[Move over to pyember/ember] Hugging Face Models Support

.gitignore

@@ -0,0 +1,30 @@
+# Python
+__pycache__/
+*.py[cod]
+*.so
+.Python
+build/
+dist/
+*.egg-info/
+.pytest_cache/
+.coverage
+htmlcov/
+
+# Environments
+.env
+.venv
+env/
+venv/
+
+# OS specific
+.DS_Store
+Thumbs.db
+
+# Editors
+.idea/
+.vscode/
+*.swp
+*~
+
+# Project specific
+.hypothesis/

src/ember/core/app_context.py

@@ -135,6 +135,7 @@ def _initialize_api_keys_from_env(config_manager: ConfigManager) -> None:
         "OPENAI_API_KEY": "openai",
         "ANTHROPIC_API_KEY": "anthropic",
         "GOOGLE_API_KEY": "google",
+        "HUGGINGFACE_API_KEY": "huggingface",
     }
 
     # Set API keys from environment if available

src/ember/core/registry/model/base/registry/discovery.py

@@ -17,6 +17,10 @@
 )
 from ember.core.registry.model.providers.openai.openai_discovery import OpenAIDiscovery
 
+from ember.core.registry.model.providers.huggingface.huggingface_discovery import (
+    HuggingFaceDiscovery,
+) 
+
 logger: logging.Logger = logging.getLogger(__name__)
 # Set default log level to WARNING to reduce verbosity
 logger.setLevel(logging.WARNING)
@@ -80,6 +84,11 @@ def _initialize_providers(self) -> List[BaseDiscoveryProvider]:
                 "GOOGLE_API_KEY",
                 lambda: {"api_key": os.environ.get("GOOGLE_API_KEY", "")},
             ),
+            (
+                HuggingFaceDiscovery,
+                "HUGGINGFACE_API_KEY", #Now searches for Hugging Face API key
+                lambda: {"api_key": os.environ.get("HUGGINGFACE_API_KEY", "")},
+            ),
         ]
 
         # Initializing providers with available credentials

src/ember/core/registry/model/examples/mistral_7b_example.py

@@ -0,0 +1,162 @@
+"""Mistral-7B-Instruct-v0.2 usage example using Hugging Face Inference API.
+
+This module demonstrates how to use the Mistral-7B-Instruct-v0.2 model
+through the Ember framework, showcasing instruction following capabilities
+and chat-oriented generation.
+"""
+
+import logging
+import os
+from typing import Any, Dict, Optional, Union, cast
+
+from ember.core.registry.model.base.schemas.chat_schemas import ChatResponse
+from ember.core.registry.model.base.schemas.model_info import ModelInfo, ProviderInfo
+from ember.core.registry.model.base.services.model_service import ModelService
+from ember.core.registry.model.initialization import initialize_registry
+from ember.core.registry.model.base.schemas.cost import ModelCost, RateLimit
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+
+def get_model_service() -> ModelService:
+    """Get the model service with Mistral-7B-Instruct-v0.2 registered.
+
+    Returns:
+        A ModelService instance with Mistral-7B-Instruct-v0.2 registered
+    """
+    # Initialize the registry
+    registry = initialize_registry(auto_discover=True)
+    service = ModelService(registry=registry)
+
+    # Register Mistral-7B-Instruct-v0.2 if not already registered
+    try:
+        # Try to get the model - will raise an exception if not found
+        service.get_model("huggingface:mistralai/Mistral-7B-Instruct-v0.2")
+        logger.info("Mistral model already registered")
+    except Exception:
+        # Model not found, register it
+        logger.info("Registering Mistral model")
+
+        # Get API key from environment variable or use a default for testing
+        api_key = os.environ.get("HUGGINGFACE_API_KEY", "your_api_key_here")
+
+        # Create model info
+        model_info = ModelInfo(
+            id="huggingface:mistralai/Mistral-7B-Instruct-v0.2",
+            name="mistralai/Mistral-7B-Instruct-v0.2",
+            provider=ProviderInfo(name="HuggingFace", default_api_key=api_key),
+            cost=ModelCost(
+                input_cost_per_thousand=0.0,
+                output_cost_per_thousand=0.0,
+            ),
+        )
+
+        # Register the model
+        registry.register_model(model_info)
+
+    return service
+
+
+def basic_instruction_example(service: ModelService) -> None:
+    """Basic instruction following with Mistral-7B-Instruct-v0.2.
+
+    This example shows how to provide instructions and get responses.
+
+    Args:
+        service: The model service with Mistral-7B-Instruct-v0.2 registered
+    """
+    try:
+        # Get the model
+        mistral_model = service.get_model("huggingface:mistralai/Mistral-7B-Instruct-v0.2")
+
+        # Generate a response to an instruction
+        response = mistral_model(
+            "Explain what artificial intelligence is to a 10-year old child.",
+            temperature=0.7,
+            max_tokens=150
+        )
+
+        print("\n=== Basic Instruction ===")
+        print(f"Response: {response.data}")
+        print(f"Used {response.usage.total_tokens} tokens")
+
+    except Exception as error:
+        logger.exception("Error during basic instruction: %s", error)
+
+
+def creative_writing_example(service: ModelService) -> None:
+    """Creative writing example with Mistral-7B-Instruct-v0.2.
+
+    This example demonstrates using the model for creative writing tasks.
+
+    Args:
+        service: The model service with Mistral-7B-Instruct-v0.2 registered
+    """
+    try:
+        # Get the model
+        mistral_model = service.get_model("huggingface:mistralai/Mistral-7B-Instruct-v0.2")
+
+        # Create a short story prompt
+        response = mistral_model(
+            "Write a short story about a robot discovering emotions for the first time. Make it touching and meaningful.",
+            temperature=0.8,  # Higher temperature for more creativity
+            max_tokens=300
+        )
+
+        print("\n=== Creative Writing ===")
+        print(f"Response: {response.data}")
+        print(f"Used {response.usage.total_tokens} tokens")
+
+    except Exception as error:
+        logger.exception("Error during creative writing: %s", error)
+
+
+def factual_qa_example(service: ModelService) -> None:
+    """Factual Q&A example with Mistral-7B-Instruct-v0.2.
+
+    This example tests the model's ability to answer factual questions.
+
+    Args:
+        service: The model service with Mistral-7B-Instruct-v0.2 registered
+    """
+    try:
+        # Get the model
+        mistral_model = service.get_model("huggingface:mistralai/Mistral-7B-Instruct-v0.2")
+
+        # Ask a factual question
+        response = mistral_model(
+            "What are the main components of the solar system? List the planets in order from the sun.",
+            temperature=0.3,  # Lower temperature for more factual responses
+            max_tokens=200
+        )
+
+        print("\n=== Factual Q&A ===")
+        print(f"Response: {response.data}")
+        print(f"Used {response.usage.total_tokens} tokens")
+
+    except Exception as error:
+        logger.exception("Error during factual Q&A: %s", error)
+
+
+def main() -> None:
+    """Run all Mistral-7B-Instruct-v0.2 examples."""
+    print("Mistral-7B-Instruct-v0.2 Usage Examples")
+    print("========================================")
+
+    try:
+        # Get the model service with Mistral registered
+        service = get_model_service()
+
+        # Run examples
+        basic_instruction_example(service)
+        creative_writing_example(service)
+        factual_qa_example(service)
+
+    except Exception as error:
+        logger.exception("Error during examples: %s", error)
+
+
+if __name__ == "__main__":
+    main() 

src/ember/core/registry/model/providers/huggingface/__init__.py

@@ -0,0 +1 @@
+

src/ember/core/registry/model/providers/huggingface/huggingface_discovery.py

@@ -0,0 +1,68 @@
+#TODO Just a placeholder implementation, need to implement discovery by author later
+"""Discovery mechanism for Hugging Face models.
+
+This module provides functionality to discover and register Hugging Face models
+available through the Hugging Face Hub.
+"""
+
+import logging
+from typing import Dict, List, Optional, Set
+
+from ember.core.registry.model.base.discovery.model_discovery import ModelDiscoveryBase
+from ember.core.registry.model.base.schemas.model_info import ModelInfo, ProviderInfo
+
+logger = logging.getLogger(__name__)
+
+
+class HuggingFaceDiscovery(ModelDiscoveryBase):
+    """Discovery implementation for Hugging Face models.
+
+    This class provides methods to discover models available through the
+    Hugging Face Hub and register them with the Ember model registry.
+    """
+
+    PROVIDER_NAME = "HuggingFace"
+
+    def discover_models(self) -> List[ModelInfo]:
+        """Discover available Hugging Face models.
+
+        Returns:
+            List[ModelInfo]: A list of model information objects for discovered models.
+        """
+        logger.info("Discovering Hugging Face models...")
+
+        # This is a simplified implementation
+        # In a real implementation, you might query the Hugging Face API
+        # to get a list of popular or featured models
+
+        # For now, just return a predefined list of popular models
+        models = [
+            # Prioritize the Mistral Instruct model
+            self._create_model_info("mistralai/Mistral-7B-Instruct-v0.2"),
+            self._create_model_info("meta-llama/Llama-2-7b-chat-hf"),
+            # Keep the base model for comparison
+            self._create_model_info("mistralai/Mistral-7B-v0.3"),
+            self._create_model_info("google/gemma-7b-it"),
+        ]
+
+        logger.info("Discovered %d Hugging Face models", len(models))
+        return models
+
+    def _create_model_info(self, model_name: str) -> ModelInfo:
+        """Create model information for a Hugging Face model.
+
+        Args:
+            model_name: The name of the model on the Hugging Face Hub.
+
+        Returns:
+            ModelInfo: The model information object.
+        """
+        return ModelInfo(
+            id=f"huggingface:{model_name}",
+            name=model_name,
+            provider=ProviderInfo(
+                name=self.PROVIDER_NAME,
+                # API key will be filled in from environment or config
+                default_api_key=None,
+            ),
+        )