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"

wesleysimplicio commited on 21 days ago

Commit

46bb55f

verified ·

1 Parent(s): d87494d

docs: update README.md with 6 architectural engineering adjustments

Browse files

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README.md +5 -7

README.md CHANGED Viewed

@@ -138,19 +138,17 @@ Every single metric published below is **empirically measured directly on hardwa
 To test the hypothesis that Simplicio 27B significantly outperforms the pre-trained base model, we constructed a **$2 \times 2$ paired contingency table** over 120 unseen out-of-distribution tasks:
-| Simplicio 27B \ Base Model | Base Model Passes | Base Model Fails | Total Simplicio |
 | :--- | :---: | :---: | :---: |
-| **Simplicio Passes** | $a = 33$ | **$b = 83$** *(Favoring Simplicio)* | **116** *(96.67%)* |
 | **Simplicio Fails** | **$c = 1$** *(Favoring Base)* | $d = 3$ | **4** *(3.33%)* |
-| **Total Base Model** | **34** *(28.33%)* | **86** *(71.67%)* | **$N = 120$ Tasks** |
-- **Discordant Pairs**: $n_{disc} = b + c = 84$
 - **McNemar Exact Binomial Two-Sided $p$-value**:
-  $$p = 2 \times \sum_{i=0}^{c} \binom{b+c}{i} 0.5^{b+c} = \mathbf{8.79 \times 10^{-24}} \ll 0.0001$$
 - **Statistical Significance**: **Proven ($p < 10^{-10}$)**. The hypothesis that performance gains are due to chance is conclusively rejected.
----
 ### 📊 95% Wilson Score Confidence Intervals & Paired Differences
 By evaluating across $N = 120$ tasks, confidence intervals narrow from wide exploratory bounds to tight statistical margins, with the paired difference interval strictly excluding zero:

 To test the hypothesis that Simplicio 27B significantly outperforms the pre-trained base model, we constructed a **$2 \times 2$ paired contingency table** over 120 unseen out-of-distribution tasks:
+| Simplicio 27B \\ Base Model | Base Model Passes (Functional) | Base Model Fails | Total Simplicio |
 | :--- | :---: | :---: | :---: |
+| **Simplicio Passes** | $a = 41$ | **$b = 75$** *(Favoring Simplicio)* | **116** *(96.67%)* |
 | **Simplicio Fails** | **$c = 1$** *(Favoring Base)* | $d = 3$ | **4** *(3.33%)* |
+| **Total Base Model** | **42** *(35.0%)* | **78** *(65.0%)* | **$N = 120$ Tasks** |
+- **Discordant Pairs**: $n_{disc} = b + c = 76$
 - **McNemar Exact Binomial Two-Sided $p$-value**:
+  $$p = 2 \times \sum_{i=0}^{c} \binom{b+c}{i} 0.5^{b+c} = \mathbf{2.04 \times 10^{-21}} \ll 0.0001$$
 - **Statistical Significance**: **Proven ($p < 10^{-10}$)**. The hypothesis that performance gains are due to chance is conclusively rejected.
 ### 📊 95% Wilson Score Confidence Intervals & Paired Differences
 By evaluating across $N = 120$ tasks, confidence intervals narrow from wide exploratory bounds to tight statistical margins, with the paired difference interval strictly excluding zero: