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 /run_aider_benchmark.py

wesleysimplicio

docs: sync benchmarks/run_aider_benchmark.py with N=120 scientific proof and market charts

47e3e54 verified 20 days ago

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2.45 kB

# Official 2026 Aider Code Editing Benchmark Runner for Simplicio 27B
import os
import re
import ast
import sys
import json
import tempfile
import subprocess
from typing import List, Dict, Tuple

def load_instances() -> List[Dict]:
    data_path = os.path.join(os.path.dirname(__file__), "aider_instances.json")
    with open(data_path, "r", encoding="utf-8") as f:
        return json.load(f)

def parse_surgical_diff(patch_content: str) -> List[Tuple[str, str]]:
    pattern = r"<<<< SEARCH\s*\n(.*?)\n====\s*\n(.*?)\n>>>> REPLACE"
    return re.findall(pattern, patch_content, re.DOTALL)

def evaluate_patch(initial: str, search_c: str, replace_c: str, test_code: str, fname: str) -> Dict:
    res = {"search_matched": False, "ast_valid": False, "test_passed": False}
    c_init = initial.replace("\r\n", "\n").strip()
    c_search = search_c.replace("\r\n", "\n").strip()
    if c_search in c_init:
        res["search_matched"] = True
        patched = c_init.replace(c_search, replace_c.strip())
        try:
            ast.parse(patched)
            res["ast_valid"] = True
        except SyntaxError:
            return res
        with tempfile.TemporaryDirectory() as tmpdir:
            with open(os.path.join(tmpdir, fname), "w", encoding="utf-8") as sf:
                sf.write(patched)
            t_path = os.path.join(tmpdir, "test_" + fname)
            with open(t_path, "w", encoding="utf-8") as tf:
                tf.write(test_code)
            p = subprocess.run([sys.executable, "-m", "pytest", t_path, "-q"], cwd=tmpdir, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
            if p.returncode == 0:
                res["test_passed"] = True
    return res

def main():
    cases = load_instances()
    print("=" * 80)
    print("🚀 OFFICIAL 2026 AIDER CODE EDITING BENCHMARK (SIMPLICIO 27B HARNESS)")
    print("Evaluating Exercism testbed with atomic diff editing and pytest execution.")
    print("=" * 80)
    print(f"Loaded {len(cases)} official benchmark instances:")
    for c in cases:
        print(f"  - [{c['instance_id']}] Target: {c['file']}")
    print("\nReady for model inference and 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()

	# Official 2026 Aider Code Editing Benchmark Runner for Simplicio 27B
	import os
	import re
	import ast
	import sys
	import json
	import tempfile
	import subprocess
	from typing import List, Dict, Tuple

	def load_instances() -> List[Dict]:
	data_path = os.path.join(os.path.dirname(__file__), "aider_instances.json")
	with open(data_path, "r", encoding="utf-8") as f:
	return json.load(f)

	def parse_surgical_diff(patch_content: str) -> List[Tuple[str, str]]:
	pattern = r"<<<< SEARCH\s\n(.?)\n====\s\n(.?)\n>>>> REPLACE"
	return re.findall(pattern, patch_content, re.DOTALL)

	def evaluate_patch(initial: str, search_c: str, replace_c: str, test_code: str, fname: str) -> Dict:
	res = {"search_matched": False, "ast_valid": False, "test_passed": False}
	c_init = initial.replace("\r\n", "\n").strip()
	c_search = search_c.replace("\r\n", "\n").strip()
	if c_search in c_init:
	res["search_matched"] = True
	patched = c_init.replace(c_search, replace_c.strip())
	try:
	ast.parse(patched)
	res["ast_valid"] = True
	except SyntaxError:
	return res
	with tempfile.TemporaryDirectory() as tmpdir:
	with open(os.path.join(tmpdir, fname), "w", encoding="utf-8") as sf:
	sf.write(patched)
	t_path = os.path.join(tmpdir, "test_" + fname)
	with open(t_path, "w", encoding="utf-8") as tf:
	tf.write(test_code)
	p = subprocess.run([sys.executable, "-m", "pytest", t_path, "-q"], cwd=tmpdir, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
	if p.returncode == 0:
	res["test_passed"] = True
	return res

	def main():
	cases = load_instances()
	print("=" * 80)
	print("🚀 OFFICIAL 2026 AIDER CODE EDITING BENCHMARK (SIMPLICIO 27B HARNESS)")
	print("Evaluating Exercism testbed with atomic diff editing and pytest execution.")
	print("=" * 80)
	print(f"Loaded {len(cases)} official benchmark instances:")
	for c in cases:
	print(f" - [{c['instance_id']}] Target: {c['file']}")
	print("\nReady for model inference and 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()