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rodrigoramosrs/veriloop-coder-e2-gguf

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<div align="center"> <h1>VeriLoop E2 · GGUF</h1> <p><strong>Coding-Optimized Quantized Models</strong></p> <p> <a href="https://huggingface.co/tsinghua-sigs-robot-lab/VeriLoop-E2">Original Model ↗</a> · <a href="https://github.com/rodrigoramosrs">GitHub</a> · Apache-2.0 </p> </div>

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`	Q5KM	18.2 GB	Strong quality, reduced size
`LoopCoder-VeriLoop-E2-Q4_K_M.gguf`	Q4KM	15.7 GB	Balanced quality / size
`LoopCoder-VeriLoop-E2-Q3_K_M.gguf`	Q3KM	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

bash

./llama-cli \
  -m LoopCoder-VeriLoop-E2-Q4_K_M.gguf \
  -p "Your coding prompt here" \
  -n 2048 \
  -t 8

llama-cpp-python

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

bash

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.