veriloop-coder-e1-gguf

<div align="center"> <h1>VeriLoop Coder-E1 · GGUF</h1> <p><strong>Coding-Optimized Quantized Models</strong></p> <p> <a href="https://huggingface.co/tsinghua-sigs-robot-lab/veriloop-coder-e1">Original Model ↗</a> · <a href="https://github.com/rodrigoramosrs">GitHub</a> · Apache-2.0 </p> </div>

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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

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.