awq example runs into error with llama 3.2 3b due to embedding layer

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**Describe the bug**
When I run the example from examples/python/awq-quantized-model.md, but switching out phi-3 for llama-3.2-3b, I get an error message stating that `AttributeError: 'NoneType' object has no attribute 'detach'`. However, when I use the extra_option `exclude_embeds=true`, the onnx conversion step runs successfully.

**To Reproduce**
Steps to reproduce the behavior:
1. Follow the example from examples/python/awq-quantized-model.md, but switching out for `model_name = "meta-llama/Llama-3.2-3B-Instruct"`
2. At the onnx conversion step (after the quantization is complete), observe the error.

**Expected behavior**
The conversion to onnx should occur successfully, with no errors.

**Screenshots**
```
(base) C:\Users\Tranl\Documents>python -m onnxruntime_genai.models.builder -i C:\Users\Tranl\Documents\Llama-3.2-3B-Instruct-quant -o C:\Users\Tranl\Documents\Llama-3.2-3B-Instruct-onnx -p int4 -e dml -c ..\cache_dir
Valid precision + execution provider combinations are: FP32 CPU, FP32 CUDA, FP16 CUDA, FP16 DML, INT4 CPU, INT4 CUDA, INT4 DML
GroupQueryAttention (GQA) is used in this model.
Unpacking and repacking layer 0
Unpacking and repacking layer 1
Unpacking and repacking layer 2
Unpacking and repacking layer 3
Unpacking and repacking layer 4
Unpacking and repacking layer 5
Unpacking and repacking layer 6
Unpacking and repacking layer 7
Unpacking and repacking layer 8
Unpacking and repacking layer 9
Unpacking and repacking layer 10
Unpacking and repacking layer 11
Unpacking and repacking layer 12
Unpacking and repacking layer 13
Unpacking and repacking layer 14
Unpacking and repacking layer 15
Unpacking and repacking layer 16
Unpacking and repacking layer 17
Unpacking and repacking layer 18
Unpacking and repacking layer 19
Unpacking and repacking layer 20
Unpacking and repacking layer 21
Unpacking and repacking layer 22
Unpacking and repacking layer 23
Unpacking and repacking layer 24
Unpacking and repacking layer 25
Unpacking and repacking layer 26
Unpacking and repacking layer 27
Reading embedding layer
Traceback (most recent call last):
File "", line 198, in _run_module_as_main
File "", line 88, in _run_code
File "C:\Users\Tranl\AppData\Roaming\Python\Python312\site-packages\onnxruntime_genai\models\builder.py", line 3267, in
create_model(args.model_name, args.input, args.output, args.precision, args.execution_provider, args.cache_dir, **extra_options)
File "C:\Users\Tranl\AppData\Roaming\Python\Python312\site-packages\onnxruntime_genai\models\builder.py", line 3151, in create_model
onnx_model.make_model(input_path)
File "C:\Users\Tranl\AppData\Roaming\Python\Python312\site-packages\onnxruntime_genai\models\builder.py", line 2058, in make_model
self.make_embedding(module.weight.detach().numpy())
^^^^^^^^^^^^^^^^^^^^
AttributeError: 'NoneType' object has no attribute 'detach'
```

**Desktop (please complete the following information):**
- OS: Windows
- Version 11 Pro
- Anaconda3-2024.10-1-Windows-x86_64
- torch 2.5.1+cu124
- onnxruntime-genai-directml 0.5.1

**Additional context**
I've manually tried loading the awq quantized model and it looks fine. I can see the embeddings and grab them by attribute as well. Here is the output when I exclude embeddings:
```
(base) C:\Users\Tranl\Documents>python -m onnxruntime_genai.models.builder -i C:\Users\Tranl\Documents\Llama-3.2-3B-Instruct-quant -o C:\Users\Tranl\Documents\Llama-3.2-3B-Instruct-onnx -p int4 -e dml -c ..\cache_dir --extra_options exclude_embeds=true
Valid precision + execution provider combinations are: FP32 CPU, FP32 CUDA, FP16 CUDA, FP16 DML, INT4 CPU, INT4 CUDA, INT4 DML
Extra options: {'exclude_embeds': 'true'}
GroupQueryAttention (GQA) is used in this model.
Unpacking and repacking layer 0
Unpacking and repacking layer 1
Unpacking and repacking layer 2
Unpacking and repacking layer 3
Unpacking and repacking layer 4
Unpacking and repacking layer 5
Unpacking and repacking layer 6
Unpacking and repacking layer 7
Unpacking and repacking layer 8
Unpacking and repacking layer 9
Unpacking and repacking layer 10
Unpacking and repacking layer 11
Unpacking and repacking layer 12
Unpacking and repacking layer 13
Unpacking and repacking layer 14
Unpacking and repacking layer 15
Unpacking and repacking layer 16
Unpacking and repacking layer 17
Unpacking and repacking layer 18
Unpacking and repacking layer 19
Unpacking and repacking layer 20
Unpacking and repacking layer 21
Unpacking and repacking layer 22
Unpacking and repacking layer 23
Unpacking and repacking layer 24
Unpacking and repacking layer 25
Unpacking and repacking layer 26
Unpacking and repacking layer 27
Reading decoder layer 0
Reading decoder layer 1
Reading decoder layer 2
Reading decoder layer 3
Reading decoder layer 4
Reading decoder layer 5
Reading decoder layer 6
Reading decoder layer 7
Reading decoder layer 8
Reading decoder layer 9
Reading decoder layer 10
Reading decoder layer 11
Reading decoder layer 12
Reading decoder layer 13
Reading decoder layer 14
Reading decoder layer 15
Reading decoder layer 16
Reading decoder layer 17
Reading decoder layer 18
Reading decoder layer 19
Reading decoder layer 20
Reading decoder layer 21
Reading decoder layer 22
Reading decoder layer 23
Reading decoder layer 24
Reading decoder layer 25
Reading decoder layer 26
Reading decoder layer 27
Reading final norm
Reading LM head
Saving ONNX model in C:\Users\Tranl\Documents\Llama-3.2-3B-Instruct-onnx
2024-11-21 21:16:27,418 onnxruntime.quantization.matmul_4bits_quantizer [INFO] - skip to quantize /model/constant_nodes/TensorProto.INT64/1D/1 ...
<...etc>
<...etc>
<...etc>
2024-11-21 21:16:27,441 onnxruntime.quantization.matmul_4bits_quantizer [INFO] - skip to quantize /model/layers.28/final_norm_layernorm/SkipLayerNorm ...
2024-11-21 21:16:27,441 onnxruntime.quantization.matmul_4bits_quantizer [INFO] - start to quantize /lm_head/MatMul ...
2024-11-21 21:16:28,155 onnxruntime.quantization.matmul_4bits_quantizer [INFO] - complete quantization of /lm_head/MatMul ...
Saving GenAI config in C:\Users\Tranl\Documents\Llama-3.2-3B-Instruct-onnx
Saving processing files in C:\Users\Tranl\Documents\Llama-3.2-3B-Instruct-onnx for GenAI
```

Start with examples/python/awq-quantized-model.md and reproduce the Llama-3.2-3B conversion without exclude_embeds. Then inspect builder.py around onnx_model.make_model, make_embedding, and the Reading embedding layer path shown in the traceback. Done means the conversion completes successfully without requiring exclude_embeds.

Written by the indexing model from the issue text.