_path,
         per_layer_quant_config=per_layer_quant_config,
     )
+
+    remove_quantization_config_from_original_config(args.fp4_path)