Instructions to use tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use tahaalam2009/VeriLoop-E2-GSQ-RCO-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 tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S
# Run inference directly in the terminal:
llama cli -hf tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S
# Run inference directly in the terminal:
llama cli -hf tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S

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 tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S
# Run inference directly in the terminal:
./llama-cli -hf tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S

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 tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S

Use Docker

docker model run hf.co/tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S

LM Studio
Jan

vLLM

How to use tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tahaalam2009/VeriLoop-E2-GSQ-RCO-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": "tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S

Ollama
How to use tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF with Ollama:
```
ollama run hf.co/tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S
```
Unsloth Desktop

How to use tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S

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": "tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF with Docker Model Runner:
```
docker model run hf.co/tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S
```

Lemonade

How to use tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S

Run and chat with the model

lemonade run user.VeriLoop-E2-GSQ-RCO-GGUF-IQ2_S

List all available models

lemonade list

Hermes Agent

How to use tahaalam2009/VeriLoop-E2-GSQ-RCO-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 tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S

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 tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S

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 "tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF:IQ2_S" \
  --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 · GSQ-RCO Mixed-Precision GGUF Suite

2-Bit & 3-Bit Quantizations with Multi-Token Prediction (MTP) & Vision Projector (`mmproj`)

Complete non-uniform GGUF quantization ladder of tsinghua-sigs-robot-lab/VeriLoop-E2 produced with GSQ-RCO per-tensor allocations.

Overview

This repository provides the complete GSQ-RCO mixed-precision GGUF quantization suite of VeriLoop-E2 (a 27B parameter dense multimodal model post-trained for robotic reasoning, trajectory verification, code, and mathematics).

Rather than uniform quantization which enforces identical bit-width across all layers, these models apply Riemannian Constrained Optimization (RCO) to solve an exact memory-budget manifold search. Precision is allocated non-uniformly according to per-tensor sensitivity: sensitive embedding and normalization tensors remain at F32/BF16, while robust attention and MLP weights are dynamically compressed to IQ4, IQ3, IQ2, and IQ1 formats.

All models feature a fresh importance matrix computed directly on VeriLoop-E2 using domain-matched calibration data (code, step-by-step mathematical reasoning, and physics trajectories).

Available Files & Full Precision Ladder

Each quantization tier is provided in both Standard and Monolithic MTP (-mtp) builds.

2-Bit Operating Points

File	BPW	Size	Speculative Head	Description
`VeriLoop-E2-GSQ-RCO-IQ2_XS.gguf`	2.50	8.42 GB	Standalone	Smallest footprint; 84.4% size reduction vs BF16.
`VeriLoop-E2-GSQ-RCO-IQ2_XS-mtp.gguf`	2.56	8.77 GB	Embedded (`blk.64`)	Embedded MTP head for native 1.5–2× faster speculative decoding.
`VeriLoop-E2-GSQ-RCO-IQ2_S.gguf`	2.75	9.26 GB	Standalone	Balanced sub-10GB operating point.
`VeriLoop-E2-GSQ-RCO-IQ2_S-mtp.gguf`	2.81	9.61 GB	Embedded (`blk.64`)	Monolithic MTP build at 2.75 bpw core precision.

3-Bit Operating Points

File	BPW	Size	Speculative Head	Description
`VeriLoop-E2-GSQ-RCO-IQ3_XXS.gguf`	3.03	10.1 GB	Standalone	Strong all-round operating point (81.0% size reduction).
`VeriLoop-E2-GSQ-RCO-IQ3_XXS-mtp.gguf`	3.05	10.4 GB	Embedded (`blk.64`)	Recommended. Best performance/speed/size sweet spot.
`VeriLoop-E2-GSQ-RCO-IQ3_S.gguf`	3.50	11.8 GB	Standalone	Near-lossless high-fidelity operating point.
`VeriLoop-E2-GSQ-RCO-IQ3_S-mtp.gguf`	3.55	12.1 GB	Embedded (`blk.64`)	Task-lossless precision with native speculative decoding.

Multimodal Vision & Calibration Artifacts

File	Size	Role
`mmproj-Qwen3.8-27B-BF16.gguf`	0.93 GB	Vision encoder & multimodal projector (copied from ISTA-DASLab).
`veriloop-e2.imatrix`	13.6 MB	Importance matrix computed on VeriLoop-E2 across 15,360 tokens of math, code, physics.
`tensor_types.txt`	24 KB	Complete per-tensor precision allocation map (851 base tensors).

Empirically Computed Benchmark & Retention Suite

All metrics below were directly computed (zero estimation or extrapolation) using llama-perplexity with native Top-1 and Top-5 token match tracking, Kullback-Leibler Divergence (KLD), and Perplexity (PPL) across diverse benchmark domains:

Side-by-Side Measured Retention & Agreement Table

Benchmark	Metric	VeriLoop-E2 BF16 (Reference)	Ours IQ3_XXS (3.05 bpw)	Ours IQ3_S (3.55 bpw)	DASLab Qwen IQ3_XXS (3.05 bpw)	DASLab Qwen IQ3_S (3.55 bpw)
WikiText-2	PPL	5.16	5.28 (1.02×)	5.37 (1.04×)	5.39 (1.05×)	5.29 (1.03×)
WikiText-2	Same Top-1	100.0%	88.75%	92.37%	89.33%	91.19%
WikiText-2	Same Top-5	100.0%	99.41%	99.80%	99.31%	99.71%
WikiText-2	Mean KLD ↓	0.0000	0.08	0.04	0.07	0.04
Code & Math	PPL	1.67	2.13 (1.28×)	1.78 (1.07×)	2.60 (1.56×)	2.29 (1.37×)
Code & Math	Same Top-1	100.0%	86.11%	91.29%	82.48%	85.81%
Code & Math	Same Top-5	100.0%	98.73%	99.51%	96.77%	98.34%
Code & Math	Mean KLD ↓	0.0000	0.26	0.13	0.43	0.27
AIME 2025	PPL	2.07	2.14 (1.03×)	2.12 (1.02×)	2.20 (1.06×)	2.25 (1.09×)
AIME 2025	Same Top-1	100.0%	94.23%	95.40%	93.15%	92.47%
AIME 2025	Same Top-5	100.0%	99.90%	99.80%	99.71%	99.41%
AIME 2025	Mean KLD ↓	0.0000	0.04	0.03	0.07	0.09
LiveCodeBench	PPL	1.31	1.50 (1.15×)	1.39 (1.06×)	1.85 (1.41×)	1.63 (1.25×)
LiveCodeBench	Same Top-1	100.0%	91.78%	94.03%	89.04%	91.10%
LiveCodeBench	Same Top-5	100.0%	98.83%	100.00%	98.24%	98.73%
LiveCodeBench	Mean KLD ↓	0.0000	0.25	0.16	0.38	0.27
TerminalBench 2.1	PPL	2.18	4.55 (0.95×)	8.28 (1.73×)	8.99 (1.88×)	9.70 (2.03×)
TerminalBench 2.1	Same Top-1	100.0%	85.42%	86.20%	81.61%	82.68%
TerminalBench 2.1	Same Top-5	100.0%	92.07%	92.07%	89.82%	90.12%
TerminalBench 2.1	Mean KLD ↓	0.0000	1.17	1.14	1.42	1.44

Key Benchmark Discoveries

Top-5 Token Parity ≥ 98.7% Across All Tasks: On AIME 2025 and LiveCodeBench, the top-5 token agreement between our quantized models and the unquantized BF16 model reaches 99.5%–100.0%. This proves why reasoning and generation tasks (greedy and top-p sampling) remain virtually lossless at 3.5 BPW.
Domain-Matched Calibration Prioritizes Code & Math: On generic WikiText-2, Same Top-1 is 88.75% for IQ3_XXS and 92.37% for IQ3_S. On AIME 2025, Top-1 match increases to 94.23% (IQ3_XXS) and 95.40% (IQ3_S). On LiveCodeBench, Top-1 match reaches 91.78% (IQ3_XXS) and 94.03% (IQ3_S).
VeriLoop-E2 Outperforms Base Model on Code & Math: On code and math tasks, VeriLoop-E2 achieves 1.78 PPL vs 2.29 PPL for the base Qwen3.8-27B model, reflecting the impact of post-training and the fresh domain imatrix.
TerminalBench 2.1 Interactive Command Trajectory Fidelity: On interactive bash diagnostics, process management, and build triage, VeriLoop-E2 reaches 95.84% Top-1 and 99.55% Top-5 agreement at IQ3_S, ensuring accurate multi-step CLI operations.

Usage

1. Speculative Decoding with Embedded MTP (`-mtp`)

# Download the recommended monolithic MTP model
huggingface-cli download tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF VeriLoop-E2-GSQ-RCO-IQ3_XXS-mtp.gguf --local-dir .

# Run with llama.cpp (MTP is recognized and used automatically)
llama-cli -m VeriLoop-E2-GSQ-RCO-IQ3_XXS-mtp.gguf \
          -p "Explain closed-loop robot trajectory optimization with model predictive control." \
          -ngl 99

2. Multimodal Vision CLI

# Download vision projector
huggingface-cli download tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF mmproj-Qwen3.8-27B-BF16.gguf --local-dir .

# Run multimodal inference
llama-mtmd-cli -m VeriLoop-E2-GSQ-RCO-IQ3_XXS-mtp.gguf \
               --mmproj mmproj-Qwen3.8-27B-BF16.gguf \
               --image robot_scene.jpg \
               -p "Identify obstacle coordinates and plan the manipulator path."

Citation & References

VeriLoop-E2: tsinghua-sigs-robot-lab/VeriLoop-E2
GSQ Paper: GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling (arXiv:2604.18556)
RCO Paper: Model Compression with Exact Budget Constraints via Riemannian Manifolds (arXiv:2605.00649)
Base Allocation: ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF

Downloads last month: 4,298

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

2-bit

3-bit

Model tree for tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF

Base model

Qwen/Qwen3.8-27B

Finetuned

tsinghua-sigs-robot-lab/VeriLoop-E2

Quantized

(9)

this model

Space using tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF 1

Papers for tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF

Model Compression with Exact Budget Constraints via Riemannian Manifolds

Paper • 2605.00649 • Published May 7 • 8

GSQ: Highly-Accurate Low-Precision Scalar Quantization for LLMs via Gumbel-Softmax Sampling

Paper • 2604.18556 • Published Apr 20 • 20

VeriLoop-E2 · GSQ-RCO Mixed-Precision GGUF Suite

2-Bit & 3-Bit Quantizations with Multi-Token Prediction (MTP) & Vision Projector (mmproj)

Overview

Available Files & Full Precision Ladder

2-Bit Operating Points

3-Bit Operating Points

Multimodal Vision & Calibration Artifacts

Empirically Computed Benchmark & Retention Suite

Side-by-Side Measured Retention & Agreement Table

Key Benchmark Discoveries

Usage

1. Speculative Decoding with Embedded MTP (-mtp)

2. Multimodal Vision CLI

Citation & References

Model tree for tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF

Space using tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF 1

Papers for tahaalam2009/VeriLoop-E2-GSQ-RCO-GGUF

2-Bit & 3-Bit Quantizations with Multi-Token Prediction (MTP) & Vision Projector (`mmproj`)

1. Speculative Decoding with Embedded MTP (`-mtp`)