added typehints and updated docs

lm_eval/api/model.py

 import abc
 import os
 
-from typing import Union
+from typing import Union, List, Tuple
 from sqlitedict import SqliteDict
 import json
 import hashlib
@@ -25,31 +25,32 @@ class LM(abc.ABC):
         self.cache_hook = CacheHook(None)
 
     @abc.abstractmethod
-    def loglikelihood(self, requests):
+    def loglikelihood(self, requests) -> List[Tuple[float, bool]]:
         """Compute log-likelihood of generating a continuation from a context.
         Downstream tasks should attempt to use loglikelihood instead of other
         LM calls whenever possible.
 
-        :param requests: list
-            A list of pairs (context, continuation)
-            context: str
+        :param requests: list[Instance]
+            A list of Instance objects, with property `args` which returns a tuple (context, continuation).
+            `context: str`
                 Context string. Implementations of LM must be able to handle an
                 empty context string.
-            continuation: str
+            `continuation: str`
                 The continuation over which log likelihood will be calculated. If
                 there is a word boundary, the space should be in the continuation.
                 For example, context="hello" continuation=" world" is correct.
-        :return: list
+
+        :return: list[tuple[float, bool]]
             A list of pairs (logprob, isgreedy)
-            logprob: float
-                The log probability of `continuation`
-            isgreedy:
-                Whether `continuation` would be generated by greedy sampling from `context`
+            `logprob: float`
+                The log probability of `continuation`.
+            `isgreedy`:
+                Whether `continuation` would be generated by greedy sampling from `context`.
         """
         pass
 
     @abc.abstractmethod
-    def loglikelihood_rolling(self, requests):
+    def loglikelihood_rolling(self, requests) -> List[Tuple[float, bool]]:
         """Compute full log-likelihood of a string, with no truncation, for perplexity computation
         - We will use the full max context length of the model.
         - For inputs that exceed the max context length, we divide the tokenized string into chunks of up to
@@ -77,11 +78,11 @@ class LM(abc.ABC):
             1. Each token is predicted exactly once
             2. For the last pair, we provide the full context, but only score the last two tokens
 
-        :param requests: list
-            A list of strings
+        :param requests: list[Instance]
+            A list of Instance objects with property `args` which returns a tuple (context, continuation).
             string: str
                 String for which we are computing per-token loglikelihood
-        :return: list
+        :return: list[tuple[float, bool]]
             A list of pairs (logprob, isgreedy)
             logprob: float
                 The log probability of `continuation`
@@ -92,17 +93,17 @@ class LM(abc.ABC):
 
     # TODO: Add an optional max length
     @abc.abstractmethod
-    def greedy_until(self, requests):
+    def greedy_until(self, requests) -> List[str]:
         """Generate greedily until a stopping sequence
 
-        :param requests: list
-            A list of pairs (context, until)
+        :param requests: list[Instance]
+            A list of Instance objects with property `args` which returns a tuple (context, until).
             context: str
                 Context string
             until: [str]
                 The string sequences to generate until. These string sequences
                 may each span across multiple tokens, or may be part of one token.
-        :return: list
+        :return: list[str]
             A list of strings continuation
             continuation: str
                 The generated continuation.