[Feature][Quant] Reapply auto-detect quantization format and support remote model ID (#7111)

### What this PR does / why we need it?
Reapply the auto-detect quantization format feature (originally in
#6645, reverted in #6873) and extend it to support remote model
identifiers (e.g., `org/model-name`).

Changes:
- Reapply auto-detection of quantization method from model files
(`quant_model_description.json` for ModelSlim, `config.json` for
compressed-tensors)
- Add `get_model_file()` utility to handle file retrieval from both
local paths and remote repos (HuggingFace Hub / ModelScope)
- Update `detect_quantization_method()` to accept remote repo IDs with
optional `revision` parameter
- Update `maybe_update_config()` to work with remote model identifiers
- Add platform-level `auto_detect_quantization` support
- Add unit tests and e2e tests for both local and remote model ID
scenarios

Closes #6836

### Does this PR introduce _any_ user-facing change?

Yes. When `--quantization` is not explicitly specified, vllm-ascend will
now automatically detect the quantization format from the model files
for both local directories and remote model IDs.

- vLLM version: v0.16.0
- vLLM main:
4034c3d32e
---------
Signed-off-by: SlightwindSec <slightwindsec@gmail.com>

tests/e2e/singlecard/test_quantization.py

@@ -49,6 +49,43 @@ def test_qwen3_w8a8_quant():
         name_1="vllm_quant_w8a8_outputs",
     )
 
+# fmt: off
+def test_qwen3_w8a8_quant_auto_detect():
+    """Test that ModelSlim quantization is auto-detected without --quantization.
+
+    Uses the same W8A8 model as test_qwen3_w8a8_quant but omits the
+    quantization parameter, verifying that the auto-detection in
+    maybe_auto_detect_quantization() picks up quant_model_description.json
+    and produces identical results.
+    """
+    max_tokens = 5
+    example_prompts = [
+        "vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs."
+    ]
+    vllm_target_outputs = [([
+        85, 4086, 44, 374, 264, 1550, 42747, 628, 323, 4938, 72816, 44378, 323,
+        13480, 4712, 369, 444, 10994, 82, 13, 1084, 374, 6188, 311, 387
+    ], 'vLLM is a high-throughput and memory-efficient inference and serving engine for LLMs. It is designed to be'
+                            )]
+# fmt: on
+
+    with VllmRunner(
+            "vllm-ascend/Qwen3-0.6B-W8A8",
+            max_model_len=8192,
+            gpu_memory_utilization=0.7,
+            cudagraph_capture_sizes=[1, 2, 4, 8],
+    ) as vllm_model:
+        vllm_quant_auto_detect_outputs = vllm_model.generate_greedy(
+            example_prompts, max_tokens)
+
+    check_outputs_equal(
+        outputs_0_lst=vllm_target_outputs,
+        outputs_1_lst=vllm_quant_auto_detect_outputs,
+        name_0="vllm_target_outputs",
+        name_1="vllm_quant_auto_detect_outputs",
+    )
+
+
 # fmt: off
 def test_qwen3_dense_w8a16():
     max_tokens = 5