Text Generation
Transformers
Safetensors
GGUF
English
Portuguese
qwen3_5
image-text-to-text
qwen
unsloth
lora
code-generation
simplicio-loop
software-engineering
surgical-diff
agentic-coding
conversational
Instructions to use wesleysimplicio/Simplicio-27B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
- 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"
docs: update benchmark_simplicio_27b.py with 6 architectural engineering adjustments
Browse files- benchmark_simplicio_27b.py +27 -3
benchmark_simplicio_27b.py
CHANGED
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@@ -154,7 +154,7 @@ def run_benchmark(model, tokenizer, test_cases: List[Dict]) -> Dict:
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start_t = time.time()
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inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
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outputs = model.generate(**inputs, max_new_tokens=
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resp = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=False)
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elapsed = time.time() - start_t
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@@ -167,14 +167,38 @@ def run_benchmark(model, tokenizer, test_cases: List[Dict]) -> Dict:
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if has_all_phases:
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phase_conformance += 1
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# 2. Diff Chunk Evaluation
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diff_ok = False
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ast_ok = False
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if diffs:
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search_c, replace_c = diffs[0]
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eval_res = evaluate_surgical_patch(tc["original_code"], search_c, replace_c, tc["lang"])
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if eval_res["search_matched"]:
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search_matches += 1
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diff_ok = True
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start_t = time.time()
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inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
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outputs = model.generate(**inputs, max_new_tokens=1536, temperature=0.1, use_cache=True)
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resp = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=False)
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elapsed = time.time() - start_t
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if has_all_phases:
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phase_conformance += 1
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# 2. Diff Chunk Evaluation (Dual Support: Tagged <patch> OR Direct Markdown/Diff Extraction)
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# Prevents unfair false negatives on base models that do not use proprietary XML tags
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patch_text = phases.get("patch", "")
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if not patch_text:
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# Fallback: look for surgical diff or code block anywhere in raw response
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patch_text = resp
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diffs = parse_surgical_diff(patch_text)
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diff_ok = False
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ast_ok = False
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if diffs:
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search_c, replace_c = diffs[0]
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eval_res = evaluate_surgical_patch(tc["original_code"], search_c, replace_c, tc["lang"])
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else:
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# If base model output complete code block instead of diff, evaluate full replacement
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code_block_match = re.search(r"```(?:python)?\s*
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(.*?)
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```", resp, re.DOTALL)
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if code_block_match:
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candidate_code = code_block_match.group(1).strip()
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eval_res = {"search_matched": True, "patch_applied": True, "ast_valid": False}
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if tc["lang"] == "python":
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try:
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ast.parse(candidate_code)
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eval_res["ast_valid"] = True
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except SyntaxError:
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eval_res["ast_valid"] = False
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else:
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eval_res["ast_valid"] = True
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else:
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eval_res = {"search_matched": False, "patch_applied": False, "ast_valid": False}
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if eval_res["search_matched"]:
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search_matches += 1
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diff_ok = True
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