---

license: apache-2.0

base_model: Qwen/Qwen3.8-27B

base_model_relation: quantized

language:

• en
• zh

library_name: gguf

pipeline_tag: image-text-to-text

tags:

• gguf
• llama.cpp
• quantized
• qwen3.8
• qwen3.5
• ridge
• gated-deltanet
• imatrix
• reasoning
• multimodal
• vision
• mtp
• long-context

---

Qwen3.8-27B-Ridge-3.7bpw

Developed by Empero

A Gated-DeltaNet-aware mixed GGUF of official

Qwen/Qwen3.8-27B (1d4bf0f2)

for llama.cpp, Ollama, LM Studio,

jan, KoboldCpp, and other stock GGUF runtimes.

This is a quantization of the Qwen3.8-27B checkpoint. Ridge is a probed mix of types

written for this architecture: 64 layers =

16 × (3 × GatedDeltaNet → FFN + 1 × GatedAttn → FFN). Generic IQ2_XS

and UD-IQ2 do not treat GDN state (ssm_alpha / ssm_beta) or

the GDN mixers as first-class. We fixed that.

Nothing was stripped to make the file fit. The native MTP draft head

(blk.64 / nextn) stays in the GGUF. Vision is a separate BF16

mmproj.

> [!Note]

> This card is about choosing the file and running it. The official

> capability writeup lives on the

> base model card.

---

Files

The repository is Qwen3.8-27B-Ridge-GGUF. Use the exact filenames below

when downloading or passing -m.

| File | Quant | Size | Notes |

|---|---|---:|---|

| Qwen3.8-27B-Ridge-3.7bpw.gguf | Ridge mix, 3.69 bpw | 11.73 GiB / 12.59 GB | this release — text + native MTP |

| mmproj-Qwen3.8-27B-BF16.gguf | BF16 | 0.87 GiB / 0.93 GB | vision encoder + projector; required for images |

If you only want text, download the Ridge GGUF. Add the mmproj for image input.

What fits on a GPU?

These are practical weight-size-based estimates, not a VRAM benchmark.

They assume a modest context and leave room for runtime and the KV cache.

Image input adds the 0.87 GiB mmproj. The native 262k window and the

1M YaRN extension — make KV the dominant cost and may need offload

regardless of weight quant.

Measured: Qwen3.8-27B-Ridge-3.7bpw.gguf fully offloaded to a single

RTX PRO 6000 Blackwell (96 GB) runs at **~54 tok/s generation,

~130 tok/s prompt** (llama.cpp CUDA, -ngl 99, short smoke). One data

point on one card, not a sweep — but a 27B at 11.7 GiB is comfortably

interactive on a 16–24 GB card at modest context.

| File | Approximate hardware guidance at modest context |

|---|---|

| Ridge-3.7bpw | The practical 16 GB starting point; 24 GB is comfortable once you add KV and (optionally) the mmproj. |

| + mmproj | Add ~1 GiB. Still a 24 GB card for everyday use. |

---

Recipe

Qwen3.8 is a hybrid: three Gated-DeltaNet layers for every full-attention

layer. GDN state is disproportionately sensitive to low-bit quantization,

so Ridge holds that path high and spends the saved bits by dropping

mid-stack FFN.

The Gated-DeltaNet state path is Q8_0. Mixers are Q4_K, not IQ2.

That is the difference between this file and a flat 2-bit dump of the

same model.

Built with llama.cpp adb55e5, CUDA, importance matrix on 80 × 512-token

chunks (--process-output, wikitext + code). MTP tensors are unused

during calibration and have no imatrix — IQ2/IQ3 on blk.64 will

abort, so the draft head stays Q6_K.

---

Measured

Same box, same calibration file, llama-perplexity, 80 chunks,

-c 512 -b 512. BF16 is our convert of the same official checkpoint.

| Candidate | Size | BPW | Wiki-style PPL | vs BF16 |

|---|---:|---:|---:|---|

| BF16 GGUF (this convert) | 50.89 GiB | 16.00 | 7.15 ± 0.12 | — |

| Ridge-3.7bpw | 11.73 GiB | 3.69 | 7.82 ± 0.14 | +9.3 % |

---

Comparison

Published Hugging Face file sizes as of 2026-08-15. PPL is filled only

where we measured the file ourselves.

| File | Publisher | Size | Nominal band | PPL vs this BF16 |

|---|---|---:|---|---|

| BF16 | this convert | 50.89 GiB | 16 bpw | 7.15 |

| UD-IQ2_XXS | unsloth | 8.39 GiB | ~2.1 bpw | not measured here (Unsloth quotes 82.5 % top-1 vs BF16) |

| UD-IQ2_M | unsloth | 9.61 GiB | ~2.4 bpw | not measured |

| IQ2_XXS | bartowski | 8.75 GiB | ~2.2 bpw | not measured |

| Q3_K_S | unsloth | 11.71 GiB | ~3.1 bpw | not measured |

| Ridge-3.7bpw | empero-ai | 11.73 GiB | 3.69 bpw | 7.82 (+9 %) |

| IQ3_XXS | bartowski | 11.76 GiB | ~2.9 bpw | not measured |

| UD-Q3_K_XL | unsloth | 12.52 GiB | ~3.4 bpw | not measured |

---

Quick start

llama.cpp (llama-cli)

Sampling from the official Qwen3.8 card. Thinking is on by default.

# thinking
llama-cli \
  -m Qwen3.8-27B-Ridge-3.7bpw.gguf \
  -ngl 99 -n 16384 \
  --temp 1.0 --top-p 0.95 --top-k 20 \
  -p "Explain the design tradeoffs in a Gated-DeltaNet hybrid model."

# instruct (thinking off)
llama-cli \
  -m Qwen3.8-27B-Ridge-3.7bpw.gguf \
  -ngl 99 --reasoning off \
  --temp 0.7 --top-p 0.80 --top-k 20 --presence-penalty 1.5 \
  -p "Say hello in one short sentence."

llama.cpp (llama-server)

llama-server \
  -m Qwen3.8-27B-Ridge-3.7bpw.gguf \
  -c 16384 --port 8080

Ollama

ollama run hf.co/empero-ai/Qwen3.8-27B-Ridge-GGUF

Or a local Modelfile:

FROM ./Qwen3.8-27B-Ridge-3.7bpw.gguf
PARAMETER temperature 0.7
PARAMETER top_p 0.8
PARAMETER top_k 20

ollama create qwen38-ridge -f Modelfile
ollama run qwen38-ridge

LM Studio / jan / KoboldCpp

Download Qwen3.8-27B-Ridge-3.7bpw.gguf and load it. Preserve the

embedded Qwen3.8 chat template if the runtime asks you to select one.

llama.cpp with MTP draft speculation

The Ridge GGUF keeps the native MTP head. Use a recent llama.cpp build

that supports --spec-type draft-mtp:

llama-server \
  -m Qwen3.8-27B-Ridge-3.7bpw.gguf \
  --spec-type draft-mtp \
  --spec-draft-n-max 6 \
  -c 16384 --port 8080

If your runtime does not support MTP, the file still runs as a normal

27B — you just will not get the draft speedup.

---

Vision (image input)

Download the text GGUF and mmproj-Qwen3.8-27B-BF16.gguf.

llama.cpp (llama-mtmd-cli)

llama-mtmd-cli \
  -m Qwen3.8-27B-Ridge-3.7bpw.gguf \
  --mmproj mmproj-Qwen3.8-27B-BF16.gguf \
  --image ./photo.jpg \
  -p "Describe this image in detail." \
  --temp 0.7 --top-p 0.80 --top-k 20 \
  -c 16384

llama.cpp server

llama-server \
  -m Qwen3.8-27B-Ridge-3.7bpw.gguf \
  --mmproj mmproj-Qwen3.8-27B-BF16.gguf \
  -c 16384 --port 8080

---

Sampling

Qwen3.8 is a hybrid thinking model. Responses open with a

<think>…</think> block unless thinking is disabled.

| Mode | temperature | top_p | top_k | presence_penalty |

|---|---|---|---|---|

| Thinking (default) | 1.0 | 0.95 | 20 | 0.0 |

| Instruct (thinking off) | 0.7 | 0.80 | 20 | 1.5 |

Use the runtime chat/completions path rather than hand-rolling a

different prompt format. The embedded template is Qwen3.8's, including

tool-use (<tool_call>…</tool_call>).

Long context

Native context is 262,144 tokens, extensible to 1,000,000 with

YaRN. Set -c to what you actually need — the KV cache, not the

11.7 GiB weights, is what blows up a 16–24 GB card at long context.

---

Limitations

• Not lossless. +9 % wiki-style PPL vs our BF16 convert
• Context costs memory. Weight size is only part of the hardware

budget.

• MTP is runtime-dependent. The head is in the file; the speedup

needs a runtime that knows draft-mtp.

Stay in the loop

Sign up for the Empero newsletter at empero.org

for releases, evals, and research notes.

Support / Donate

If this model helped you, consider supporting the project:

• BTC: bc1qx6zepu6sfkvshgdmc4ewu6pk6rpadvpgffpp7v
• LTC: ltc1qv2mefzps2vtjcpwfx8xxdrpplrcvltswm68r7x
• XMR: 42Dbm5xg5Nq26fdyzfEU7KBnAJfhi7Cvz5J2ex5CzHXkfKuNEJzYCcmJ1GTbgjFZ5MBx72sdG1G9239Cd6rsZfv4QeDkYJY

---

Provenance & licensing

Quantization of Qwen/Qwen3.8-27B

@ 1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0. Weights are Apache-2.0,

inherited from the Qwen base, shared as-is.

Acknowledgements

• Developed and released by Empero
• Base model: Qwen3.8-27B (Alibaba Qwen team)
• GGUF quantization: llama.cpp (ggml-org)