Qwen3.5-9B-Holodeck-Lounge (MTP-restored) — GGUF

This repo ships the IQ4_XS and Q4_K_M GGUF conversions of our model, MTP head and patched template included. The chat template is also included as a separate file (chat_template.jinja) — use it for best results.

TL;DR A working native Multi-Token Prediction (MTP) head on top of the Holodeck-Lounge merge. The upstream merge lineage shipped without a functional MTP head (the mtp.fc.weight tensor is absent after merge), so we restored it by sourcing only that single missing tensor — mtp.fc.weight — from Jackrong/Qwopus3.5-9B-Coder. The other 14 of the MTP head's 15 layers (the mtp.layers.* blocks) were already present in the merge. We also patched in the froggeric/Qwen-Fixed-Chat-Templates fixed jinja template (v22.1), which improves reasoning structure and MTP acceptance in our evals. This model is the reference donor for our head fine-tune experiments. This repo ships the GGUF conversion(s) — IQ4_XS and Q4_K_M — plus the chat_template.jinja file.

Why this model exists

nightmedia/Qwen3.5-9B-Holodeck-Lounge is a fine creative-fiction merge. Excellent writing model — but its merge lineage (13 base models, see the upstream card for the full recipe) does not carry a working MTP head: mtp.fc.weight is missing, so native speculative decoding is impossible from the source.
What we did: the merge already carried 14 of the MTP head's 15 layers (the mtp.layers.* transformer blocks); only the input-fusion projection mtp.fc.weight was absent. We sourced that single tensor from Jackrong/Qwopus3.5-9B-Coder, transplanted it into the Holodeck-Lounge backbone, and verified it loads and runs end-to-end in llama.cpp (draft-mtp) and vLLM (qwen3_5 MTP method). Backbone weights untouched.
Chat template: baked in the fixed jinja template v22.1 from froggeric/Qwen-Fixed-Chat-Templates (Qwen 3.5/3.6/3.8), improving reasoning structure, organisation, coherence, and MTP acceptance rate in our evals.
GGUF conversions: this repo ships the IQ4_XS and Q4_K_M GGUF conversions of the model, MTP head and patched template included, plus the chat_template.jinja file — use the template file for best results.

Native MTP head

Single-layer, DeepSeek-style MTP head (as in the DeepSeek-V3 Technical Report), 3 draft tokens, fixed adjacent context.
The 14 mtp.layers.* blocks come from the Holodeck-Lounge merge; only the mtp.fc.weight fusion projection was sourced from Jackrong/Qwopus3.5-9B-Coder (a model whose checkpoint carries the same native Qwen3.5 MTP architecture).
The donor projection plus the 14 host layers reach the acceptance range below — higher than any of our head-only fine-tunes (related experiment →).

Evaluation

Metric	Value
llama.cpp greedy / target-only acceptance	59.0%
vLLM rejection-sampling acceptance	58.55%
RS pos0 / pos1 / pos2	77.2% / 57.0% / 41.4%
Mean accepted length	2.756

Eval: 30 prompts, temperature 0.7, max 256 tokens, concurrency 8. llama.cpp numbers are greedy target-only acceptance (reasoning off); vLLM RS acceptance from vllm:spec_decode_num_accepted_tokens / _num_draft_tokens. With reasoning enabled per the Usage section, measured acceptance reaches the 60–69% range (n=30, same eval set).

Eval caveat: n=30 is a small sample — treat the 3-decimal precision as indicative; confidence intervals are wide.

Real-world usage

With the recommended settings — draft-mtp in llama.cpp, qwen3_5 MTP in vLLM — the restored head has consistently delivered a 60–69% draft-token acceptance rate (65+% typical) in our testing. Real-world mileage will vary with workload and hardware; treat 65% as a strong baseline, not a guarantee. The point of this model isn't raw speed, but option: MTP when it pays off, plain greedy decoding when it doesn't.

Where the win matters most:

Bandwidth-constrained serving, where accepted draft tokens cut tokens-per-request at the wire.
High-concurrency, latency-sensitive inference, where saved decode steps shrink queue and p95.
Settings where every accepted draft token compounds — long generations, reasoning-heavy prompts, tight SLOs.

The clean part: the host checkpoint already carried 14 of the MTP head's 15 layers, so this model ships at essentially the same size as its host — it was always that big. The single-tensor transplant just completes the missing mtp.fc.weight fusion layer, so you get the choice in one model, at no real size cost.

Thanks to nightmedia for the Holodeck-Lounge backbone (which already carried 14 of the MTP head's 15 layers), to Jackrong/Qwopus3.5-9B-Coder for the mtp.fc.weight tensor that completed the restore, and to froggeric for the Qwen-Fixed-Chat-Templates v22.1 baked in here.

Usage (recommended)

For best performance, use the baked-in froggeric template with reasoning enabled and preserved:

llama-server -m model.gguf \
    --jinja --chat-template-file chat_template.jinja \
    --reasoning-format deepseek --reasoning on --reasoning-preserve \
    --spec-type draft-mtp --spec-draft-n-max 3

llama.cpp MTP: --spec-type draft-mtp --spec-draft-n-max 3
vLLM MTP: --speculative-config '{"method":"mtp","num_speculative_tokens":3}'

When running a quantized target (IQ4_XS / Q4_K_M), keep --model-draft / the MTP head at higher precision (fp16/bf16) so the draft stays accurate.

Provenance & credits

Component	Source
Base architecture	Qwen/Qwen3.5-9B (Qwen team)
Backbone merge	nightmedia/Qwen3.5-9B-Holodeck-Lounge — 13-model creative-writing merge (DavidAU heretic family, armand0e, microsoft/Fara1.5-9B, Jackrong, et al.; see upstream card)
MTP head — 14 `mtp.layers.` blocks + `mtp.norm`/`mtp.pre_fc_norm_`	Present in nightmedia/Qwen3.5-9B-Holodeck-Lounge merge
MTP head — `mtp.fc.weight` (input-fusion projection, the missing 15th layer)	Jackrong/Qwopus3.5-9B-Coder
Chat template (v22.1)	froggeric/Qwen-Fixed-Chat-Templates

License: apache-2.0. License chain (all components apache-2.0, verified): Qwen/Qwen3.5-9B → nightmedia merge → Jackrong MTP head → froggeric template.

Limitations

Acceptance trails dedicated trained draft heads such as EAGLE-3 (≈61% on Qwen3.5-9B).
Deepest draft positions (pos2) accept at lower rates than pos0/pos1.
The transplanted head is inference-accelerator only; it does not affect backbone text quality.
The head is only directly usable as a built-in MTP head for this architecture (same hidden size + vocab); as a standalone GGUF it can also be used as a sidecar draft (--model-draft mtp-*.gguf).

AI was used to draft this report.