[Doc] Add documentation for specifying model architecture (#12105)

Author: DarkLight1337

docs/source/serving/offline_inference.md

@@ -31,6 +31,59 @@ Please refer to the above pages for more details about each API.
 This section lists the most common options for running the vLLM engine.
 For a full list, refer to the [Engine Arguments](#engine-args) page.
 
+### Model resolution
+
+vLLM loads HuggingFace-compatible models by inspecting the `architectures` field in `config.json` of the model repository
+and finding the corresponding implementation that is registered to vLLM.
+Nevertheless, our model resolution may fail for the following reasons:
+
+- The `config.json` of the model repository lacks the `architectures` field.
+- Unofficial repositories refer to a model using alternative names which are not recorded in vLLM.
+- The same architecture name is used for multiple models, creating ambiguity as to which model should be loaded.
+
+In those cases, vLLM may throw an error like:
+
+```text
+Traceback (most recent call last):
+...
+  File "vllm/model_executor/models/registry.py", line xxx, in inspect_model_cls
+    for arch in architectures:
+TypeError: 'NoneType' object is not iterable
+```
+
+or:
+
+```text
+  File "vllm/model_executor/models/registry.py", line xxx, in _raise_for_unsupported
+    raise ValueError(
+ValueError: Model architectures ['<arch>'] are not supported for now. Supported architectures: [...]
+```
+
+:::{note}
+The above error is distinct from the following similar but different error:
+
+```text
+  File "vllm/model_executor/models/registry.py", line xxx, in _raise_for_unsupported
+    raise ValueError(
+ValueError: Model architectures ['<arch>'] failed to be inspected. Please check the logs for more details.
+```
+
+This error means that vLLM failed to import the model file. Usually, it is related to missing dependencies or outdated
+binaries in the vLLM build. Please read the logs carefully to determine the real cause of the error.
+:::
+
+To fix this, explicitly specify the model architecture by passing `config.json` overrides to the `hf_overrides` option.
+For example:
+
+```python
+model = LLM(
+    model="cerebras/Cerebras-GPT-1.3B",
+    hf_overrides={"architectures": ["GPT2LMHeadModel"]},  # GPT-2
+)
+```
+
+Our [list of supported models](#supported-models) shows the model architectures that are recognized by vLLM.
+
 ### Reducing memory usage
 
 Large models might cause your machine to run out of memory (OOM). Here are some options that help alleviate this problem.