---

license: apache-2.0

base_model: DavidAU/Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-MTP

tags:

• gguf
• qwen3.6
• mtp
• enclave

---

fable-fusion-27b-mtp-q4-gguf (Enclave model volume)

Curated model volume for Enclave confidential inference:

DavidAU's Fable-Fusion-27B (Qwen3.6-27B fine-tune) at **NEO imatrix Q4_K_M with

the trained multi-token-prediction head** (~18.5 GB), bundled with the official

tokenizer.json so the volume is self-contained.

This is the small-share sibling of

EnclaveHost/fable-fusion-27b-mtp-gguf

(Q5_K_M, ~21.2 GB) — same model, same geometry, one quant step down. The

quant step is the point: with speculative decoding on, Q5_K_M needs about

33.1 GB to serve on a 256K-context node (21.2 weights + 9.7 KV at the q8_0

node window + ~0.6 GB head KV + working set), which does not leave room inside

the 35.1 GB a 25% H200 share holds. Because the MTP head context is created

lazily on the first speculative session, a tenant in that position preloads

and warms up normally and then stalls the first real request. Q4_K_M puts the

same sum near 30.4 GB and serves that share with ~4.7 GB to spare.

Upstream ships 25 quants plus vision mmproj files in one repo (~450 GB). A

Modelwrap volume carries the whole repo and a multi-gguf volume makes the

host's preload pick ambiguous, so this repo carries exactly one served gguf.

Provenance

| File | Upstream | Revision | sha256 |

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

| Fable-Fusion-27B-MTP-Q4_K_M.gguf | DavidAU/Qwen3.6-27B-Fable-Fusion-711-Uncensored-Heretic-NM-DAU-NEO-MAX-MTP-GGUF, file Qwen3.6-27B-Fable-Fus-711-UnHeretic-NM-DAU-NEO-MAX-NEO-MTP-Q4_K_M.gguf | ee5b7441ffe27869e1e5b4357733248d561c26b7 | c796c2c011eaa0edf06395ff49cda5bfd4843ad52b86b58a83296dfc33849e4e |

| tokenizer.json | the Qwen3.x family copy, taken from EnclaveHost/fable-fusion-27b-mtp-gguf (byte-identical across the 9b / 27b / 122b volumes) | 00b1e5483d5457fe32c0dd82f2d24a19ef0dc957 | 5f9e4d4901a92b997e463c1f46055088b6cca5ca61a6522d1b9f64c4bb81cb42 |

The gguf sha256 is also the blob hash HuggingFace serves for the upstream file,

so the copy is verified end to end. LICENSE is the Apache-2.0 text; upstream

is Apache-2.0.

Verified GGUF geometry

Read from the file header (llama.cpp gguf-py), and what the serving config

must match:

| key | value |

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

| general.architecture | qwen35 (hybrid SSM/attention) |

| qwen35.block_count | 65 — 64 trunk layers + 1 MTP layer |

| qwen35.nextn_predict_layers | 1 (blk.64.nextn.eh_proj, blk.64.nextn.enorm) |

| qwen35.attention.head_count / head_count_kv | 24 / 4 |

| qwen35.attention.key_length / value_length | 256 / 256 |

| qwen35.full_attention_interval | 4 → 16 full-attention trunk layers + the dense MTP layer = 17 KV-bearing layers |

| qwen35.context_length | 262144 |

| vocab | 248320, eos [248046 <|im_end|>, 248044 <|endoftext|>] |

| file type | 15 (Q4_K_M); the MTP tensors are Q8_0, as upstream sets them for every quant |

The qwen35 arch is served unchanged by the fleet's pinned llama.cpp

(LLAMA_COMMIT bec4772f); the MTP head additionally needs the batch4 MTP

toolchain on the host, and older hosts serve the volume as a plain 27b.

Usage on Enclave

Wrapped as a Tinfoil Modelwrap volume (dm-verity; the root hash is part of the

enclave measurement). Deployments attach it by name and the guest reads it at

/models/<name>; the host preloads the GGUF as the wasi-nn ggml graph. Single

file, so it loads on toolchains without split-family support, and with the

tokenizer bundled llm-chat's default lookup needs no cross-volume

configuration.

llm-chat catalog entry: chatml template, thinking: true, draft: "mtp"

(self-drafting through the head — no second model), draft_tokens: 6.

Upstream notes that MTP acceptance falls off above temperature 1, so keep

sampling at or below it; draft_tokens / draft_accepted in the app's

completion stats report the acceptance rate directly.

Attach this volume or the Q5_K_M one, never both. llm-chat serves the

largest attached model that fits, so a deployment holding both picks the Q5

and gives back exactly the stall this volume exists to avoid.