Instructions to use nileshc90/apex-code-14b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nileshc90/apex-code-14b-instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="nileshc90/apex-code-14b-instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nileshc90/apex-code-14b-instruct")
model = AutoModelForCausalLM.from_pretrained("nileshc90/apex-code-14b-instruct", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

MLX

How to use nileshc90/apex-code-14b-instruct with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("nileshc90/apex-code-14b-instruct")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use nileshc90/apex-code-14b-instruct with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf nileshc90/apex-code-14b-instruct:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nileshc90/apex-code-14b-instruct:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf nileshc90/apex-code-14b-instruct:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf nileshc90/apex-code-14b-instruct:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf nileshc90/apex-code-14b-instruct:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf nileshc90/apex-code-14b-instruct:Q4_K_M

Use Docker

docker model run hf.co/nileshc90/apex-code-14b-instruct:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use nileshc90/apex-code-14b-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nileshc90/apex-code-14b-instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nileshc90/apex-code-14b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nileshc90/apex-code-14b-instruct:Q4_K_M

SGLang

How to use nileshc90/apex-code-14b-instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "nileshc90/apex-code-14b-instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nileshc90/apex-code-14b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "nileshc90/apex-code-14b-instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nileshc90/apex-code-14b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use nileshc90/apex-code-14b-instruct with Ollama:
```
ollama run hf.co/nileshc90/apex-code-14b-instruct:Q4_K_M
```
Unsloth Desktop

How to use nileshc90/apex-code-14b-instruct with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "nileshc90/apex-code-14b-instruct"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "nileshc90/apex-code-14b-instruct"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use nileshc90/apex-code-14b-instruct with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "nileshc90/apex-code-14b-instruct"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "nileshc90/apex-code-14b-instruct"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "nileshc90/apex-code-14b-instruct",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use nileshc90/apex-code-14b-instruct with Docker Model Runner:
```
docker model run hf.co/nileshc90/apex-code-14b-instruct:Q4_K_M
```

Lemonade

How to use nileshc90/apex-code-14b-instruct with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nileshc90/apex-code-14b-instruct:Q4_K_M

Run and chat with the model

lemonade run user.apex-code-14b-instruct-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use nileshc90/apex-code-14b-instruct with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "nileshc90/apex-code-14b-instruct"

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default nileshc90/apex-code-14b-instruct

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use nileshc90/apex-code-14b-instruct with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "nileshc90/apex-code-14b-instruct"

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "nileshc90/apex-code-14b-instruct" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

apex-code-14b-instruct / deepcoder_benchmark_results.json

nileshc90

docs: add verified 9/9 DeepCoder adaptive benchmark results

f0aa745 verified 3 days ago

Raw History Blame Contribute Delete

8.72 kB

{
  "timestamp": "2026-11-04T05:15:22Z",
  "reasoning_effort": "auto",
  "hardware": {
    "used_gb": 19.7,
    "total_gb": 36.0,
    "percent": 73.2,
    "swap_used_mb": 648.38
  },
  "problems": [
    {
      "id": "concurrent_lru_cache",
      "name": "Concurrent LRU Cache with TTL",
      "category": "Systems & Concurrency",
      "difficulty": "Hard",
      "deepcoder": {
        "ast_valid": true,
        "passed": true,
        "effort_level": "auto (escalated from stateful_cache_lru)",
        "repairs": 2,
        "latency_sec": 196.44,
        "tokens": 2306,
        "sandbox": "warmed_docker"
      },
      "baseline": {
        "ast_valid": true,
        "passed": false,
        "latency_sec": 18.07,
        "tokens": 640,
        "error": "Runtime error (exit code 1):\nTraceback (most recent call last):\n  File \"<stdin>\", line 72, in <module>\n  File \"<stdin>\", line 59, in test_lru_cache\nAssertionError"
      }
    },
    {
      "id": "augmented_interval_tree",
      "name": "Augmented Interval Tree (Overlap Queries)",
      "category": "Algorithms & Trees",
      "difficulty": "Hard",
      "deepcoder": {
        "ast_valid": true,
        "passed": true,
        "effort_level": "auto (escalated from range_augmented_tree)",
        "repairs": 0,
        "latency_sec": 154.35,
        "tokens": 2869,
        "sandbox": "warmed_docker"
      },
      "baseline": {
        "ast_valid": true,
        "passed": false,
        "latency_sec": 32.68,
        "tokens": 815,
        "error": "Runtime error (exit code 1):\nTraceback (most recent call last):\n  File \"<stdin>\", line 85, in <module>\n  File \"<stdin>\", line 68, in test_augmented_interval_tree\nAssertionError"
      }
    },
    {
      "id": "sliding_window_rate_limiter",
      "name": "Sliding Window Rate Limiter",
      "category": "Distributed Systems",
      "difficulty": "Medium",
      "deepcoder": {
        "ast_valid": true,
        "passed": true,
        "effort_level": "system1 (rate_limiter_sliding_window)",
        "repairs": 0,
        "latency_sec": 26.98,
        "tokens": 693,
        "sandbox": "warmed_docker"
      },
      "baseline": {
        "ast_valid": true,
        "passed": true,
        "latency_sec": 28.63,
        "tokens": 753,
        "error": null
      }
    },
    {
      "id": "leetcode_easy_valid_anagram",
      "name": "LeetCode Easy: Valid Anagram with Unicode & Frequency",
      "category": "LeetCode Easy",
      "difficulty": "Easy",
      "deepcoder": {
        "ast_valid": true,
        "passed": true,
        "effort_level": "auto (escalated from unicode_frequency_string)",
        "repairs": 0,
        "latency_sec": 105.1,
        "tokens": 2014,
        "sandbox": "warmed_docker"
      },
      "baseline": {
        "ast_valid": true,
        "passed": false,
        "latency_sec": 19.69,
        "tokens": 509,
        "error": "Runtime error (exit code 1):\nTraceback (most recent call last):\n  File \"<stdin>\", line 58, in <module>\n  File \"<stdin>\", line 53, in test_is_anagram\nAssertionError"
      }
    },
    {
      "id": "leetcode_medium_coin_change",
      "name": "LeetCode Medium: Coin Change with Path Reconstruction",
      "category": "LeetCode Medium",
      "difficulty": "Medium",
      "deepcoder": {
        "ast_valid": true,
        "passed": true,
        "effort_level": "system1 (dp_path_reconstruction)",
        "repairs": 0,
        "latency_sec": 27.9,
        "tokens": 713,
        "sandbox": "warmed_docker"
      },
      "baseline": {
        "ast_valid": true,
        "passed": false,
        "latency_sec": 33.59,
        "tokens": 877,
        "error": "Runtime error (exit code 1):\nTraceback (most recent call last):\n  File \"<stdin>\", line 82, in <module>\n  File \"<stdin>\", line 79, in test_coin_change_with_path\nAssertionError"
      }
    },
    {
      "id": "leetcode_hard_median_sorted_arrays",
      "name": "LeetCode Hard: Median of Two Sorted Arrays",
      "category": "LeetCode Hard",
      "difficulty": "Hard",
      "deepcoder": {
        "ast_valid": true,
        "passed": true,
        "effort_level": "system1 (logarithmic_median_partition)",
        "repairs": 0,
        "latency_sec": 27.66,
        "tokens": 694,
        "sandbox": "warmed_docker"
      },
      "baseline": {
        "ast_valid": true,
        "passed": true,
        "latency_sec": 38.56,
        "tokens": 1013,
        "error": null
      }
    },
    {
      "id": "sysdesign_component_rwlock",
      "name": "System Design Component: Fair ReadWriteLock with Writer Preference",
      "category": "System Design (Component)",
      "difficulty": "Medium-Hard",
      "deepcoder": {
        "ast_valid": true,
        "passed": true,
        "effort_level": "system1 (concurrency_rwlock)",
        "repairs": 0,
        "latency_sec": 33.42,
        "tokens": 854,
        "sandbox": "warmed_docker"
      },
      "baseline": {
        "ast_valid": true,
        "passed": false,
        "latency_sec": 23.25,
        "tokens": 604,
        "error": "Runtime error (exit code 1):\nException in thread Thread-1 (reader):\nTraceback (most recent call last):\n  File \"/usr/local/lib/python3.11/threading.py\", line 1045, in _bootstrap_inner\n    self.run()\n  File \"/usr/local/lib/python3.11/threading.py\", line 982, in run\n    self._target(*self._args, **self._kwargs)\nException in thread Thread-2 (reader):\nTraceback (most recent call last):\n  File \"<stdin>\", line 59, in reader\nTypeError: '_thread.lock' object is not callable\nException in thread Thread-3 (reader):\nTraceback (most recent call last):\n  File \"/usr/local/lib/python3.11/threading.py\", line 1045, in _bootstrap_inner\n  File \"/usr/local/lib/python3.11/threading.py\", line 1045, in _bootstrap_inner\n    self.run()\n  File \"/usr/local/lib/python3.11/threading.py\", line 982, in run\n    self.run()\n  File \"/usr/local/lib/python3.11/threading.py\", line 982, in run\n    self._target(*self._args, **self._kwargs)\n  File \"<stdin>\", line 59, in reader\nTypeError: '_thread.lock' object is not callable\n    self._target(*self._args, **self._kwargs)\n  File \"<stdin>\", line 59, in reader\nTypeError: '_thread.lock' object is not callable\nTraceback (most recent call last):\n  File \"<stdin>\", line 95, in <module>\n  File \"<stdin>\", line 72, in test_fair_read_write_lock\nAssertionError: Concurrent readers test failed"
      }
    },
    {
      "id": "sysdesign_service_consistent_hash",
      "name": "System Design Service: Consistent Hashing Ring with Virtual Nodes",
      "category": "System Design (Service)",
      "difficulty": "Hard",
      "deepcoder": {
        "ast_valid": true,
        "passed": true,
        "effort_level": "system1 (distributed_consistent_hash)",
        "repairs": 0,
        "latency_sec": 30.52,
        "tokens": 762,
        "sandbox": "warmed_docker"
      },
      "baseline": {
        "ast_valid": true,
        "passed": false,
        "latency_sec": 34.31,
        "tokens": 904,
        "error": "Runtime error (exit code 1):\nTraceback (most recent call last):\n  File \"<stdin>\", line 106, in <module>\n  File \"<stdin>\", line 91, in test_consistent_hash_ring\nAssertionError"
      }
    },
    {
      "id": "sysdesign_distributed_2pc",
      "name": "System Design Distributed: Two-Phase Commit Coordinator (2PC)",
      "category": "System Design (Distributed)",
      "difficulty": "Hard",
      "deepcoder": {
        "ast_valid": true,
        "passed": true,
        "effort_level": "auto (escalated from distributed_2pc)",
        "repairs": 0,
        "latency_sec": 165.74,
        "tokens": 2951,
        "sandbox": "warmed_docker"
      },
      "baseline": {
        "ast_valid": true,
        "passed": false,
        "latency_sec": 29.7,
        "tokens": 773,
        "error": "Runtime error (exit code 1):\nTraceback (most recent call last):\n  File \"<stdin>\", line 82, in <module>\n  File \"<stdin>\", line 79, in test_two_phase_commit\nAssertionError"
      }
    }
  ],
  "summary": {
    "baseline_pass_count": 2,
    "deepcoder_pass_count": 9,
    "fast_path_resolved_count": 5,
    "escalated_count": 4,
    "total_problems": 9,
    "baseline_latencies": [
      18.07,
      32.68,
      28.63,
      19.69,
      33.59,
      38.56,
      23.25,
      34.31,
      29.7
    ],
    "deepcoder_latencies": [
      196.44,
      154.35,
      26.98,
      105.1,
      27.9,
      27.66,
      33.42,
      30.52,
      165.74
    ],
    "avg_baseline_latency_sec": 28.72,
    "avg_deepcoder_latency_sec": 85.35
  },
  "final_memory": {
    "used_gb": 21.29,
    "total_gb": 36.0,
    "percent": 78.5,
    "swap_used_mb": 648.38
  }
}

	{
	"timestamp": "2026-11-04T05:15:22Z",
	"reasoning_effort": "auto",
	"hardware": {
	"used_gb": 19.7,
	"total_gb": 36.0,
	"percent": 73.2,
	"swap_used_mb": 648.38
	},
	"problems": [
	{
	"id": "concurrent_lru_cache",
	"name": "Concurrent LRU Cache with TTL",
	"category": "Systems & Concurrency",
	"difficulty": "Hard",
	"deepcoder": {
	"ast_valid": true,
	"passed": true,
	"effort_level": "auto (escalated from stateful_cache_lru)",
	"repairs": 2,
	"latency_sec": 196.44,
	"tokens": 2306,
	"sandbox": "warmed_docker"
	},
	"baseline": {
	"ast_valid": true,
	"passed": false,
	"latency_sec": 18.07,
	"tokens": 640,
	"error": "Runtime error (exit code 1):\nTraceback (most recent call last):\n File \"<stdin>\", line 72, in <module>\n File \"<stdin>\", line 59, in test_lru_cache\nAssertionError"
	}
	},
	{
	"id": "augmented_interval_tree",
	"name": "Augmented Interval Tree (Overlap Queries)",
	"category": "Algorithms & Trees",
	"difficulty": "Hard",
	"deepcoder": {
	"ast_valid": true,
	"passed": true,
	"effort_level": "auto (escalated from range_augmented_tree)",
	"repairs": 0,
	"latency_sec": 154.35,
	"tokens": 2869,
	"sandbox": "warmed_docker"
	},
	"baseline": {
	"ast_valid": true,
	"passed": false,
	"latency_sec": 32.68,
	"tokens": 815,
	"error": "Runtime error (exit code 1):\nTraceback (most recent call last):\n File \"<stdin>\", line 85, in <module>\n File \"<stdin>\", line 68, in test_augmented_interval_tree\nAssertionError"
	}
	},
	{
	"id": "sliding_window_rate_limiter",
	"name": "Sliding Window Rate Limiter",
	"category": "Distributed Systems",
	"difficulty": "Medium",
	"deepcoder": {
	"ast_valid": true,
	"passed": true,
	"effort_level": "system1 (rate_limiter_sliding_window)",
	"repairs": 0,
	"latency_sec": 26.98,
	"tokens": 693,
	"sandbox": "warmed_docker"
	},
	"baseline": {
	"ast_valid": true,
	"passed": true,
	"latency_sec": 28.63,
	"tokens": 753,
	"error": null
	}
	},
	{
	"id": "leetcode_easy_valid_anagram",
	"name": "LeetCode Easy: Valid Anagram with Unicode & Frequency",
	"category": "LeetCode Easy",
	"difficulty": "Easy",
	"deepcoder": {
	"ast_valid": true,
	"passed": true,
	"effort_level": "auto (escalated from unicode_frequency_string)",
	"repairs": 0,
	"latency_sec": 105.1,
	"tokens": 2014,
	"sandbox": "warmed_docker"
	},
	"baseline": {
	"ast_valid": true,
	"passed": false,
	"latency_sec": 19.69,
	"tokens": 509,
	"error": "Runtime error (exit code 1):\nTraceback (most recent call last):\n File \"<stdin>\", line 58, in <module>\n File \"<stdin>\", line 53, in test_is_anagram\nAssertionError"
	}
	},
	{
	"id": "leetcode_medium_coin_change",
	"name": "LeetCode Medium: Coin Change with Path Reconstruction",
	"category": "LeetCode Medium",
	"difficulty": "Medium",
	"deepcoder": {
	"ast_valid": true,
	"passed": true,
	"effort_level": "system1 (dp_path_reconstruction)",
	"repairs": 0,
	"latency_sec": 27.9,
	"tokens": 713,
	"sandbox": "warmed_docker"
	},
	"baseline": {
	"ast_valid": true,
	"passed": false,
	"latency_sec": 33.59,
	"tokens": 877,
	"error": "Runtime error (exit code 1):\nTraceback (most recent call last):\n File \"<stdin>\", line 82, in <module>\n File \"<stdin>\", line 79, in test_coin_change_with_path\nAssertionError"
	}
	},
	{
	"id": "leetcode_hard_median_sorted_arrays",
	"name": "LeetCode Hard: Median of Two Sorted Arrays",
	"category": "LeetCode Hard",
	"difficulty": "Hard",
	"deepcoder": {
	"ast_valid": true,
	"passed": true,
	"effort_level": "system1 (logarithmic_median_partition)",
	"repairs": 0,
	"latency_sec": 27.66,
	"tokens": 694,
	"sandbox": "warmed_docker"
	},
	"baseline": {
	"ast_valid": true,
	"passed": true,
	"latency_sec": 38.56,
	"tokens": 1013,
	"error": null
	}
	},
	{
	"id": "sysdesign_component_rwlock",
	"name": "System Design Component: Fair ReadWriteLock with Writer Preference",
	"category": "System Design (Component)",
	"difficulty": "Medium-Hard",
	"deepcoder": {
	"ast_valid": true,
	"passed": true,
	"effort_level": "system1 (concurrency_rwlock)",
	"repairs": 0,
	"latency_sec": 33.42,
	"tokens": 854,
	"sandbox": "warmed_docker"
	},
	"baseline": {
	"ast_valid": true,
	"passed": false,
	"latency_sec": 23.25,
	"tokens": 604,
	"error": "Runtime error (exit code 1):\nException in thread Thread-1 (reader):\nTraceback (most recent call last):\n File \"/usr/local/lib/python3.11/threading.py\", line 1045, in _bootstrap_inner\n self.run()\n File \"/usr/local/lib/python3.11/threading.py\", line 982, in run\n self._target(self._args, self._kwargs)\nException in thread Thread-2 (reader):\nTraceback (most recent call last):\n File \"<stdin>\", line 59, in reader\nTypeError: '_thread.lock' object is not callable\nException in thread Thread-3 (reader):\nTraceback (most recent call last):\n File \"/usr/local/lib/python3.11/threading.py\", line 1045, in _bootstrap_inner\n File \"/usr/local/lib/python3.11/threading.py\", line 1045, in _bootstrap_inner\n self.run()\n File \"/usr/local/lib/python3.11/threading.py\", line 982, in run\n self.run()\n File \"/usr/local/lib/python3.11/threading.py\", line 982, in run\n self._target(self._args, *self._kwargs)\n File \"<stdin>\", line 59, in reader\nTypeError: '_thread.lock' object is not callable\n self._target(self._args, **self._kwargs)\n File \"<stdin>\", line 59, in reader\nTypeError: '_thread.lock' object is not callable\nTraceback (most recent call last):\n File \"<stdin>\", line 95, in <module>\n File \"<stdin>\", line 72, in test_fair_read_write_lock\nAssertionError: Concurrent readers test failed"
	}
	},
	{
	"id": "sysdesign_service_consistent_hash",
	"name": "System Design Service: Consistent Hashing Ring with Virtual Nodes",
	"category": "System Design (Service)",
	"difficulty": "Hard",
	"deepcoder": {
	"ast_valid": true,
	"passed": true,
	"effort_level": "system1 (distributed_consistent_hash)",
	"repairs": 0,
	"latency_sec": 30.52,
	"tokens": 762,
	"sandbox": "warmed_docker"
	},
	"baseline": {
	"ast_valid": true,
	"passed": false,
	"latency_sec": 34.31,
	"tokens": 904,
	"error": "Runtime error (exit code 1):\nTraceback (most recent call last):\n File \"<stdin>\", line 106, in <module>\n File \"<stdin>\", line 91, in test_consistent_hash_ring\nAssertionError"
	}
	},
	{
	"id": "sysdesign_distributed_2pc",
	"name": "System Design Distributed: Two-Phase Commit Coordinator (2PC)",
	"category": "System Design (Distributed)",
	"difficulty": "Hard",
	"deepcoder": {
	"ast_valid": true,
	"passed": true,
	"effort_level": "auto (escalated from distributed_2pc)",
	"repairs": 0,
	"latency_sec": 165.74,
	"tokens": 2951,
	"sandbox": "warmed_docker"
	},
	"baseline": {
	"ast_valid": true,
	"passed": false,
	"latency_sec": 29.7,
	"tokens": 773,
	"error": "Runtime error (exit code 1):\nTraceback (most recent call last):\n File \"<stdin>\", line 82, in <module>\n File \"<stdin>\", line 79, in test_two_phase_commit\nAssertionError"
	}
	}
	],
	"summary": {
	"baseline_pass_count": 2,
	"deepcoder_pass_count": 9,
	"fast_path_resolved_count": 5,
	"escalated_count": 4,
	"total_problems": 9,
	"baseline_latencies": [
	18.07,
	32.68,
	28.63,
	19.69,
	33.59,
	38.56,
	23.25,
	34.31,
	29.7
	],
	"deepcoder_latencies": [
	196.44,
	154.35,
	26.98,
	105.1,
	27.9,
	27.66,
	33.42,
	30.52,
	165.74
	],
	"avg_baseline_latency_sec": 28.72,
	"avg_deepcoder_latency_sec": 85.35
	},
	"final_memory": {
	"used_gb": 21.29,
	"total_gb": 36.0,
	"percent": 78.5,
	"swap_used_mb": 648.38
	}
	}