bugfix

lm_eval/models/vllm.py

@@ -80,6 +80,8 @@ class VLLM(LM):
         stop: Optional[List[str]] = None,
         **kwargs,
     ):
+        if "do_sample" in kwargs.keys():
+            kwargs.pop("do_sample")
         if generate:
             generate_sampling_params = SamplingParams(
                 max_tokens=max_tokens, stop=stop, **kwargs
@@ -181,7 +183,8 @@ class VLLM(LM):
                 fn=None,
             )
             for chunk in chunks:
-                context, context_encoding, all_gen_kwargs = zip(*chunk)
+                context_and_encoding, all_gen_kwargs = zip(*chunk)
+                context, context_encoding = context_and_encoding
                 # we assume all gen kwargs in the batch are the same
                 # this is safe to assume because the `grouper` object ensures it.
                 gen_kwargs = all_gen_kwargs[0]