Text Generation
Transformers
Safetensors
GGUF
MLX
English
qwen2
code
code-generation
qwen2.5-coder
ollama
llama.cpp
speculative-decoding
compact-invariant-distillation
mathematical-induction
36gb-ram
conversational
text-generation-inference
4-bit precision
Instructions to use nileshc90/apex-code-14b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
- Pi
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"
File size: 8,721 Bytes
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"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
}
} |