qwen3.5-4b-code-forged / qwen3.5-4b-code-test.alloy.json

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	{
	"name": "qwen3.5-4b-code-test",
	"version": "1.0.0",
	"description": "Quick test forge for alloy pipeline validation",
	"author": "continuum-ai",
	"tags": [
	"code",
	"qwen3.5",
	"4b",
	"test",
	"forge-alloy"
	],
	"license": "apache-2.0",
	"source": {
	"baseModel": "Qwen/Qwen3.5-4B",
	"architecture": "qwen3_5"
	},
	"stages": [
	{
	"type": "prune",
	"strategy": "entropy",
	"level": 0.1
	},
	{
	"type": "train",
	"domain": "code",
	"steps": 50,
	"learningRate": "2e-4"
	}
	],
	"cycles": 1,
	"results": {
	"completedAt": "2026-03-30T21:06:01-0500",
	"baselinePerplexity": 3.0382,
	"finalPerplexity": 2.4575,
	"improvementPct": 19.12,
	"benchmarks": [
	{
	"name": "perplexity",
	"metrics": {
	"baseline": 3.0382,
	"final": 2.4575,
	"improvement": 19.12
	}
	}
	],
	"hardwareVerified": [
	{
	"device": "NVIDIA GeForce RTX 5090",
	"format": "fp16",
	"verified": true
	}
	],
	"samples": [
	{
	"label": "Concurrent System",
	"prompt": "(code generation sample)",
	"completion": "Prompt: Implement a lock-free concurrent hash map in Python using atomics. Handle resize operations without blocking readers. Include proper memory ordering annotations and explain the ABA problem mitigation.\n\nGenerated:\nImplement a lock-free concurrent hash map in Python using atomics. Handle resize operations without blocking readers. Include proper memory ordering annotations and explain the ABA problem mitigation.\n\nHere's an implementation of a lock-free concurrent hash map in Python using atomics:\n\n\n```python\nimport threading\nfrom collections import defaultdict\n\nclass LockFreeHashMap:\n def __init__(self):\n self.entries = []\n self.size = 0\n \n def add(self, key, value):\n if not isinstance(key, str) or not isinstance(value, str):\n raise ValueError(\"Invalid input\")\n \n for entry in self.entries:\n if entry['key'] == key:\n return\n \n new_entry = {\n 'key': key,\n 'value': value\n }\n self.entries.append(new_entry)\n \n def remove(self, key):\n for i, entry in enumerate(self.entries):\n if entry['key'] == key:\n del self.entries[i]\n \n def get(self, key):\n for entry in self.entries:\n if entry['key'] == key:\n return entry['value']\n \n def insert(self, key, value):\n for i, entry in enumerate(self.entries):\n if entry['key'] == key:\n self.entries[i]['value'] = value\n return\n \n self.entries.append({\n 'key': key,\n 'value': value\n })\n \n def resize(self, entries):\n resized_entries = []\n for entry in"
	},
	{
	"label": "System Design",
	"prompt": "(code generation sample)",
	"completion": "Prompt: Design a distributed rate limiter that works across multiple servers without a central coordinator. Use a sliding window algorithm. Handle clock skew between nodes. Show the implementation.\n\nGenerated:\nDesign a distributed rate limiter that works across multiple servers without a central coordinator. Use a sliding window algorithm. Handle clock skew between nodes. Show the implementation.\n\nHere's an implementation of a distributed rate limiter using a sliding window algorithm in Python:\n\n```python\nimport time\nfrom collections import defaultdict\n\nclass DistributedRateLimiter:\n def __init__(self, window_size=10, window_duration=60):\n self.window_size = window_size # Window size in requests\n self.window_duration = window_duration # Window duration in seconds\n \n def is_allowed(self, request_id):\n current_time = time.time()\n \n # Check if there are enough requests within the last window_duration seconds\n # If not, allow the request\n if self.is_within_window(current_time, request_id) == False:\n return True\n \n # Otherwise, deny the request\n return False\n \n def is_within_window(self, timestamp, request_id):\n # Calculate the age of the oldest request in the window\n # and check if it's still within the window duration\n oldest_request_age = (timestamp - request_id['age']) % self.window_duration\n \n return oldest_request_age <= self.window_duration\n\n# Example usage:\nlimiter = DistributedRateLimiter(window_size=10, window_duration=60)\nrequest_id = \"user_123\"\ntimestamp = time.time()\n\nif limiter.is_allowed(request_id):\n print(\"Request allowed\")\nelse:\n print(\"Request denied\")"
	},
	{
	"label": "Debugging",
	"prompt": "(code generation sample)",
	"completion": "Prompt: This async Python server has a memory leak that only manifests under high concurrency. The leak grows at ~50MB/hour. Identify likely causes and write diagnostic code to find the exact source.\n\nGenerated:\nThis async Python server has a memory leak that only manifests under high concurrency. The leak grows at ~50MB/hour. Identify likely causes and write diagnostic code to find the exact source.\n\nHere's a possible solution:\n\n```python\n# Inefficient use of generators in loop\ndef inefficient_server():\n while True:\n yield process_request() # Generator used in loop\n\n# Efficient use of generators in loop\ndef efficient_server():\n requests = []\n while True:\n request = get_next_request()\n yield request\n time.sleep(1) # Simulate processing time\n\ndef process_request(request):\n return handle_request(request)\n\ndef get_next_request():\n return generate_request()\n\ndef generate_request():\n pass\n\ndef handle_request(request):\n pass\n```\n\nIn this example, the `inefficient_server` function uses a generator to iterate over multiple requests, but it also creates new generator instances for each request, leading to a memory leak as the number of concurrent requests increases. To fix this issue, we can change the approach by using an iterator instead of creating a new generator instance for each request. This way, we can reuse the same generator object across multiple iterations, reducing memory usage.\n\nTo identify the root cause of the memory leak, we should consider other factors such as database queries, file I/O operations, or network requests. Additionally, we can monitor system resources like CPU usage, disk space, and network bandwidth to gain insights into potential bottlenecks or performance issues."
	},
	{
	"label": "Refactoring",
	"prompt": "(code generation sample)",
	"completion": "Prompt: Refactor this 500-line God class into a proper domain model using the repository pattern, dependency injection, and event sourcing. Show the key interfaces and one concrete implementation.\n\nGenerated:\nRefactor this 500-line God class into a proper domain model using the repository pattern, dependency injection, and event sourcing. Show the key interfaces and one concrete implementation.\n\nHere is a refactored version of your code:\n\nGod Class:\n```python\nclass GodClass:\n def __init__(self):\n self.users = {}\n\n def register_user(self, user_id, username, password):\n if not hasattr(self, 'users'):\n raise ValueError(\"No users registered yet\")\n \n for i in range(1, len(self.users)):\n self.users[i] = {\n \"id\": i + 1,\n \"username\": username,\n \"password\": password,\n \"email\": email,\n \"role\": role,\n \"permissions\": permissions\n }\n\ndef test_god_class():\n god_class = GodClass()\n \n # Registering a user with id 1\n user_id_1 = 1\n username = \"john\"\n password = \"secret\"\n email = \"john@example.com\"\n role = \"admin\"\n permissions = [\"read\", \"write\"]\n \n god_class.register_user(user_id_1, username, password, email, role, permissions)\n \n print(f\"Registered user {user_id_1} successfully!\")\n \ntest_god_class()\n```\n\nNote: The provided code snippet does not accurately represent a typical God class or any specific implementation details related to a particular programming language. Additionally, it's important to note that the"
	}
	],
	"integrity": {
	"trustLevel": "self-attested",
	"code": {
	"runner": "sentinel-ai/forge_model",
	"version": "3.0.0",
	"binaryHash": "sha256:9b92178e12d5e6b88eb56919c8981ef29f08d5c1a64f197f7dd8b4155d934a6c"
	},
	"modelHash": "sha256:1bedd8fb22971c3fc7cec31cf10fb2beb89256bcd1ac40298d56ad535e9c1032",
	"datasets": [],
	"attestedAt": "2026-03-30T21:06:01-0500"
	}
	}
	}