Instructions to use rodrigoramosrs/veriloop-coder-e1-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rodrigoramosrs/veriloop-coder-e1-gguf with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="rodrigoramosrs/veriloop-coder-e1-gguf")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rodrigoramosrs/veriloop-coder-e1-gguf", device_map="auto")

llama-cpp-python

How to use rodrigoramosrs/veriloop-coder-e1-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="rodrigoramosrs/veriloop-coder-e1-gguf",
	filename="LoopCoder-Qwen3.6-27B-BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use rodrigoramosrs/veriloop-coder-e1-gguf 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 rodrigoramosrs/veriloop-coder-e1-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf rodrigoramosrs/veriloop-coder-e1-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf rodrigoramosrs/veriloop-coder-e1-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf rodrigoramosrs/veriloop-coder-e1-gguf:Q4_K_M

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 rodrigoramosrs/veriloop-coder-e1-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf rodrigoramosrs/veriloop-coder-e1-gguf:Q4_K_M

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 rodrigoramosrs/veriloop-coder-e1-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf rodrigoramosrs/veriloop-coder-e1-gguf:Q4_K_M

Use Docker

docker model run hf.co/rodrigoramosrs/veriloop-coder-e1-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use rodrigoramosrs/veriloop-coder-e1-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rodrigoramosrs/veriloop-coder-e1-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rodrigoramosrs/veriloop-coder-e1-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rodrigoramosrs/veriloop-coder-e1-gguf:Q4_K_M

SGLang

How to use rodrigoramosrs/veriloop-coder-e1-gguf 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 "rodrigoramosrs/veriloop-coder-e1-gguf" \
    --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": "rodrigoramosrs/veriloop-coder-e1-gguf",
		"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 "rodrigoramosrs/veriloop-coder-e1-gguf" \
        --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": "rodrigoramosrs/veriloop-coder-e1-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use rodrigoramosrs/veriloop-coder-e1-gguf with Ollama:
```
ollama run hf.co/rodrigoramosrs/veriloop-coder-e1-gguf:Q4_K_M
```

Unsloth Studio

How to use rodrigoramosrs/veriloop-coder-e1-gguf with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for rodrigoramosrs/veriloop-coder-e1-gguf to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for rodrigoramosrs/veriloop-coder-e1-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for rodrigoramosrs/veriloop-coder-e1-gguf to start chatting

How to use rodrigoramosrs/veriloop-coder-e1-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf rodrigoramosrs/veriloop-coder-e1-gguf:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "rodrigoramosrs/veriloop-coder-e1-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use rodrigoramosrs/veriloop-coder-e1-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf rodrigoramosrs/veriloop-coder-e1-gguf:Q4_K_M

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 rodrigoramosrs/veriloop-coder-e1-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use rodrigoramosrs/veriloop-coder-e1-gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf rodrigoramosrs/veriloop-coder-e1-gguf:Q4_K_M

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 "rodrigoramosrs/veriloop-coder-e1-gguf:Q4_K_M" \
  --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"

Docker Model Runner
How to use rodrigoramosrs/veriloop-coder-e1-gguf with Docker Model Runner:
```
docker model run hf.co/rodrigoramosrs/veriloop-coder-e1-gguf:Q4_K_M
```

Lemonade

How to use rodrigoramosrs/veriloop-coder-e1-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull rodrigoramosrs/veriloop-coder-e1-gguf:Q4_K_M

Run and chat with the model

lemonade run user.veriloop-coder-e1-gguf-Q4_K_M

List all available models

lemonade list

VeriLoop Coder-E1 · GGUF

Coding-Optimized Quantized Models

Original Model ↗ · GitHub · Apache-2.0

Overview

This repository contains GGUF quantizations of VeriLoop Coder-E1, an open-source vertical coding model built on Qwen3.6-27B. The original model introduces the Self-Harness paradigm — an evidence-bound execution substrate that turns model generation into a recursive engineering loop of falsification, exploration, and repair.

Quantized by Rodrigo Ramos.

Quantization Approach

All quants were produced with llama.cpp using a code-specialized importance matrix (imatrix). Unlike generic imatrix datasets, this one was curated from software engineering corpora — repository-level code, patches, test suites, and agentic coding traces — ensuring that quantization preserves fidelity on the distributions that matter most for coding tasks.

The result is a set of GGUF files that retain the original model's strong software-engineering capabilities while being deployable via llama.cpp, llama-cpp-python, Ollama, LM Studio, and other GGUF-compatible runtimes.

Available Quants

File	Quant Type	Notes
`LoopCoder-Qwen3.6-27B-BF16.gguf`	BF16	Full-precision reference
`LoopCoder-Qwen3.6-27B-Q8_0.gguf`	Q8_0	High quality, larger file
`LoopCoder-Qwen3.6-27B-Q6_K.gguf`	Q6_K	Excellent quality / size trade-off
`LoopCoder-Qwen3.6-27B-Q5_K_M.gguf`	Q5_K_M	Strong quality, reduced size
`LoopCoder-Qwen3.6-27B-Q4_K_M.gguf`	Q4_K_M	Balanced quality / size
`LoopCoder-Qwen3.6-27B-Q3_K_M.gguf`	Q3_K_M	Smaller, good for limited RAM
`LoopCoder-Qwen3.6-27B-IQ4_XS.gguf`	IQ4_XS	Extra-small 4-bit
`LoopCoder-Qwen3.6-27B-IQ3_XS.gguf`	IQ3_XS	Extra-small 3-bit

Usage

llama.cpp

./llama-cli \
  -m LoopCoder-Qwen3.6-27B-Q4_K_M.gguf \
  -p "Your coding prompt here" \
  -n 2048 \
  -t 8

llama-cpp-python

from llama_cpp import Llama

llm = Llama(
    model_path="LoopCoder-Qwen3.6-27B-Q4_K_M.gguf",
    n_ctx=32768,
    n_threads=8,
)

output = llm(
    "Write a Python function to merge two sorted lists.",
    max_tokens=1024,
    temperature=0.2,
)
print(output["choices"][0]["text"])

Ollama

ollama modelfile from ./LoopCoder-Qwen3.6-27B-Q4_K_M.gguf
ollama create veriloop-coder-e1:q4_k_m -f Modelfile
ollama run veriloop-coder-e1:q4_k_m

Acknowledgements

Libo Wang and the Intelligent Robotics Laboratory, Tsinghua SIGS for developing the original VeriLoop Coder-E1 model.
The llama.cpp community for the quantization and inference tooling.
The original model repository: tsinghua-sigs-robot-lab/veriloop-coder-e1

License

Apache-2.0. The weights are quantized from the original Apache-2.0 licensed model. See the original repository for full licensing details and third-party notices.

Downloads last month: -

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for rodrigoramosrs/veriloop-coder-e1-gguf

Base model

Qwen/Qwen3.6-27B

Finetuned

tsinghua-sigs-robot-lab/veriloop-coder-e1

Quantized

(1)

this model