removed quantization to simplify

src/about.py

@@ -8,15 +8,16 @@ class Task:
     col_name: str
 
 
-# Init: to update with your specific keys
+# Select your tasks here
+# ---------------------------------------------------
 class Tasks(Enum):
     # task_key in the json file, metric_key in the json file, name to display in the leaderboard 
     task0 = Task("anli_r1", "acc", "ANLI")
     task1 = Task("logiqa", "acc_norm", "LogiQA")
 
-TASKS_HARNESS = [task.value.benchmark for task in Tasks]
-
 NUM_FEWSHOT = 0 # Change with your few shot
+# ---------------------------------------------------
+
 
 
 # Your leaderboard name

src/backend/manage_requests.py

@@ -14,7 +14,7 @@ class EvalRequest:
     json_filepath: str
     weight_type: str = "Original"
     model_type: str = ""  # pretrained, finetuned, with RL
-    precision: str = ""  # float16, bfloat16, 8bit, 4bit, GPTQ
+    precision: str = ""  # float16, bfloat16
     base_model: Optional[str] = None # for adapter models
     revision: str = "main" # commit
     submitted_time: Optional[str] = "2022-05-18T11:40:22.519222"  # random date just so that we can still order requests by date
@@ -28,11 +28,12 @@ class EvalRequest:
 
         if self.precision in ["float16", "bfloat16"]:
             model_args += f",dtype={self.precision}"
-        elif self.precision == "8bit":
-            model_args += ",load_in_8bit=True"
-        elif self.precision == "4bit":
-            model_args += ",load_in_4bit=True"
-        elif self.precision == "GPTQ":
+        # Quantized models need some added config, the install of bits and bytes, etc
+        #elif self.precision == "8bit":
+        #    model_args += ",load_in_8bit=True"
+        #elif self.precision == "4bit":
+        #    model_args += ",load_in_4bit=True"
+        #elif self.precision == "GPTQ":
             # A GPTQ model does not need dtype to be specified,
             # it will be inferred from the config
             pass
@@ -42,9 +43,7 @@ class EvalRequest:
         return model_args
 
 
-def set_eval_request(
-    api: HfApi, eval_request: EvalRequest, set_to_status: str, hf_repo: str, local_dir: str
-):
+def set_eval_request(api: HfApi, eval_request: EvalRequest, set_to_status: str, hf_repo: str, local_dir: str):
     """Updates a given eval request with its new status on the hub (running, completed, failed, ...)"""
     json_filepath = eval_request.json_filepath
 

src/display/utils.py

@@ -94,9 +94,9 @@ class WeightType(Enum):
 class Precision(Enum):
     float16 = ModelDetails("float16")
     bfloat16 = ModelDetails("bfloat16")
-    qt_8bit = ModelDetails("8bit")
-    qt_4bit = ModelDetails("4bit")
-    qt_GPTQ = ModelDetails("GPTQ")
+    #qt_8bit = ModelDetails("8bit")
+    #qt_4bit = ModelDetails("4bit")
+    #qt_GPTQ = ModelDetails("GPTQ")
     Unknown = ModelDetails("?")
 
     def from_str(precision):
@@ -104,12 +104,12 @@ class Precision(Enum):
             return Precision.float16
         if precision in ["torch.bfloat16", "bfloat16"]:
             return Precision.bfloat16
-        if precision in ["8bit"]:
-            return Precision.qt_8bit
-        if precision in ["4bit"]:
-            return Precision.qt_4bit
-        if precision in ["GPTQ", "None"]:
-            return Precision.qt_GPTQ
+        #if precision in ["8bit"]:
+        #    return Precision.qt_8bit
+        #if precision in ["4bit"]:
+        #    return Precision.qt_4bit
+        #if precision in ["GPTQ", "None"]:
+        #    return Precision.qt_GPTQ
         return Precision.Unknown
 
 # Column selection