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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"
benchmarks: add aider_instances.json
Browse files
benchmarks/aider_instances.json
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[
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{
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"instance_id": "aider_two_fer",
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"file": "two_fer.py",
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"instructions": "Implement two_fer(name=None) returning \"One for {name}, one for me.\", defaulting to \"you\".",
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"initial_code": "def two_fer(name=None):\n pass\n",
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"test_code": "from two_fer import two_fer\ndef test_no_name(): assert two_fer() == \"One for you, one for me.\"\ndef test_name(): assert two_fer(\"Alice\") == \"One for Alice, one for me.\"\n"
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},
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{
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"instance_id": "aider_isogram",
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"file": "isogram.py",
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"instructions": "Determine if a word or phrase is an isogram, ignoring spaces and hyphens.",
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"initial_code": "def is_isogram(string):\n pass\n",
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"test_code": "from isogram import is_isogram\ndef test_empty(): assert is_isogram(\"\") is True\ndef test_iso(): assert is_isogram(\"lumberjacks\") is True\ndef test_dup(): assert is_isogram(\"eleven\") is False\n"
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},
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{
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"instance_id": "aider_raindrops",
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"file": "raindrops.py",
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"instructions": "Convert factor to raindrop sounds: 3=Pling, 5=Plang, 7=Plong, else number as string.",
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"initial_code": "def convert(number):\n pass\n",
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"test_code": "from raindrops import convert\ndef test_28(): assert convert(28) == \"Plong\"\ndef test_30(): assert convert(30) == \"PlingPlang\"\ndef test_34(): assert convert(34) == \"34\"\n"
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},
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{
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"instance_id": "aider_flatten_array",
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"file": "flatten_array.py",
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"instructions": "Flatten a nested list omitting all None values.",
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"initial_code": "def flatten(iterable):\n pass\n",
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"test_code": "from flatten_array import flatten\ndef test_empty(): assert flatten([]) == []\ndef test_nested(): assert flatten([0, 2, [[2, 3], 8, 100, 4, [[[50]]]], -2]) == [0, 2, 2, 3, 8, 100, 4, 50, -2]\n"
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},
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{
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"instance_id": "aider_resistor_color",
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"file": "resistor_color.py",
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"instructions": "Return resistance band number and list of colors.",
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"initial_code": "def color_code(color):\n pass\n\ndef colors():\n pass\n",
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"test_code": "from resistor_color import color_code, colors\ndef test_black(): assert color_code(\"black\") == 0\ndef test_white(): assert color_code(\"white\") == 9\ndef test_list(): assert colors()[0] == \"black\"\n"
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}
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]
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