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README.md

@@ -1,3 +1,60 @@
+---
+title: Phi-4 Unsloth Training
+emoji: 🧠
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: 5.17.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+# Phi-4 Unsloth Optimized Training
+
+This space is dedicated to training Microsoft's Phi-4 model using Unsloth optimizations for enhanced performance and efficiency. The training process utilizes 4-bit quantization and advanced memory optimizations.
+
+## Features
+
+- 4-bit quantization using Unsloth
+- Optimized training pipeline
+- Cognitive dataset integration
+- Advanced memory management
+- Gradient checkpointing
+- Sequential data processing
+
+## Configuration Files
+
+- `transformers_config.json`: Model and training parameters
+- `hardware_config.json`: Hardware-specific optimizations
+- `dataset_config.json`: Dataset processing settings
+- `requirements.txt`: Required dependencies
+
+## Training Process
+
+The training utilizes the following optimizations:
+- Unsloth's 4-bit quantization
+- Custom chat templates for Phi-4
+- Paper-order preservation
+- Efficient memory usage
+- Gradient accumulation
+
+## Dataset
+
+Training uses the cognitive dataset with:
+- Maintained paper order
+- Proper metadata handling
+- Optimized sequence length
+- Efficient batching
+
+## Hardware Requirements
+
+- GPU: A10G or better
+- VRAM: 24GB minimum
+- RAM: 32GB recommended
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
 # Phase 1: Domain Adaptation (Unsupervised)
 
 This directory contains the code and configuration for domain adaptation of the phi-4-unsloth-bnb-4bit model to the cognitive science domain. This phase produces our domain-adapted model: [George-API/phi-4-research-assistant](https://huggingface.co/George-API/phi-4-research-assistant).

update_space.py

@@ -26,6 +26,18 @@ logger = logging.getLogger(__name__)
 
 def load_env_variables():
     """Load environment variables from system or .env file."""
+    # First try to load from local .env file
+    try:
+        from dotenv import load_dotenv
+        env_path = Path(__file__).parent / ".env"
+        if env_path.exists():
+            load_dotenv(env_path)
+            logger.info(f"Loaded environment variables from {env_path}")
+        else:
+            logger.warning(f"No .env file found at {env_path}")
+    except ImportError:
+        logger.warning("python-dotenv not installed, skipping .env loading")
+    
     # Check if we're running in a Hugging Face Space
     if os.environ.get("SPACE_ID"):
         logger.info("Running in Hugging Face Space")
@@ -33,23 +45,12 @@ def load_env_variables():
             username = os.environ.get("SPACE_ID").split("/")[0]
             os.environ["HF_USERNAME"] = username
             logger.info(f"Set HF_USERNAME from SPACE_ID: {username}")
-    else:
-        try:
-            from dotenv import load_dotenv
-            env_path = Path(__file__).parent.parent / ".env"
-            if env_path.exists():
-                load_dotenv(env_path)
-                logger.info(f"Loaded environment variables from {env_path}")
-            else:
-                logger.warning(f"No .env file found at {env_path}")
-        except ImportError:
-            logger.warning("python-dotenv not installed, skipping .env loading")
     
     # Verify required variables
     required_vars = {
         "HF_TOKEN": os.environ.get("HF_TOKEN"),
         "HF_USERNAME": os.environ.get("HF_USERNAME"),
-        "HF_SPACE_NAME": os.environ.get("HF_SPACE_NAME", "phi4-cognitive-training")
+        "HF_SPACE_NAME": os.environ.get("HF_SPACE_NAME", "phi4training")
     }
     
     missing_vars = [k for k, v in required_vars.items() if not v]