VeriLoop E2 — GGUF

Official llama.cpp distribution of VeriLoop E2
BF16 reference · Q8_0 high fidelity · Q6_K overall sweet spot · Q5_K_M memory-quality sweet spot · IQ1_M minimum-footprint sweet spot

27B post-trained model for code, mathematics, and physics · 262K native context · Apache License 2.0
Developed by Tsinghua SIGS Robot Lab · Libo Wang

GGUF · Hugging Face · GGUF · ModelScope Global · GGUF · ModelScope China

Parent Model · Technical Report · Evaluation Evidence · Riemann ζ Artifact

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Model variants

Recommended deployment points

Q6_K — overall quality / efficiency sweet spot. It is 58.96% smaller than BF16 while remaining statistically consistent with BF16 PPL parity under the frozen paired protocol.

Q5_K_M — memory-quality sweet spot. It reduces the main-file footprint to 18.965 GiB while preserving wider KLD and Same-top margins than the sub-17 GiB variants.

IQ1_M — minimum-footprint sweet spot. It is 16.790078 GiB, 66.4955% smaller than BF16, and passes the frozen hard gate, engineering-reserve gate, stock llama.cpp runtime validation, and real MTP engagement validation. IQ2_S remains the lower-KLD low-footprint alternative.

Naming note: VeriLoop-E2-IQ1_M.gguf is a mixed-precision artifact, not a uniform 1-bit model. Its measured tensor policy is 353 F32 + 1 IQ1_M + 2 IQ2_S + 64 Q4_K + 429 Q5_K + 2 Q6_K = 851 tensors, with 5.36 effective BPW. VeriLoop-E2-IQ2_S.gguf is likewise mixed precision rather than uniform 2-bit quantization.

Precision ladder

Quantization-retention benchmark

All measured tiers use the same frozen BF16 logits and the same paired protocol.

These figures measure quantization retention against the BF16 reference. They are not downstream benchmark-score loss percentages. The nine parent-model benchmarks were not independently rerun for every quantization tier.

IQ1_M validation record

IQ1_M changes exactly one tensor relative to the IQ2_S precision policy: blk.1.ffn_down.weight moves from IQ2_S to IQ1_M. All other tensor assignments remain unchanged.

Fidelity and runtime validation

IQ1_M artifact identity

Low-footprint comparison

IQ1_M is 8.633 MiB smaller than IQ2_S and 36.523 MiB smaller than Q3_K_M. IQ2_S retains the lowest Mean KLD of the three; IQ1_M has the smallest footprint, the lowest PPL ratio, and higher Same top-p than IQ2_S. The point-estimate differences remain small relative to the reported uncertainty scale.

Frozen BF16-paired protocol

Parent-model benchmark record

The scores below describe the parent VeriLoop E2 release and are not relabeled as quantization-specific reruns.

Quick start with llama.cpp

# Overall sweet spot
hf download tsinghua-sigs-robot-lab/VeriLoop-E2-GGUF VeriLoop-E2-Q6_K.gguf --local-dir .

# Minimum-footprint sweet spot
hf download tsinghua-sigs-robot-lab/VeriLoop-E2-GGUF VeriLoop-E2-IQ1_M.gguf --local-dir .

# Run
llama-server \
  -m ./VeriLoop-E2-IQ1_M.gguf \
  -ngl 99 \
  -c 32768 \
  --host 127.0.0.1 \
  --port 8080

Memory guidance

A 16.79 GiB model file does not imply full offload on a 16 GiB GPU. Runtime memory also includes KV cache, compute buffers, allocator overhead, and optional speculative-decoding weights.

A standalone Q8_0 llama-bench record exists (pp512 3102.722776 tok/s; tg128 41.999677 tok/s), but there is no frozen paired BF16 throughput campaign. No universal speedup percentage is claimed for the quantization ladder.

Measurement boundaries

• PPL, KLD, Same top-p, and token-probability statistics are quantization-retention measurements, not universal downstream capability-loss percentages.
• IQ1_M and IQ2_S are mixed-precision artifacts; the tier name does not equal the model-wide effective bit width.
• Native 262K context comes from the parent configuration; practical context depends on runtime memory.
• MTP acceptance is prompt- and workload-dependent.
• The model can still produce incorrect code, mathematics, scientific reasoning, or commands.

Source model and evidence

License

The VeriLoop E2 model weights and this GGUF distribution are released under the Apache License 2.0, consistent with the parent model release.

Citation

@misc{wang2026veriloope2,
  title        = {VeriLoop E2: A 27B Post-Trained Model for Code, Mathematics, and Scientific Reasoning},
  author       = {Wang, Libo},
  year         = {2026},
  note         = {Tsinghua Shenzhen International Graduate School (SIGS)},
  howpublished = {Open model release},
  url          = {https://huggingface.co/tsinghua-sigs-robot-lab/VeriLoop-E2}
}

For quantization-specific comparisons, identify the exact GGUF filename and corresponding manifest identity.