Updated Bittensor Version

docs/VALIDATOR.md

@@ -107,6 +107,7 @@ Using Together AI and Open AI simplifies setup and reduces local resource requir
    echo "TOGETHERAI_API_KEY=your_together_ai_api_key" > .env
    echo "OPENAI_API_KEY=your_openai_api_key" >> .env
    echo "HF_TOKEN=your_hugging_face_token" >> .env (needed for some vLLM model)
+   echo "USE_TORCH=1" >> .env
    ```
 
 ### Step 3: Run the Validator

logicnet/base/validator.py

@@ -27,7 +27,7 @@ def __init__(self, config=None):
 
         # Set up initial scoring weights for validation
         bt.logging.info("\033[1;32m⚖️ Building validation weights.\033[0m")
-        self.scores = torch.zeros_like(self.metagraph.S, dtype=torch.float32)
+        self.scores = torch.zeros_like(self.metagraph.S.clone().detach(), dtype=torch.float32)
 
         # Init sync with the network. Updates the metagraph.
         self.resync_metagraph()
@@ -205,12 +205,15 @@ def set_weights(self):
         bt.logging.trace("top10 values", raw_weights.sort()[0])
         bt.logging.trace("top10 uids", raw_weights.sort()[1])
 
+        # Convert uids to a PyTorch tensor before processing
+        uids_tensor = self.metagraph.uids.clone().detach()
+
         # Process the raw weights to final_weights via subtensor limitations.
         (
             processed_weight_uids,
             processed_weights,
         ) = bt.utils.weight_utils.process_weights_for_netuid(
-            uids=self.metagraph.uids.to("cpu"),
+            uids=uids_tensor.to("cpu"),
             weights=raw_weights.to("cpu"),
             netuid=self.config.netuid,
             subtensor=self.subtensor,

logicnet/utils/regex_helper.py

@@ -0,0 +1,10 @@
+import re
+
+def extract_numerical_part(text):
+    # Use regex to find the first occurrence of a number    
+    match = re.search(r'[-+]?\d*\.?\d+|\d+', text)
+    if match:
+        return match.group(0)
+    else:
+        # Return a specific message or None if no numerical value is found
+        return "No numerical value found"

logicnet/validator/rewarder.py

@@ -6,6 +6,7 @@
 from logicnet.protocol import LogicSynapse
 from sentence_transformers import SentenceTransformer
 from logicnet.utils.model_selector import model_selector
+from logicnet.utils.regex_helper import extract_numerical_part
 
 SIMILARITY_WEIGHT = 0.2
 CORRECTNESS_WEIGHT = 0.8
@@ -235,8 +236,16 @@ def _compare_numerical_answers(self, ground_truth: str, miner_answer: str):
             for char in formatting_chars:
                 ground_truth = ground_truth.replace(char, '')
                 miner_answer = miner_answer.replace(char, '')
-            gt_value = sympy.sympify(ground_truth.strip())
-            miner_value = sympy.sympify(miner_answer.strip())
+
+            # Extract numerical values
+            gt_value_str = extract_numerical_part(ground_truth)
+            miner_value_str = extract_numerical_part(miner_answer)
+
+            if gt_value_str is None or miner_value_str is None:
+                raise ValueError("No numerical value found in one of the answers.")
+
+            gt_value = sympy.sympify(gt_value_str)
+            miner_value = sympy.sympify(miner_value_str)
 
             abs_difference = abs(gt_value - miner_value)
             epsilon = 1e-8

neurons/validator/validator.py

@@ -1,4 +1,6 @@
 import os
+from dotenv import load_dotenv
+load_dotenv()
 import time
 import threading
 import datetime
@@ -378,7 +380,7 @@ def load_state(self):
             bt.logging.info(
                 "\033[1;32m🧠 Loading validator state from: " + path + "\033[0m"
             )
-            state = torch.load(path)
+            state = torch.load(path, weights_only=True)  # Set weights_only=True
             self.step = state["step"]
             all_uids_info = state["all_uids_info"]
             for k, v in all_uids_info.items():

requirements.txt

@@ -1,11 +1,11 @@
-bittensor==6.9.4
+bittensor==8.5.1
 Pillow==10.2.0
 PyYAML==6.0.1
-setuptools==68.0.0
+setuptools==70.0.0
 slowapi==0.1.8
 tqdm==4.65.0
 httpx==0.26.0
-numpy==1.26.4
+numpy==2.0.1
 openai==1.35.14
 sentence-transformers==3.0.1
 python-dotenv==1.0.1