cleanup and fix variable names for GGUF model

lm_eval/models/gguf.py

@@ -10,14 +10,14 @@ from lm_eval.base import BaseLM
 logger = logging.getLogger(__name__)
 
 
-def get_result(logprobs, context_lenght):
+def get_result(logprobs, context_length):
     is_greedy = True
     offsets = logprobs['text_offset']
     tokens = logprobs['tokens']
     tokens_logprobs = logprobs['token_logprobs']
 
     idx = 0
-    while offsets[idx] < context_lenght:
+    while offsets[idx] < context_length:
         idx += 1
     continuation_logprobs = sum(tokens_logprobs[idx:-1])
     for i in range(idx, len(tokens)):
@@ -32,21 +32,19 @@ def get_result(logprobs, context_lenght):
 
 
 class GGUFLM(BaseLM):
-    def __init__(self, base_url, truncate=False):
+    def __init__(self, base_url, max_length=2048):
         super().__init__()
         self.base_url = base_url
         self.truncate = truncate
-        self.tokenizer = transformers.GPT2TokenizerFast.from_pretrained("gpt2")
-        self.logpobs = 10
+        self.logprobs = 10
         self.temperature = 0.0
-        self.max_length = 1024
-        self.vocab_size = self.tokenizer.vocab_size
+        self.max_length = max_length
 
     def gguf_completion(self, context, continuation=None, stop=None, retries=3, delay=5, **kwargs):
         for _ in range(retries):
             try:
                 prompt = context
-                request = {'prompt': prompt, 'logprobs': self.logpobs,
+                request = {'prompt': prompt, 'logprobs': self.logprobs,
                            'temperature': self.temperature}
                 if continuation:
                     prompt += continuation
@@ -105,19 +103,7 @@ class GGUFLM(BaseLM):
         return res
 
     def loglikelihood_rolling(self, requests):
-        results = []
-
-        for request in requests:
-            logprobs = []
-            for i in range(0, len(request), self.max_length):
-                chunk = request[i:i + self.max_length]
-                chunk_loglikelihood = self.loglikelihood([(chunk, request[i + 1:i + self.max_length + 1])])
-                logprobs.extend(chunk_loglikelihood)
-
-            avg_loglikelihood = sum([logprob for logprob, _ in logprobs]) / len(logprobs)
-            results.append((avg_loglikelihood, True))
-
-        return results
+        raise NotImplementedError("loglikelihood_rolling not yet supported for GGUF models")
 
     def _model_call(self, inps):
         # Placeholder implementation
@@ -128,10 +114,10 @@ class GGUFLM(BaseLM):
         raise NotImplementedError()
 
     def tok_encode(self, string: str):
-        return self.tokenizer.encode(string, add_special_tokens=False)
+        raise NotImplementedError()
 
     def tok_decode(self, tokens):
-        return self.tokenizer.decode(tokens)
+        raise NotImplementedError()
 
     @property
     def batch_size(self):