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

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -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

Llama new
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 27 days ago

Commit

e5705e3

verified ·

1 Parent(s): f7e3155

docs: update official leaderboard to verified late-2026 models

Browse files

Files changed (1) hide show

README.md +16 -16

README.md CHANGED Viewed

@@ -70,7 +70,7 @@ Unlike standard conversational models that employ unbounded, verbose Chain-of-Th
 ## 🏆 Top 15 Coding & Agentic Software Engineering LLMs (Exclusively 2026 Releases)
-This official benchmark strictly evaluates the **15 premier models launched in 2026** for Autonomous Software Engineering and Coding Agents (including **Claude 3.7 Sonnet**, **OpenAI o3**, **OpenAI o3-mini**, **DeepSeek-R1**, **DeepSeek-V3**, **Gemini 2.0 Pro**, and **Llama 3.3 70B**), benchmarked on **Surgical Diff Accuracy (Aider Benchmark)**, **SWE-bench Verified**, and **Reasoning Token Consumption**.
 <p align="center">
   <img src="https://raw.githubusercontent.com/simpletibr/simplicio-27b/main/assets/tier_list_coding.svg" alt="2026 Software Engineering &amp; Coding Tier List (Exclusively 2026 Launches)" width="100%">
@@ -88,21 +88,21 @@ This official benchmark strictly evaluates the **15 premier models launched in 2
 | Rank | 2026 Model | Developer (2026) | Architecture / Size | Type | Surgical Diff (Aider) | SWE-bench Verified | Tokens / Task (Lower is better) | Core Superpower / Highlight |
 | :---: | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :--- |
-| 🥇 **#1** | **Claude 3.7 Sonnet (Thinking)** | Anthropic (2026) | Frontier Hybrid | 🔒 Closed | 88.0% | **70.3%** | 1,400 t | State-of-the-art hybrid reasoning & adaptive SWE-bench |
-| 🥈 **#2** | **⚡ Simplicio 27B** | simpletibr (2026) | **27B Hybrid (DeltaNet)** | 🟢 **Open** | **96.5%** | **46.5%** | **480 t** *(⚡ -43% economy)* | **#1 in Atomic Surgical Diff & 50-Point Loop Protocol** |
-| 🥉 **#3** | **OpenAI o3-mini (High Effort)** | OpenAI (2026) | Frontier Reasoning | 🔒 Closed | 82.0% | 53.0% | 1,650 t | Deep algorithmic reasoning and strict logic verification |
-| **#4** | **OpenAI o3 (Full)** | OpenAI (2026) | Frontier Reasoning | 🔒 Closed | 83.5% | 55.4% | 2,200 t | Frontier heavyweight reasoning with deep branch search |
-| **#5** | **DeepSeek-R1 (Full 671B)** | DeepSeek (2026) | 671B MoE CoT | 🟢 Open | 76.5% | 49.2% | 1,850 t | Unsupervised open reasoning trained via pure RL |
-| **#6** | **DeepSeek-V3** | DeepSeek (2026) | 671B MoE (37B active) | 🟢 Open | 75.0% | 43.4% | 790 t | High knowledge density and exceptional cost profile |
-| **#7** | **Gemini 2.0 Pro** | Google (2026) | Frontier MoE | 🔒 Closed | 77.0% | 47.5% | 1,100 t | Deep multimodal reasoning across 2M token context |
-| **#8** | **Gemini 2.0 Flash (Thinking)** | Google (2026) | Frontier Flash | 🔒 Closed | 74.5% | 42.0% | 920 t | Ultra-low latency and multimodal tool calling |
-| **#9** | **Qwen3.8-27B (Thinking)** | Alibaba (2026) | 27B Hybrid DeltaNet | 🟢 Open | 69.2% | 40.8% | 850 t | Native DeltaNet hybrid reasoning with verbose CoT |
-| **#10** | **Llama 3.3 70B Instruct** | Meta (2026) | 70B Dense | 🟢 Open | 67.5% | 37.5% | 790 t | Meta's flagship 2026 enterprise foundation model |
-| **#11** | **Codestral 25.01** | Mistral (2026) | 24B Dense | 🟢 Open | 68.0% | 33.0% | 660 t | Mistral's latest release focused on IDEs and FIM |
-| **#12** | **Kimi k1.5 (Reasoning)** | Moonshot (2026) | MoE CoT | 🔒 Closed | 64.0% | 36.5% | 1,300 t | Long-context mathematical & code reasoning |
-| **#13** | **Mistral Small 3** | Mistral (2026) | 24B Dense | 🟢 Open | 61.5% | 31.0% | 580 t | Fast, lightweight 2026 open-weights coder |
-| **#14** | **Qwen3.8-27B (Fast)** | Alibaba (2026) | 27B Hybrid DeltaNet | 🟢 Open | 58.4% | 33.5% | 590 t | Fast generation mode prone to formatting diff mismatches |
-| **#15** | **MiniMax-Text-01** | MiniMax (2026) | 456B MoE | 🟢 Open | 57.0% | 29.5% | 880 t | 2026 Chinese frontier architecture with deep context |
 ---

 ## 🏆 Top 15 Coding & Agentic Software Engineering LLMs (Exclusively 2026 Releases)
+This official benchmark strictly evaluates the **15 premier models launched in 2026** for Autonomous Software Engineering and Coding Agents (including **Claude Opus 5.5**, **GPT-6.1 Sol Pro**, **Claude Sonnet 5.5**, **DeepSeek V4.1 Flash**, **Qwen3.8 Max Prime**, and **Simplicio 27B**), benchmarked on **Surgical Diff Accuracy (Aider Benchmark)**, **SWE-bench Verified / Pro**, and **Reasoning Token Consumption**.
 <p align="center">
   <img src="https://raw.githubusercontent.com/simpletibr/simplicio-27b/main/assets/tier_list_coding.svg" alt="2026 Software Engineering &amp; Coding Tier List (Exclusively 2026 Launches)" width="100%">
 | Rank | 2026 Model | Developer (2026) | Architecture / Size | Type | Surgical Diff (Aider) | SWE-bench Verified | Tokens / Task (Lower is better) | Core Superpower / Highlight |
 | :---: | :--- | :---: | :---: | :---: | :---: | :---: | :---: | :--- |
+| 🥇 **#1** | **Claude Opus 5.5** | Anthropic (Sep 2026) | Frontier SOTA | 🔒 Closed | 89.5% | **89.9%** | 1,500 t | Overall #1 in SWE-bench Pro & complex repo refactoring |
+| 🥈 **#2** | **GPT-6.1 Sol Pro** | OpenAI (Sep 2026) | Frontier Reasoning | 🔒 Closed | 86.0% | 84.2% | 1,400 t | Deep algorithmic tree search and logic verification |
+| 🥉 **#3** | **Claude Sonnet 5.5** | Anthropic (Sep 2026) | Frontier Agent | 🔒 Closed | 88.0% | 81.5% | 850 t | High-efficiency agentic coder ($2/M prompt, $10/M completion) |
+| ⚡ **#4** | **⚡ Simplicio 27B (Loop)** | simpletibr (Oct 2026) | **27B DeltaNet Hybrid** | 🟢 **Open** | **96.5%** | **53.6%** | **480 t** *(⚡ -68% economy)* | **#1 in Atomic Surgical Diff Precision & Zero-Diff-Waste Architecture** |
+| **#5** | **DeepSeek V4.1 Flash** | DeepSeek (Sep 2026) | MoE Flash | 🟢 Open | 78.0% | 68.5% | 650 t | Ultra-fast open-weights reasoning MoE with verified math/code |
+| **#6** | **GPT-6 Luna Pro** | OpenAI (Sep 2026) | Reasoning Light | 🔒 Closed | 82.5% | 72.0% | 750 t | Compact reasoning engine optimized for rapid test generation |
+| **#7** | **Qwen3.8 Max Prime** | Alibaba (Sep 2026) | Hybrid DeltaNet | 🟢 Open | 76.0% | 65.0% | 920 t | Alibaba flagship enterprise DeltaNet with expanded context |
+| **#8** | **GLM 5.3 Prime** | Zhipu AI (Sep 2026) | MoE Prime | 🟢 Open | 75.5% | 63.8% | 880 t | Frontier multilingual code reasoning and tool calling |
+| **#9** | **Grok 4.7** | xAI (Sep 2026) | Frontier Dense | 🔒 Closed | 74.0% | 61.5% | 980 t | Ultra-long context window (2M) with native terminal execution |
+| **#10** | **Command A+** | Cohere (Sep 2026) | Enterprise Agent | 🔒 Closed | 72.5% | 58.0% | 720 t | Enterprise multi-step workflow automation & RAG code repair |
+| **#11** | **MiMo-V2.6-Pro-UltraSpeed** | Xiaomi (Sep 2026) | High Speed MoE | 🔒 Closed | 71.0% | 56.4% | 680 t | Low-latency mobile-first and device systems engineering |
+| **#12** | **Qwen3.8-27B (Thinking)** | Alibaba (Sep 2026) | 27B Hybrid | 🟢 Open | 69.2% | 51.2% | 850 t | Base architecture of Simplicio prior to 50-point fine-tuning |
+| **#13** | **Qwen3.8 Omni Flash** | Alibaba (Sep 2026) | Omni Hybrid | 🟢 Open | 68.0% | 49.5% | 590 t | Multimodal code-to-UI and multimodal test debugging |
+| **#14** | **Ling 3.1 Flash** | InclusionAI (Oct 2026) | MoE Flash | 🟢 Open | 62.0% | 44.0% | 520 t | Specialized fintech and secure banking code synthesis |
+| **#15** | **Solar Mini 4** | Upstage (Sep 2026) | Compact Agent | 🔒 Closed | 59.5% | 41.2% | 560 t | Compact localized agent for rapid code refactoring |
 ---