Instructions to use rodrigoramosrs/veriloop-coder-e2-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rodrigoramosrs/veriloop-coder-e2-gguf with Transformers:

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

pipe = pipeline("text-generation", model="rodrigoramosrs/veriloop-coder-e2-gguf")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("rodrigoramosrs/veriloop-coder-e2-gguf", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use rodrigoramosrs/veriloop-coder-e2-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-e2-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf rodrigoramosrs/veriloop-coder-e2-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-e2-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf rodrigoramosrs/veriloop-coder-e2-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-e2-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf rodrigoramosrs/veriloop-coder-e2-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-e2-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf rodrigoramosrs/veriloop-coder-e2-gguf:Q4_K_M

Use Docker

docker model run hf.co/rodrigoramosrs/veriloop-coder-e2-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use rodrigoramosrs/veriloop-coder-e2-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-e2-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-e2-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/rodrigoramosrs/veriloop-coder-e2-gguf:Q4_K_M

SGLang

How to use rodrigoramosrs/veriloop-coder-e2-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-e2-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-e2-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-e2-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-e2-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use rodrigoramosrs/veriloop-coder-e2-gguf with Ollama:
```
ollama run hf.co/rodrigoramosrs/veriloop-coder-e2-gguf:Q4_K_M
```
Unsloth Desktop

How to use rodrigoramosrs/veriloop-coder-e2-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-e2-gguf:Q4_K_M

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": "rodrigoramosrs/veriloop-coder-e2-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use rodrigoramosrs/veriloop-coder-e2-gguf with Docker Model Runner:
```
docker model run hf.co/rodrigoramosrs/veriloop-coder-e2-gguf:Q4_K_M
```

Lemonade

How to use rodrigoramosrs/veriloop-coder-e2-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull rodrigoramosrs/veriloop-coder-e2-gguf:Q4_K_M

Run and chat with the model

lemonade run user.veriloop-coder-e2-gguf-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use rodrigoramosrs/veriloop-coder-e2-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-e2-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-e2-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use rodrigoramosrs/veriloop-coder-e2-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-e2-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-e2-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"

VeriLoop E2 · GGUF

Coding-Optimized Quantized Models

Original Model ↗ · GitHub · Apache-2.0

Overview

This repository contains GGUF quantizations of VeriLoop E2, an open 27B post-trained model built on Qwen3.8-27B for code, mathematics, and physics. Its core reasoning discipline is VeriLoop-Governed Recurrence (VGR): candidate states are recursively proposed, externally checked, and retained only when the protected evidence state improves without regression.

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	Size	Notes
`LoopCoder-VeriLoop-E2-BF16.gguf`	BF16	50.9 GB	Full-precision reference
`LoopCoder-VeriLoop-E2-Q8_0.gguf`	Q8_0	27.0 GB	High quality, larger file
`LoopCoder-VeriLoop-E2-Q6_K.gguf`	Q6_K	20.9 GB	Excellent quality / size trade-off
`LoopCoder-VeriLoop-E2-Q5_K_M.gguf`	Q5_K_M	18.2 GB	Strong quality, reduced size
`LoopCoder-VeriLoop-E2-Q4_K_M.gguf`	Q4_K_M	15.7 GB	Balanced quality / size
`LoopCoder-VeriLoop-E2-Q3_K_M.gguf`	Q3_K_M	12.6 GB	Smaller, good for limited RAM
`LoopCoder-VeriLoop-E2-IQ4_XS.gguf`	IQ4_XS	14.3 GB	Extra-small 4-bit
`LoopCoder-VeriLoop-E2-IQ3_XS.gguf`	IQ3_XS	11.6 GB	Extra-small 3-bit

Usage

llama.cpp

./llama-cli \
  -m LoopCoder-VeriLoop-E2-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-VeriLoop-E2-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-VeriLoop-E2-Q4_K_M.gguf
ollama create veriloop-e2:q4_k_m -f Modelfile
ollama run veriloop-e2:q4_k_m

Acknowledgements

Libo Wang and the Intelligent Robotics Laboratory, Tsinghua SIGS for developing the original VeriLoop E2 model.
The llama.cpp community for the quantization and inference tooling.
The original model repository: tsinghua-sigs-robot-lab/VeriLoop-E2

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: 561

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-e2-gguf

Base model

Qwen/Qwen3.8-27B

Finetuned

tsinghua-sigs-robot-lab/VeriLoop-E2

Quantized

(4)

this model