update docs

.env.validator.template

@@ -26,5 +26,5 @@ GENERATION_BATCH_SIZE=3
 DEVICE=cuda
 
 # Other
-LOGLEVEL=info
+LOGLEVEL=INFO
 AUTO_UPDATE=true

docs/Discriminative-Mining.md

@@ -11,11 +11,13 @@ Follow the [Installation Guide](Installation.md) to set up your environment befo
 
 ## Model Preparation
 
-Discriminative miners can submit models in two formats:
-- **ONNX Format** - Traditional ONNX Runtime models (single .onnx file)
-- **Safetensors Format** - Custom PyTorch architectures with native weights
+> **⚠️ Important**: Competition submissions now require **safetensors format**. ONNX is no longer accepted.
 
-**📖 [How to Create ONNX Models](ONNX.md)** - Guide for creating compatible ONNX models
+Discriminative miners must submit models in **safetensors format**:
+- Directory containing: `model_config.yaml`, `model.py`, `*.safetensors`
+- ZIP archive of the directory for upload
+
+**📖 [Safetensors Model Specification](https://github.com/bitmind-ai/gasbench/blob/main/docs/Safetensors.md)** - Requirements for model submission
 
 You can submit models for any combination of modalities:
 - `image_detector.zip` - Image classification model

docs/ONNX.md

@@ -1,5 +1,10 @@
 # ONNX Model Creation Guide
 
+> **⚠️ DEPRECATED**: ONNX format is no longer accepted for Subnet 34 competition submissions. 
+> Please use **safetensors format** instead. See the [Safetensors Guide](https://github.com/bitmind-ai/gasbench/blob/main/docs/Safetensors.md) for requirements.
+
+---
+
 This guide explains how to create ONNX models for discriminative mining using the example scripts in `neurons/discriminator/onnx_examples`.
 
 ## Key Requirements

docs/Validating.md

@@ -40,7 +40,7 @@ GENERATION_BATCH_SIZE=3
 DEVICE=cuda
 
 # Other
-LOGLEVEL=info
+LOGLEVEL=INFO
 AUTO_UPDATE=true
 ```
 

neurons/discriminator/push_model.py

@@ -53,7 +53,7 @@ class Style:
 from gas.protocol.model_uploads import upload_single_modality
 
 
-MODEL_UPLOAD_ENDPOINT = "https://onnx-models-worker.bitmind.workers.dev/upload"
+MODEL_UPLOAD_ENDPOINT = "https://upload.bitmind.ai/upload"
 
 
 def print_success(message: str):