Version 3.7 update (#1167)

* Update README.md

* version update + bug fix for server

---------

Co-authored-by: Unconst <32490803+unconst@users.noreply.github.com>

README.md

@@ -13,13 +13,18 @@
 
 </div>
 
-This repository contains Bittensor's python API which can be used to 1) Query the Bittensor network as a [client](#31-client) 2) Run and build Bittensor miners & validators for [mining TAO](#43-running-a-template-miner), 3) Pull network [state information](#3-using-bittensor) and 4) Manage [TAO wallets](#41-cli), balances, transfers etc. 
+This repository contains Bittensor's Python API, which can be used for the following purposes:
 
-Bittensor is a mining network (like Bitcoin) with inbaked incentives which are designed to drive miners to provide value; which, in our network, is achieved by hosting trained or training machine learning models, which can be queried by clients seeking inference over inputs (i.e. text-generation, or numerical embeddings from a large foundation model like GPT-NeoX-20B). 
+1. Querying the Bittensor network as a [client](https://github.com/opentensor/bittensor#31-client).
+2. Running and building Bittensor miners and validators for [mining TAO](https://github.com/opentensor/bittensor#43-running-a-template-miner).
+3. Pulling network [state information](https://github.com/opentensor/bittensor#3-using-bittensor).
+4. Managing [TAO wallets](https://github.com/opentensor/bittensor#41-cli), balances, transfers, etc.
 
-The use of token based incentives is by design, built-in to drive the network's size and as a means of distributing the value generated by the network directly to the individuals producing that value without intermediary. The network is open to those who participate and no individual or group has full power of what it learns, who can profit from it, or access it.
+Bittensor is a mining network, similar to Bitcoin, that includes built-in incentives designed to encourage miners to provide value by hosting trained or training machine learning models. These models can be queried by clients seeking inference over inputs, such as token-based text generations or numerical embeddings from a large foundation model like GPT-NeoX-20B.
 
-To learn more about Bittensor read our [paper].(https://drive.google.com/file/d/1VnsobL6lIAAqcA1_Tbm8AYIQscfJV4KU/view). 
+Token-based incentives are designed to drive the network's growth and distribute the value generated by the network directly to the individuals producing that value, without intermediaries. The network is open to all participants, and no individual or group has full control over what is learned, who can profit from it, or who can access it.
+
+To learn more about Bittensor, please read our [paper](https://drive.google.com/file/d/1VnsobL6lIAAqcA1_Tbm8AYIQscfJV4KU/view).
 
 - [1. Documentation](#1-documentation)
 - [2. Install](#2-install)

VERSION

@@ -1 +1 @@
-3.6.3
+3.7.0

bittensor/_neuron/text/core_server/__init__.py

@@ -426,28 +426,26 @@ def synapse_check(self, synapse, hotkey, inputs_x=None):
         """
         ## Uid that sent the request
         incoming_uid = self.metagraph.hotkeys.index(hotkey)
+        batch_size, sequence_len  =  inputs_x[0].size()
         if synapse.synapse_type == bittensor.proto.Synapse.SynapseType.TEXT_LAST_HIDDEN_STATE:
             if self.metagraph.S[incoming_uid] < self.config.neuron.lasthidden_stake \
                 or (batch_size > self.config.neuron.max_batch_size) \
                 or (sequence_len > self.config.neuron.max_sequence_len):
                 return False
 
         elif synapse.synapse_type == bittensor.proto.Synapse.SynapseType.TEXT_CAUSAL_LM:
-            batch_size, sequence_len  =  inputs_x[0].size()
             if (self.metagraph.S[incoming_uid] < self.config.neuron.causallm_stake) \
                 or (batch_size > self.config.neuron.max_batch_size) \
                 or (sequence_len > self.config.neuron.max_sequence_len):
                 return False
 
         elif synapse.synapse_type == bittensor.proto.Synapse.SynapseType.TEXT_CAUSAL_LM_NEXT:
-            batch_size, sequence_len  =  inputs_x[0].size()
             if (self.metagraph.S[incoming_uid] < self.config.neuron.causallmnext_stake) \
                 or (batch_size > self.config.neuron.max_batch_size) \
                 or (sequence_len > self.config.neuron.max_sequence_len):
                 return False
 
         elif synapse.synapse_type == bittensor.proto.Synapse.SynapseType.TEXT_SEQ_2_SEQ:
-            batch_size, sequence_len  =  inputs_x[0].size()
             if (self.metagraph.S[incoming_uid] < self.config.neuron.seq2seq_stake) \
                 or (batch_size > self.config.neuron.max_batch_size) \
                 or (sequence_len > self.config.neuron.max_sequence_len) \