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

Notebooks
Google Colab
Kaggle
Local Apps Settings

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

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

A Qwen3.8-27B fine-tune that answers code-change requests with SEARCH/REPLACE patches instead of whole files.

Overview

Simplicio 27B is a LoRA fine-tune of Qwen3.8-27B by Wesley Simplicio at SimpleTI. It answers a code-change request in five tagged phases (<orient>, <plan>, <patch>, <validate>, <deliver>). The <patch> phase holds SEARCH/REPLACE blocks that touch only the lines that change.

Base model: Qwen3.8-27B, 27.36B parameters, 64 layers that alternate linear attention (DeltaNet) and full attention in a 3:1 pattern.
Files on Hugging Face: the LoRA adapter (0.64 GB), the merged BF16 checkpoint (18 shards, 55.6 GB), and a GGUF Q4_K_M (16.8 GB) with the base model's vision projector (0.93 GB).
Status: research release. It passes 46.7% of the project's own 120 held-out tasks. It has not been scored on public benchmarks.

Results

Held-out set (120 tasks)

The full BF16 model ran on Google Colab G4 (NVIDIA RTX PRO 6000 Blackwell, 95 GB) against 120 tasks that were not used in training (data/unseen_eval_120.json). Each task got one attempt at temperature 0, and generation stopped at </deliver>. A task passes when its unit test passes after the patch is applied. Aggregate results: benchmarks/live_colab_g4_bf16_n120.json.

Metric	Result
Unit test passes	56/120 (46.7%)
SEARCH block found, patch applied	104/120 (86.7%)
Patched file parses (AST)	104/120 (86.7%)
No call to a nonexistent API	120/120
Output tokens per task, mean / max	69.9 / 103
Wall time for all 120 tasks	438 s

Category	Tasks	Unit test passes	Patch applied
Surgical diff and AST precision	30	17	20
Edge-case correctness	30	6	27
Nonexistent and deprecated API traps	30	27	30
Adversarial and out-of-distribution	30	6	27

The base model has not been run under this protocol yet, so the gain from fine-tuning is not measured.

Withdrawn numbers

Earlier versions of this card reported 96.5% accuracy, 116 of 120 tasks passed, 480 tokens per task, a "Top 12" leaderboard and per-token prices. None of these came from running the model on those tasks:

116/120 and its McNemar test come from benchmarks/prove_benchmark_120.py, which simulates model outputs.
480 tokens per task comes from a 3-task smoke test on an A100 (benchmarks/empirical_a100_results.json).
96.5% and the leaderboard rows are hard-coded in benchmarks/compare_top10_2026.py.
The model is not listed on OpenRouter and has no public per-token price.

The held-out run above replaces them.

Public coding-agent benchmarks

Simplicio 27B has not been evaluated on SWE-bench, the Aider benchmark, Terminal-Bench or the Artificial Analysis Coding Agent Index. For scale, these are published Coding Agent Index v1.5 results (Artificial Analysis, retrieved 5 October 2026):

Agent	Coding Agent Index	DeepSWE v1.1	Terminal-Bench 4.0	SWE-Atlas-QnA	Cost per task
Claude Opus 5.5 (max)	66	68%	63%	66%	$13.04
Claude Sonnet 5.5 (max)	68	72%	66%	67%	$14.19
GPT-6.1 Sol (xhigh)	63	73%	55%	61%	$1.04
Simplicio 27B	not evaluated	–	–	–	–

Quick start

Ollama

ollama run wesleysimplicio/simplicio-27b

The latest tag holds the Q4_K_M GGUF and the vision projector. It uses temperature 0.2 and a 32,768-token context, and it stops at <|im_end|> and </deliver>.

The installer installs Ollama if it is missing, then runs the model:

curl -fsSL https://raw.githubusercontent.com/simpletibr/simplicio-27b/main/install.sh | bash

vLLM (OpenAI-compatible server with tool calls)

git clone https://github.com/simpletibr/simplicio-27b
cd simplicio-27b
./deploy/serve_vllm.sh wesleysimplicio/Simplicio-27B 8000

This serves the merged BF16 checkpoint. The weights alone take 55.6 GB; the evaluation above ran on a 95 GB GPU. The script sets:

--max-model-len 40960, defined once in deploy/context.env: a measured 31,692-token OpenCode prompt plus 4,096 output tokens.
--chat-template with deploy/chat_template_chatml.jinja. It prefills <think> so that --reasoning-parser qwen3 moves reasoning out of content.
--enable-auto-tool-choice --tool-call-parser simplicio, using deploy/simplicio_tool_parser.py. It turns <tool><name>…</name><params>…</params></tool> into a single tool_calls entry.
--served-model-name simplicio-27b simpleti/simplicio-27b.

On a smaller GPU, Simplicio_27B_Serve_Colab.ipynb serves the 4-bit base with the LoRA adapter on Colab.

OpenCode

Add the vLLM server to opencode.json as an OpenAI-compatible provider:

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "simplicio": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "Simplicio 27B",
      "options": { "baseURL": "http://localhost:8000/v1" },
      "models": { "simplicio-27b": { "name": "Simplicio 27B" } }
    }
  }
}

opencode -m simplicio/simplicio-27b

OpenCode works through tool calls, so point it at the vLLM server. The Ollama template does not declare tools.

Python (Unsloth)

This loads the adapter on its 4-bit base, the same way Simplicio_27B_Merge_Colab.ipynb does:

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="wesleysimplicio/Simplicio-27B",  # adapter; the base comes from adapter_config.json
    max_seq_length=16384,
    load_in_4bit=True,
)
FastLanguageModel.for_inference(model)

messages = [{"role": "user", "content": "In api/schemas/user.py, accept tax_id with punctuation such as 123.456.789-00."}]
inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to("cuda")
output = model.generate(inputs, max_new_tokens=1024, do_sample=False)
print(tokenizer.decode(output[0][inputs.shape[1]:], skip_special_tokens=True))

Output format

Each phase is a list of numbered points. This example is shortened and translated from the first training example:

<simplicio_loop>
<orient>
[Point 1: Root] Root confirmed at /workspace/api-gateway (pyproject.toml found).
[Point 3: Type signatures] UserCreate.tax_id: str = Field(..., min_length=11, max_length=14).
…
</orient>
<plan>
[Point 11: Atomic steps] Step 1: add a field_validator to the schema. Step 2: run tests/test_users.py.
…
</plan>
<patch>
<<<< SEARCH
    tax_id: str = Field(..., min_length=11, max_length=14)
====
    tax_id: str = Field(..., min_length=11, max_length=11)

    @field_validator("tax_id", mode="before")
    @classmethod
    def sanitize_tax_id(cls, v: str) -> str:
        cleaned = re.sub(r"\D", "", v)
        if len(cleaned) != 11:
            raise ValueError("tax_id must have exactly 11 digits")
        return cleaned
>>>> REPLACE
</patch>
<validate>
[Point 32: Targeted tests] pytest tests/test_users.py -k "tax_id" -> 2 passed
…
</validate>
<deliver>
…
</deliver>
</simplicio_loop>

The SEARCH/REPLACE markers are four characters long (<<<<, ====, >>>>), not the seven that Git and Aider use. To apply a patch, find the SEARCH text verbatim in the file and replace it.

Training

The published adapter was produced by Simplicio_27B_Training_Colab.ipynb. The settings below come from that notebook and the published adapter_config.json.

Setting	Value
Method	QLoRA with Unsloth; base loaded in 4-bit
LoRA	r 32, alpha 32, dropout 0, on the q, k, v, o, gate, up and down projections of every layer
Steps	120 steps, batch size 1, gradient accumulation 8
Optimizer	AdamW 8-bit, learning rate 2e-4, cosine schedule, 10 warmup steps, weight decay 0.01
Sequence length	4,096
Loss	whole sequence, prompt included
Data	101 examples in Portuguese: 1 written by hand and 100 generated from a short list of stack and task templates
Hardware	Google Colab A100 (40 GB)

train_simplicio_27b.py is a script version with extra options: freezing the bottom layers, attention-only LoRA, and registering the phase tags as special tokens. The published adapter used none of them, and its tokenizer has no added tokens. generate_dataset.py writes data/simplicio_loop_50pts_train.jsonl (80 examples) and data/simplicio_loop_50pts_val.jsonl (15 examples).

Limitations

It passes 46.7% of the held-out tasks, and only 6 of 30 in both the edge-case and the adversarial categories.
The training set is small (101 examples), templated and in Portuguese. The model follows the format more reliably than it solves the task.
In the training examples, <validate> and <deliver> contain written-out results such as "2 passed" or "COMMIT_READY". The model writes these without running anything. Treat them as claims and run your own tests.
It has not been compared with the base model under the same protocol, and it has not been run on public benchmarks.
Aider: the patch markers differ from Aider's edit format, and Aider has not been tested.
Vision: the GGUF ships the base model's vision projector. Training was text-only, and image input has not been evaluated.

Repository

Path	Contents
`Simplicio_27B_Training_Colab.ipynb`	Training run that produced the adapter
`Simplicio_27B_Merge_Colab.ipynb`	Merges the adapter into 16-bit weights and exports the GGUF Q4_K_M
`Simplicio_27B_Serve_Colab.ipynb`, `deploy/`	vLLM serving, chat template, tool parser, context length, Ollama `Modelfile`
`data/unseen_eval_120.json`	The 120 held-out tasks
`benchmarks/live_colab_g4_bf16_n120.json`	The results above
`tests/`	Tests for the serving code: `python -m pytest tests`

Citation

@misc{simplicio27b2026,
  author       = {Simplicio, Wesley},
  title        = {Simplicio 27B: a Qwen3.8-27B fine-tune for SEARCH/REPLACE code patches},
  year         = {2026},
  publisher    = {SimpleTI},
  howpublished = {\url{https://huggingface.co/wesleysimplicio/Simplicio-27B}}
}

Model tree for wesleysimplicio/Simplicio-27B

Base model

Qwen/Qwen3.8-27B

Adapter

(153)

this model

Adapters

2 models

wesleysimplicio
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Simplicio-27B

Simplicio 27B

Overview

Results

Held-out set (120 tasks)

Withdrawn numbers

Public coding-agent benchmarks

Quick start

Ollama

vLLM (OpenAI-compatible server with tool calls)

OpenCode

Python (Unsloth)

Output format

Training

Limitations

Repository

Citation

Links

Model tree for wesleysimplicio/Simplicio-27B