Instructions to use wesleysimplicio/Simplicio-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wesleysimplicio/Simplicio-27B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="wesleysimplicio/Simplicio-27B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("wesleysimplicio/Simplicio-27B")
model = AutoModelForMultimodalLM.from_pretrained("wesleysimplicio/Simplicio-27B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

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

SGLang

How to use wesleysimplicio/Simplicio-27B 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 "wesleysimplicio/Simplicio-27B" \
    --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": "wesleysimplicio/Simplicio-27B",
		"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 "wesleysimplicio/Simplicio-27B" \
        --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": "wesleysimplicio/Simplicio-27B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use wesleysimplicio/Simplicio-27B with Ollama:
```
ollama run hf.co/wesleysimplicio/Simplicio-27B:BF16
```
Unsloth Desktop

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"

Simplicio-27B / benchmarks /aider_instances.json

wesleysimplicio

benchmarks: add aider_instances.json

018704e verified 22 days ago

Raw History Blame Contribute Delete

2.22 kB

[
  {
    "instance_id": "aider_two_fer",
    "file": "two_fer.py",
    "instructions": "Implement two_fer(name=None) returning \"One for {name}, one for me.\", defaulting to \"you\".",
    "initial_code": "def two_fer(name=None):\n    pass\n",
    "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"
  },
  {
    "instance_id": "aider_isogram",
    "file": "isogram.py",
    "instructions": "Determine if a word or phrase is an isogram, ignoring spaces and hyphens.",
    "initial_code": "def is_isogram(string):\n    pass\n",
    "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"
  },
  {
    "instance_id": "aider_raindrops",
    "file": "raindrops.py",
    "instructions": "Convert factor to raindrop sounds: 3=Pling, 5=Plang, 7=Plong, else number as string.",
    "initial_code": "def convert(number):\n    pass\n",
    "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"
  },
  {
    "instance_id": "aider_flatten_array",
    "file": "flatten_array.py",
    "instructions": "Flatten a nested list omitting all None values.",
    "initial_code": "def flatten(iterable):\n    pass\n",
    "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"
  },
  {
    "instance_id": "aider_resistor_color",
    "file": "resistor_color.py",
    "instructions": "Return resistance band number and list of colors.",
    "initial_code": "def color_code(color):\n    pass\n\ndef colors():\n    pass\n",
    "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"
  }
]

	[
	{
	"instance_id": "aider_two_fer",
	"file": "two_fer.py",
	"instructions": "Implement two_fer(name=None) returning \"One for {name}, one for me.\", defaulting to \"you\".",
	"initial_code": "def two_fer(name=None):\n pass\n",
	"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"
	},
	{
	"instance_id": "aider_isogram",
	"file": "isogram.py",
	"instructions": "Determine if a word or phrase is an isogram, ignoring spaces and hyphens.",
	"initial_code": "def is_isogram(string):\n pass\n",
	"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"
	},
	{
	"instance_id": "aider_raindrops",
	"file": "raindrops.py",
	"instructions": "Convert factor to raindrop sounds: 3=Pling, 5=Plang, 7=Plong, else number as string.",
	"initial_code": "def convert(number):\n pass\n",
	"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"
	},
	{
	"instance_id": "aider_flatten_array",
	"file": "flatten_array.py",
	"instructions": "Flatten a nested list omitting all None values.",
	"initial_code": "def flatten(iterable):\n pass\n",
	"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"
	},
	{
	"instance_id": "aider_resistor_color",
	"file": "resistor_color.py",
	"instructions": "Return resistance band number and list of colors.",
	"initial_code": "def color_code(color):\n pass\n\ndef colors():\n pass\n",
	"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"
	}
	]