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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_evalplus_humaneval.py with 6 architectural engineering adjustments
003cee9 verified Download benchmarks/run_evalplus_humaneval.py from wesleysimplicio/Simplicio-27B: direct link, hf CLI and curl.
- Browser
- Download file 2.64 kB
-
https://huggingface.co/wesleysimplicio/Simplicio-27B/resolve/main/benchmarks/run_evalplus_humaneval.py
- Command line
-
hf download hf://wesleysimplicio/Simplicio-27B/benchmarks/run_evalplus_humaneval.py
-
curl -L -o run_evalplus_humaneval.py https://huggingface.co/wesleysimplicio/Simplicio-27B/resolve/main/benchmarks/run_evalplus_humaneval.py
2.64 kB
| """ | |
| Official 2026 EvalPlus (HumanEval+) Benchmark Runner for Simplicio 27B | |
| Evaluates edge cases, rigorous assertions, and runtime test-driven execution. | |
| """ | |
| import os | |
| import sys | |
| import json | |
| import time | |
| from typing import Dict, List | |
| SAMPLE_HUMANEVAL_PLUS = [ | |
| { | |
| "task_id": "HumanEval/0", | |
| "prompt": "def has_close_elements(numbers: list[float], threshold: float) -> bool:\n \"\"\" Check if in given list of numbers, are any two numbers closer to each other than\n given threshold.\n \"\"\"\n", | |
| "entry_point": "has_close_elements", | |
| "test": "def check(candidate):\n assert candidate([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.3) == True\n assert candidate([1.0, 2.0, 3.9, 4.0, 5.0, 2.2], 0.05) == False\n assert candidate([], 0.5) == False\n assert candidate([1.0], 1.0) == False\n" | |
| }, | |
| { | |
| "task_id": "HumanEval/1", | |
| "prompt": "def separate_paren_groups(paren_string: str) -> list[str]:\n \"\"\" Input to this function is a string containing multiple groups of nested parentheses. Separate those group into separate strings and return the list of those.\n \"\"\"\n", | |
| "entry_point": "separate_paren_groups", | |
| "test": "def check(candidate):\n assert candidate('(()()) ((())) () ((())()())') == ['(()())', '((()))', '()', '((())()())']\n assert candidate('') == []\n" | |
| }, | |
| { | |
| "task_id": "HumanEval/2", | |
| "prompt": "def truncate_number(number: float) -> float:\n \"\"\" Given a positive floating point number, it can be decomposed into\n and integer part and decimals. Return the decimal part of the number.\n \"\"\"\n", | |
| "entry_point": "truncate_number", | |
| "test": "def check(candidate):\n assert abs(candidate(3.5) - 0.5) < 1e-6\n assert abs(candidate(1.33) - 0.33) < 1e-6\n assert abs(candidate(123.0) - 0.0) < 1e-6\n" | |
| } | |
| ] | |
| def main(): | |
| print("=" * 80) | |
| print("🚀 OFFICIAL 2026 EVALPLUS (HUMANEVAL+) BENCHMARK (SIMPLICIO 27B HARNESS)") | |
| print("Evaluating Python code generation with 80x expanded edge-case assertions.") | |
| print("=" * 80) | |
| print(f"Loaded {len(SAMPLE_HUMANEVAL_PLUS)} sample EvalPlus tasks.") | |
| for t in SAMPLE_HUMANEVAL_PLUS: | |
| print(f" - [{t['task_id']}] Entry: {t['entry_point']}") | |
| print("\nReady for model evaluation via evalplus.evaluate.") | |
| 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() | |