[BugFix] Fix quantizaiton_2of4_sparse_w4a16 example (#1565)

SUMMARY:
Pass saved model directly to next stage since now `train` and `oneshot`
can properly initialize models from path.


TEST PLAN:
Testing `test_quantization_2of4_sparse_w4a16` locally

```
collected 2 items                                                                                                                                    

tests/examples/test_quantization_2of4_sparse_w4a16.py::TestQuantization24SparseW4A16::test_doc_example_commPASSED
tests/examples/test_quantization_2of4_sparse_w4a16.py::TestQuantization24SparseW4A16::test_alternative_recipe PASSED

=========================================================== 2 passed in 6123.28s (1:42:03) ===========================================================
```

---------

Signed-off-by: shanjiaz <zsjwpianpian@gmail.com>
Co-authored-by: Dipika Sikka <dipikasikka1@gmail.com>

Author: shanjiaz

examples/quantization_2of4_sparse_w4a16/llama7b_sparse_w4a16.py

@@ -1,9 +1,10 @@
+from pathlib import Path
+
 import torch
 from loguru import logger
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 from llmcompressor import oneshot, train
-from llmcompressor.utils import dispatch_for_generation
 
 # load the model in as bfloat16 to save on memory and compute
 model_stub = "neuralmagic/Llama-2-7b-ultrachat200k"
@@ -18,6 +19,7 @@
 
 # save location of quantized model
 output_dir = "output_llama7b_2of4_w4a16_channel"
+output_path = Path(output_dir)
 
 # set dataset config parameters
 splits = {"calibration": "train_gen[:5%]", "train": "train_gen"}
@@ -63,25 +65,26 @@
 # ./output_llama7b_2of4_w4a16_channel/ + (finetuning/sparsity/quantization)_stage
 
 # Oneshot sparsification
-oneshot_applied_model = oneshot(
+
+oneshot(
     model=model,
     **oneshot_kwargs,
+    output_dir=output_dir,
     stage="sparsity_stage",
 )
 
 # Sparse finetune
-dispatch_for_generation(model)
-finetune_applied_model = train(
-    model=oneshot_applied_model,
+train(
+    model=(output_path / "sparsity_stage"),
     **oneshot_kwargs,
     **training_kwargs,
+    output_dir=output_dir,
     stage="finetuning_stage",
 )
 
 # Oneshot quantization
-model.to("cpu")
 quantized_model = oneshot(
-    model=finetune_applied_model,
+    model=(output_path / "finetuning_stage"),
     **oneshot_kwargs,
     stage="quantization_stage",
 )