update doc

sdks/python/apache_beam/ml/inference/huggingface_inference.py

@@ -571,7 +571,7 @@ class HuggingFacePipelineModelHandler(ModelHandler[str,
   def __init__(
       self,
       task: Union[str, PipelineTask] = "",
-      model=None,
+      model: str = "",
       *,
       inference_fn: PipelineInferenceFn = _default_pipeline_inference_fn,
       load_pipeline_args: Optional[Dict[str, Any]] = None,
@@ -594,11 +594,16 @@ def __init__(
     Args:
       task (str or enum.Enum): task supported by HuggingFace Pipelines.
         Accepts a string task or an enum.Enum from PipelineTask.
-      model : path to the pretrained model-id on Hugging Face Models Hub
-        to use custom model for the chosen task. If the model already defines
-        the task then no need to specify the task parameter. Use the model_id
-        string instead of an actual model here. Model specific kwargs can be
-        specified with model_kwargs using load_pipeline_args.
+      model (str): path to the pretrained model-id on Hugging Face Models Hub
+        to use custom model for the chosen task. If the `model` already defines
+        the task then no need to specify the `task` parameter. Use the model-id
+        string instead of an actual model here.
+        Model-specific kwargs for `from_pretrained(..., **model_kwargs)` can be
+        specified with `model_kwargs` using `load_pipeline_args`.
+        Example Usage:
+          model_handler = HuggingFacePipelineModelHandler(
+            model_uri="bert-base-uncased", model="meta-llama/Llama-2-7b-hf",
+            load_pipeline_args={'model_kwargs':{'quantization_map':config}})
       inference_fn: the inference function to use during RunInference.
         Default is _default_pipeline_inference_fn.
       load_pipeline_args (Dict[str, Any]): keyword arguments to provide load