HuggingFacePipeline: Forward model_kwargs. (#696)

Since the tokenizer and model are constructed manually, model_kwargs
needs to
be passed to their constructors. Additionally, the pipeline has a
specific
named parameter to pass these with, which can provide forward
compatibility if
they are used for something other than tokenizer or model construction.

langchain/llms/huggingface_pipeline.py

@@ -68,19 +68,19 @@ class HuggingFacePipeline(LLM, BaseModel):
             )
             from transformers import pipeline as hf_pipeline
 
-            tokenizer = AutoTokenizer.from_pretrained(model_id)
+            _model_kwargs = model_kwargs or {}
+            tokenizer = AutoTokenizer.from_pretrained(model_id, **_model_kwargs)
             if task == "text-generation":
-                model = AutoModelForCausalLM.from_pretrained(model_id)
+                model = AutoModelForCausalLM.from_pretrained(model_id, **_model_kwargs)
             elif task == "text2text-generation":
-                model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
+                model = AutoModelForSeq2SeqLM.from_pretrained(model_id, **_model_kwargs)
             else:
                 raise ValueError(
                     f"Got invalid task {task}, "
                     f"currently only {VALID_TASKS} are supported"
                 )
-            _model_kwargs = model_kwargs or {}
             pipeline = hf_pipeline(
-                task=task, model=model, tokenizer=tokenizer, **_model_kwargs
+                task=task, model=model, tokenizer=tokenizer, model_kwargs=_model_kwargs
             )
             if pipeline.task not in VALID_TASKS:
                 raise ValueError(