remove unnecessary tags

backend/helpers.py

@@ -25,10 +25,9 @@ def unify_precision(raw_precision: str) -> str:
         
     return PRECISION_MAP.get(clean_p, "Missing")
 
-def get_model_size(model_info: ModelInfo, precision: str) -> float:
+def get_model_size(model_info: ModelInfo) -> float:
     """
     Return approximate model parameter size in billions.
-    Note: 'precision' arg is kept for compatibility but currently unused.
     """
     try:
         # Safely access safetensors, defaulting to None if attribute missing

backend/submission_handler.py

@@ -84,8 +84,8 @@ class SlackNotifier:
         """
         model_name = submission_data.get("model", "Unknown")
         org = model_name.split("/")[0] if "/" in model_name else "Unknown"
-        precision = submission_data.get("precision", "UNK")
-        weight_type = submission_data.get("weight_type", "Unknown")
+        # precision = submission_data.get("precision", "UNK")
+        # weight_type = submission_data.get("weight_type", "Unknown")
         params = submission_data.get("params", "Unknown")
         
         blocks = [
@@ -108,14 +108,14 @@ class SlackNotifier:
                         "type": "mrkdwn",
                         "text": f"*Organization:*\n{org}"
                     },
-                    {
-                        "type": "mrkdwn",
-                        "text": f"*Precision:*\n{precision}"
-                    },
-                    {
-                        "type": "mrkdwn",
-                        "text": f"*Weight Type:*\n{weight_type}"
-                    },
+                    # {
+                    #     "type": "mrkdwn",
+                    #     "text": f"*Precision:*\n{precision}"
+                    # },
+                    # {
+                    #     "type": "mrkdwn",
+                    #     "text": f"*Weight Type:*\n{weight_type}"
+                    # },
                     {
                         "type": "mrkdwn",
                         "text": f"*Parameters:*\n{params}"
@@ -391,8 +391,8 @@ def integrate_with_submission(original_submit_func):
                     "model": args[0] if len(args) > 0 else kwargs.get("model_name"),
                     "base_model": args[1] if len(args) > 1 else kwargs.get("base_model"),
                     "revision": args[2] if len(args) > 2 else kwargs.get("revision"),
-                    "precision": args[3] if len(args) > 3 else kwargs.get("precision"),
-                    "weight_type": args[4] if len(args) > 4 else kwargs.get("weight_type"),
+                    # "precision": args[3] if len(args) > 3 else kwargs.get("precision"),
+                    # "weight_type": args[4] if len(args) > 4 else kwargs.get("weight_type"),
                     "submitted_time": datetime.utcnow().isoformat() + "Z",
                     "slack_thread_ts": null
                 }
@@ -406,7 +406,7 @@ def integrate_with_submission(original_submit_func):
     return wrapper
 
 
-def already_in_queue(df: pd.DataFrame, model_name: str, revision: str, precision: str) -> bool:
+def already_in_queue(df: pd.DataFrame, model_name: str) -> bool:
     """
     Check if (model, revision, precision) is already in the provided dataframe.
     """
@@ -415,9 +415,7 @@ def already_in_queue(df: pd.DataFrame, model_name: str, revision: str, precision
     
     # Create a boolean mask for matching rows
     mask = (
-        (df["model"] == model_name) &
-        (df["revision"] == revision) &
-        (df["precision"] == unify_precision(precision))
+        (df["model"] == model_name)
     )
     return not df[mask].empty
 
@@ -539,7 +537,7 @@ def submit_model(
     if base_model:
         base_model = base_model.strip()
     revision = revision.strip() or "main"
-    precision = precision.strip()
+    # precision = precision.strip()
     model_type = MODEL_TYPE_NORMALIZATION.get(model_type.strip().lower(), model_type.strip())
 
     
@@ -560,20 +558,20 @@ def submit_model(
         return f"**Error**: {card_msg}"
 
     # B. Check Hub Existence (Base vs Target)
-    if weight_type.lower() in ["adapter", "delta"]:
-        if not base_model:
-            return "**Error**: For adapter/delta, you must provide a valid `base_model`."
-        ok_base, base_err, _ = is_model_on_hub(
-            base_model, revision, hf_api_token, trust_remote_code=True
-        )
-        if not ok_base:
-            return f"**Error**: Base model '{base_model}' {base_err}"
-    else:
-        ok_model, model_err, _ = is_model_on_hub(
-            model_name, revision, hf_api_token, trust_remote_code=True
-        )
-        if not ok_model:
-            return f"**Error**: Model '{model_name}' {model_err}"
+    # if weight_type.lower() in ["adapter", "delta"]:
+    #     if not base_model:
+    #         return "**Error**: For adapter/delta, you must provide a valid `base_model`."
+    #     ok_base, base_err, _ = is_model_on_hub(
+    #         base_model, revision, hf_api_token, trust_remote_code=True
+    #     )
+    #     if not ok_base:
+    #         return f"**Error**: Base model '{base_model}' {base_err}"
+    # else:
+    ok_model, model_err, _ = is_model_on_hub(
+        model_name, revision, hf_api_token, trust_remote_code=True
+    )
+    if not ok_model:
+        return f"**Error**: Model '{model_name}' {model_err}"
 
     # C. Fetch Model Info (Likes, License, Private Status)
     try:
@@ -586,11 +584,11 @@ def submit_model(
     model_private = bool(getattr(info, "private", False))
 
     # D. Check Queue Duplication
-    if already_in_queue(load_requests("finished"), model_name, revision, precision):
-        return f"**Warning**: '{model_name}' (rev='{revision}', prec='{precision}') has already been evaluated."
+    if already_in_queue(load_requests("finished"), model_name):
+        return f"**Warning**: '{model_name}') has already been evaluated."
     
-    if already_in_queue(load_requests("pending"), model_name, revision, precision):
-        return f"**Warning**: '{model_name}' (rev='{revision}', prec='{precision}') is already in PENDING."
+    if already_in_queue(load_requests("pending"), model_name):
+        return f"**Warning**: '{model_name}') is already in PENDING."
 
     # E. Check Rate Limit
     under_threshold, limit_msg = check_org_threshold(org)
@@ -598,19 +596,19 @@ def submit_model(
         return f"**Error**: {limit_msg}"
 
     # --- 3. Submission Construction ---
-    precision_final = unify_precision(precision)
-    if precision_final == "Missing":
-        precision_final = "UNK"
+    # precision_final = unify_precision(precision)
+    # if precision_final == "Missing":
+    #     precision_final = "UNK"
 
-    model_params = get_model_size(model_info=info, precision=precision)
+    model_params = get_model_size(model_info=info)
     current_time = datetime.utcnow().isoformat() + "Z"
 
     submission_data = {
         "model":          model_name,
-        "base_model":     base_model,
+        # "base_model":     base_model,
         "revision":       revision,
-        "precision":      precision_final,
-        "weight_type":    weight_type,
+        # "precision":      precision_final,
+        # "weight_type":    weight_type,
         "status":         "PENDING",
         "submitted_time": current_time,
         "model_type":     model_type,
@@ -623,8 +621,7 @@ def submit_model(
     }
 
     # Define path in the requests dataset
-    private_str = "True" if model_private else "False"
-    file_path = f"{org}/{repo_id}_eval_request_{private_str}_{precision_final}_{weight_type}.json"
+    file_path = f"{org}/{repo_id}_eval_request.json"
 
     # --- 4. Upload to Hub ---
     try: