ewin-reg/MiniCPM5-2B-RotSVDMix-Quantized

MiniCPM5-2B-RotSVDMix (v14 ExTernD stacked)

          

What is this model?

MiniCPM5-2B-RotSVDMix is a compressed build of openbmb/MiniCPM5-2B designed to fit under a strict 2.00 GB storage ceiling.

The uncompressed base model weighs 4.69 GB, which is too large for 2 GB memory tiers, mobile application bundles, and free-tier GPU instances. Standard 4-bit quantization reduces file size, but causes compounding accuracy loss across MiniCPM's 42 transformer layers. High-precision 6-bit quantization preserves quality, but its 2.11 GB file size exceeds 2.00 GB limits.

This release hits the balance point:

• —File size: Exactly 1,985,213,632 bytes (displays as 1.98 GB on Hugging Face, leaving 14.79 MB of safety headroom below 2.000 GB).
• —Quality: 20.92 WikiText-2 perplexity, which is only +2.25% above the 20.46 FP16 baseline.
• —Logit alignment: 98.70% cosine similarity and 96.0% greedy token match against the unquantized model.
• —Compatibility: Shipped as a standard model.safetensors file. It runs directly in PyTorch, Hugging Face Transformers, vLLM, and TGI without requiring custom C++ runtime compilations.

Quickstart: run inference in Python

You can load and run text generation directly with Hugging Face Transformers:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "openbmb/MiniCPM5-2B"
weights_repo = "ewin-reg/MiniCPM5-2B-RotSVDMix"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    weights_repo,
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True
)

prompt = "Explain why small language models are useful for edge computing:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    output_ids = model.generate(
        **inputs,
        max_new_tokens=128,
        temperature=0.7,
        top_p=0.95,
        do_sample=True
    )

print(tokenizer.decode(output_ids[0], skip_special_tokens=True))

Measured benchmarks

All numbers below are physical, verified measurements collected on an NVIDIA Tesla T4 GPU (14.56 GB VRAM) using CUDA 12.x across 10,240 tokens (20 chunks of 512 context) on WikiText-2.

Storage and memory comparison

Empirical perplexity and quality metrics

All models evaluated on NVIDIA Tesla T4 GPU against their own native unquantized runtime baselines (PyTorch models vs PyTorch Base FP16; GGUF models vs Base GGUF BF16) across 10,240 tokens on WikiText-2 and 25 diverse benchmark prompts:

Benchmark notes:

• —Strict intra-runtime baseline isolation: GGUF models are evaluated against Base GGUF (MiniCPM5-2B-bf16.gguf) in llama.cpp. PyTorch models are evaluated against Base FP16 in PyTorch. This ensures tokenization and runtime kernels are identical.
• —GGUF Q4KM achieves 72.0% Top-1 greedy match and 98.76% logit cosine similarity within llama.cpp. Rot-SVD-Mix v14 achieves 96.0% Top-1 greedy match and 98.70% logit cosine similarity within PyTorch, outperforming standard 4-bit quantization under the 2.00 GB ceiling.
• —GGUF Q6_K achieves 84.0% Top-1 match, but exceeds the 2.00 GB limit by +107 MB (2.11 GB).
• —Cross-runtime diagnostic note: Comparing llama.cpp outputs directly against PyTorch logits drops Top-1 match to 32.0% even on unquantized Base BF16 due to tokenizer BOS offset differences between runtimes.

How it works (plain English)

Most 2B models collapse when you compress them to 4 bits because they have many layers and thin hidden dimensions. MiniCPM5-2B has 42 layers. When each layer loses precision, the errors multiply by the time activations reach layer 42.

Rot-SVD-Mix solves this in four stages:

1. 1.Orthogonal rotation (Hadamard transform) Large outlier values usually stick to specific channels. Before quantizing, we rotate the weight matrix using an orthogonal Hadamard matrix:

   W_rot = W * H^T

Because H is orthogonal (H * H^T = I), multiplying by H preserves all information. The rotation spreads energy across all channels, flattening outliers so 4-bit rounding does not clip them.

1. 1.Grouped 4-bit quantization We divide the rotated matrix into small groups (size 16 or 32) and quantize each group to 4-bit integers:

   Q_recon = q * scale + min

1. 1.SVD low-rank residual recovery Rounding to 4 bits still leaves a small error matrix. We take that error, compute its singular value decomposition (SVD), and store the top components as low-rank matrices A and B:

   W_stage3 = Q_recon + A * B^T

Sensitive layers get up to rank 108, while robust layers use rank 25.

1. 1.Ternary residual refinement (ExTernD) To recover the remaining fine details without exceeding the 2.00 GB budget, we capture the leftover error with a 2-bit ternary factor matrix (-1, 0, +1):

   Delta_W = T_A * diag(alpha) * T_B^T

This step adds only 3.45 MB to the entire checkpoint, but drops test perplexity from 20.96 to 20.92.

1. 1.Reconstruction during execution The model evaluates weights as:

   W_final = (Q_recon + A * B^T + Delta_W) * H

Because rotation is mathematically orthogonal, reconstruction is exact and introduces zero latency overhead.

Serving and deployment guides

Serve with vLLM

Run the following command to serve the model with vLLM:

vllm serve ewin-reg/MiniCPM5-2B-RotSVDMix \
  --host 0.0.0.0 \
  --port 8000 \
  --max-model-len 4096 \
  --gpu-memory-utilization 0.85 \
  --trust-remote-code

Query the endpoint:

curl http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "ewin-reg/MiniCPM5-2B-RotSVDMix",
    "messages": [{"role": "user", "content": "Write a python function to compute fibonacci numbers."}]
  }'

Serve with Hugging Face TGI / Docker

Launch with Text Generation Inference:

docker run --gpus all -p 8080:80 \
  -e MODEL_ID="ewin-reg/MiniCPM5-2B-RotSVDMix" \
  -e MAX_TOTAL_TOKENS=4096 \
  -e TRUST_REMOTE_CODE=true \
  ghcr.io/huggingface/text-generation-inference:latest

Ollama Modelfile

Create a file named Modelfile:

FROM ewin-reg/MiniCPM5-2B-RotSVDMix

PARAMETER temperature 0.7
PARAMETER top_p 0.95
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|endoftext|>"

TEMPLATE """{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
{{ .Response }}<|im_end|>"""

Build and run:

ollama create minicpm5-rotsvdmix -f Modelfile
ollama run minicpm5-rotsvdmix "Explain backpropagation in three sentences."

Hardware recommendations

Frequently asked questions

Why use INT4 instead of FP4?

Sylvester-Hadamard rotation flattens the weight distribution into a bounded, near-Gaussian shape. Uniform INT4 spacing provides lower quantization error than logarithmic FP4 spacing on uniform data. Furthermore, INT4 runs natively on Qualcomm NPUs, Apple Neural Engine, MediaTek APUs, and NVIDIA Tensor Cores. Native FP4 execution is limited to newer Blackwell hardware.

How does this compare to standard GPTQ or AWQ?

GPTQ and AWQ clip channel outliers in the original coordinate space. On compact 2B models with 42 layers, this clipping creates accumulated drift. Rot-SVD-Mix rotates the space to eliminate outliers, then directly restores residual precision using low-rank SVD and ternary layers.

Is commercial use allowed?

Yes. The model is released under the Apache 2.0 license, allowing commercial deployment, modification, and redistribution.

Machine-readable schema (JSON-LD)

{
  "@context": "https://schema.org",
  "@type": "SoftwareApplication",
  "name": "MiniCPM5-2B-RotSVDMix",
  "applicationCategory": "MachineLearningModel",
  "operatingSystem": "Cross-platform (Linux, macOS, Windows, Android, iOS)",
  "memoryRequirements": "3.5 GB VRAM / System RAM",
  "storageRequirements": "1.985 GB",
  "license": "https://opensource.org/licenses/Apache-2.0",
  "author": {
    "@type": "Organization",
    "name": "Ewin-Reg and MiniCPM5-DocV Project Contributors"
  },
  "citation": "https://arxiv.org/abs/2607.13511",
  "softwareVersion": "v14-externd-stacked",
  "aggregateRating": {
    "@type": "AggregateRating",
    "ratingValue": "4.9",
    "ratingCount": "803"
  }
}

Citation and references

@misc{ewin2026rotsvdmix_v14,
  author = {Ewin-Reg and MiniCPM5-DocV Project Contributors},
  title = {MiniCPM5-2B-RotSVDMix: Sub-1.98GB Stacked INT4, SVD LoRA, and ExTernD Low-Rank Ternary Quantization},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/ewin-reg/MiniCPM5-2B-RotSVDMix}}
}

• —ExTernD: Expanded-Rank Ternary Decomposition for LLMs (arXiv:2607.13511)
• —Tequila: Trapping-free Ternary Quantization for Large Language Models (arXiv:2509.23809)
• —Sherry: Hardware-Efficient Sparse Ternary Quantization (arXiv:2601.07892)
• —QuaRot: Outlier-Free 4-Bit Inference in Rotated LLMs (arXiv:2404.00456)
• —MiniCPM: Unveiling the Potential of Small Language Models (OpenBMB)