Text Generation
Safetensors
GGUF
English
qwen3_5
qwen
unsloth
lora
code-generation
simplicio-loop
software-engineering
surgical-diff
agentic-coding
conversational
Instructions to use wesleysimplicio/Simplicio-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use wesleysimplicio/Simplicio-27B 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 wesleysimplicio/Simplicio-27B:BF16 # Run inference directly in the terminal: llama cli -hf wesleysimplicio/Simplicio-27B:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf wesleysimplicio/Simplicio-27B:BF16 # Run inference directly in the terminal: llama cli -hf wesleysimplicio/Simplicio-27B:BF16
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf wesleysimplicio/Simplicio-27B:BF16 # Run inference directly in the terminal: ./llama-cli -hf wesleysimplicio/Simplicio-27B:BF16
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf wesleysimplicio/Simplicio-27B:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf wesleysimplicio/Simplicio-27B:BF16
Use Docker
docker model run hf.co/wesleysimplicio/Simplicio-27B:BF16
- LM Studio
- Jan
- vLLM
How to use wesleysimplicio/Simplicio-27B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "wesleysimplicio/Simplicio-27B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "wesleysimplicio/Simplicio-27B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/wesleysimplicio/Simplicio-27B:BF16
- Ollama
How to use wesleysimplicio/Simplicio-27B with Ollama:
ollama run hf.co/wesleysimplicio/Simplicio-27B:BF16
- Unsloth Desktop
- Pi
How to use wesleysimplicio/Simplicio-27B with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf wesleysimplicio/Simplicio-27B:BF16
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "wesleysimplicio/Simplicio-27B:BF16" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use wesleysimplicio/Simplicio-27B with Docker Model Runner:
docker model run hf.co/wesleysimplicio/Simplicio-27B:BF16
- Lemonade
How to use wesleysimplicio/Simplicio-27B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull wesleysimplicio/Simplicio-27B:BF16
Run and chat with the model
lemonade run user.Simplicio-27B-BF16
List all available models
lemonade list
- Hermes Agent
How to use wesleysimplicio/Simplicio-27B with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf wesleysimplicio/Simplicio-27B:BF16
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 wesleysimplicio/Simplicio-27B:BF16
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use wesleysimplicio/Simplicio-27B with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf wesleysimplicio/Simplicio-27B:BF16
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 "wesleysimplicio/Simplicio-27B:BF16" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
docs: update benchmarks/run_livecodebench.py with 6 architectural engineering adjustments
b736b72 verified Download benchmarks/run_livecodebench.py from wesleysimplicio/Simplicio-27B: direct link, hf CLI and curl.
- Browser
- Download file 2.2 kB
-
https://huggingface.co/wesleysimplicio/Simplicio-27B/resolve/main/benchmarks/run_livecodebench.py
- Command line
-
hf download hf://wesleysimplicio/Simplicio-27B/benchmarks/run_livecodebench.py
-
curl -L -o run_livecodebench.py https://huggingface.co/wesleysimplicio/Simplicio-27B/resolve/main/benchmarks/run_livecodebench.py
2.2 kB
| """ | |
| Official 2026 LiveCodeBench (LCB) Runner for Simplicio 27B | |
| Evaluates contamination-free competitive coding, self-repair, and runtime execution. | |
| """ | |
| import os | |
| import sys | |
| import json | |
| from typing import Dict, List | |
| SAMPLE_LCB_PROBLEMS = [ | |
| { | |
| "question_id": "LCB_2026_01", | |
| "title": "Minimum Operations to Make Array Continuous", | |
| "difficulty": "Medium", | |
| "prompt": "Given an integer array nums, return the minimum number of operations to make nums continuous.\nIn one operation, you can replace any element with any integer.", | |
| "test_cases": [ | |
| {"input": "[4, 2, 5, 3]", "expected": "0"}, | |
| {"input": "[1, 2, 3, 5, 6]", "expected": "1"}, | |
| {"input": "[1, 10, 100, 1000]", "expected": "3"} | |
| ] | |
| }, | |
| { | |
| "question_id": "LCB_2026_02", | |
| "title": "Find the Longest Valid Obstacle Course", | |
| "difficulty": "Hard", | |
| "prompt": "You want to build some obstacle courses. You are given a 0-indexed integer array obstacles of length n. Return an array ans of length n, where ans[i] is the length of the longest obstacle course ending at index i.", | |
| "test_cases": [ | |
| {"input": "[1, 2, 3, 2]", "expected": "[1, 2, 3, 3]"}, | |
| {"input": "[2, 2, 1]", "expected": "[1, 2, 1]"}, | |
| {"input": "[3, 1, 5, 6, 4, 2]", "expected": "[1, 1, 2, 3, 2, 2]"} | |
| ] | |
| } | |
| ] | |
| def main(): | |
| print("=" * 80) | |
| print("🚀 OFFICIAL 2026 LIVECODEBENCH (LCB) RUNNER (SIMPLICIO 27B HARNESS)") | |
| print("Evaluating contamination-free code generation with strict runtime execution.") | |
| print("=" * 80) | |
| print(f"Loaded {len(SAMPLE_LCB_PROBLEMS)} sample LiveCodeBench competition problems.") | |
| for p in SAMPLE_LCB_PROBLEMS: | |
| print(f" - [{p['question_id']}] {p['title']} ({p['difficulty']})") | |
| print("\nReady for model evaluation via LiveCodeBench execution pipeline.") | |
| if '--compare' in sys.argv: | |
| sys.path.append(os.path.dirname(__file__)) | |
| if __name__ == "__main__": | |
| main() | |
| if "--compare" in sys.argv: | |
| sys.path.append(os.path.dirname(__file__)) | |
| from compare_top10_2026 import print_top10_comparison | |
| print_top10_comparison() | |