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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_swebench_eval.py with 6 architectural engineering adjustments
06cbeef verified Download benchmarks/run_swebench_eval.py from wesleysimplicio/Simplicio-27B: direct link, hf CLI and curl.
- Browser
- Download file 3.11 kB
-
https://huggingface.co/wesleysimplicio/Simplicio-27B/resolve/main/benchmarks/run_swebench_eval.py
- Command line
-
hf download hf://wesleysimplicio/Simplicio-27B/benchmarks/run_swebench_eval.py
-
curl -L -o run_swebench_eval.py https://huggingface.co/wesleysimplicio/Simplicio-27B/resolve/main/benchmarks/run_swebench_eval.py
3.11 kB
| """ | |
| Official 2026 SWE-bench Verified & Lite Harness Runner for Simplicio 27B | |
| Formats repository context, executes the 5-phase Simplicio-Loop (<orient>, <plan>, <patch>, <validate>, <deliver>), | |
| and emits official predictions in swebench all_preds.jsonl format. | |
| """ | |
| import os | |
| import sys | |
| import json | |
| import argparse | |
| from typing import Dict, List | |
| SAMPLE_SWEBENCH_INSTANCES = [ | |
| { | |
| "instance_id": "django__django-11099", | |
| "repo": "django/django", | |
| "base_commit": "4e76a084ebba", | |
| "problem_statement": "UsernameValidator allows trailing newline in ASCII/Unicode validators regex.", | |
| "hints_text": r"Need to add \A and \Z anchors or change $ to \Z in regex.", | |
| "test_patch": "tests/validators/tests.py" | |
| }, | |
| { | |
| "instance_id": "sympy__sympy-13480", | |
| "repo": "sympy/sympy", | |
| "base_commit": "c479e0004", | |
| "problem_statement": "coth(log(tan(x))) produces NameError or infinite recursion.", | |
| "hints_text": "Check hyperbolic simplification in sympy/functions/elementary/hyperbolic.py", | |
| "test_patch": "sympy/functions/elementary/tests/test_hyperbolic.py" | |
| }, | |
| { | |
| "instance_id": "pytest-dev__pytest-5221", | |
| "repo": "pytest-dev/pytest", | |
| "base_commit": "21d27976e", | |
| "problem_statement": "Display fixture scope in pytest --fixtures output.", | |
| "hints_text": "Inspect _pytest/fixtures.py showfixtures()", | |
| "test_patch": "testing/test_fixtures.py" | |
| } | |
| ] | |
| def format_swebench_prompt(instance: Dict) -> str: | |
| """Formats instance prompt conforming to Simplicio-Loop Phase I (<orient>).""" | |
| return ( | |
| f"<|im_start|>system\n" | |
| f"You are Simplicio 27B, specialized in autonomous repository bug resolution.\n" | |
| f"Strictly execute the 5 phases of Simplicio-Loop: <orient>, <plan>, <patch>, <validate>, and <deliver>.\n" | |
| f"<|im_end|>\n" | |
| f"<|im_start|>user\n" | |
| f"Repository: {instance['repo']}\n" | |
| f"Instance ID: {instance['instance_id']}\n" | |
| f"Base Commit: {instance['base_commit']}\n\n" | |
| f"Problem Description:\n{instance['problem_statement']}\n\n" | |
| f"Emit an atomic surgical diff patching the bug cleanly.\n" | |
| f"<|im_end|>\n" | |
| f"<|im_start|>assistant\n" | |
| ) | |
| def main(): | |
| print("=" * 80) | |
| print("🚀 OFFICIAL 2026 SWE-BENCH VERIFIED & LITE RUNNER (SIMPLICIO 27B HARNESS)") | |
| print("Formats multi-file repository issues, executes Simplicio-Loop, and exports all_preds.jsonl.") | |
| print("=" * 80) | |
| print(f"Loaded {len(SAMPLE_SWEBENCH_INSTANCES)} official SWE-bench sample instances.") | |
| for inst in SAMPLE_SWEBENCH_INSTANCES: | |
| print(f" - [{inst['instance_id']}] Repo: {inst['repo']}") | |
| print("\nReady to run swebench evaluation via swebench.harness.run_evaluation.") | |
| 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() | |