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**`ManniX-ITA/JackOD-9B-Coder` v3** quantized for **DFlash speculative decoding**: every quant tier of `JackOD-9B-Coder-MTP-GGUF` with the built-in MTP (NextN) layer removed, plus a separate **DFlash drafter** that does the drafting instead. Use **this** repo if your runtime drafts with DFlash. Use `JackOD-9B-Coder-MTP-GGUF` if it drafts with the model's own MTP head, or if you do not use speculative decoding at all. The two repos hold the same quantized weights; only the speculative-decoding path differs. JackOD-9B-Coder is a merge over `Qwen/Qwen3.5-9B` built for multi-turn agentic coding — DeltaCoder-9B 0.55 / Ornith-1.5-9B 0.30 / Coder-Flash 0.15, `omnimerge_v2`. The recipe, the evaluations and the full description are on the model card.

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GGUF quants of **`ManniX-ITA/JackOD-9B-Coder`**, a four-way `omnimerge_v2` merge over `Qwen/Qwen3.5-9B` built for **multi-turn agentic coding** — staying on a task across turns and tool calls until it is actually finished, and then stopping. > **v3 (2026-10-24).** v3 is the v2 recipe with the two smaller weights swapped: > **DeltaCoder 0.55 / Ornith-1.5-9B 0.30 / Coder-Flash 0.15** (v2: Coder-Flash 0.30 / Ornith 0.15). > `omnimerge_v2` density 0.53, darex-q 0.75, seed 42, the root `eos_token_id` 248046 / `pad_token_id` 248055 > fix and the Coder-Flash tokenizer and template are unchanged. v3 ships a **new serving sampler** and was > evaluated with it; the v2 and v1 results kept at the end of this card were measured **greedy**, so they are > a record, not a same-basis comparison. Every `_K` and `IQ` tier is **imatrix-calibrated**, including `Q6_K`. The calibration matrix used to build them is published in this repo as `imatrix.dat` (sha256 `feb286c4…`) so the quants can be audited and reproduced.

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Self-contained single-file inference engine from the opencoti project — a Mozilla-Ocho llamafile base carrying the opencoti patch series: advanced KV residency/quantization (PolyKV, KVarN — see `docs/features/kvarn.md`), the KV rolling window (a KV cache larger than VRAM), elastic multi-session serving (elastic slots, an enforced KV admission gate with fast 429 + `Retry-After`, PolyKV shared-prefix pools with a REST control plane), DCA long-context extension, MTP speculative decode, sparse attention, RYS layer duplication, CUDA **and Vulkan** GPU backends, and a runtime introspection/control API. **Start with USAGE.md** — it explains exactly how this engine diverges from upstream llamafile, every added feature, its flags, defaults, limitations, and which features compose. This repo hosts the packaged release artifacts (they exceed GitHub's 2 GiB release-asset cap) plus the full patch series under `patches/` and the project documentation under `docs/`. Release notes and per-release `SHA256SUMS` live under `releases/` and are mirrored on the corresponding GitHub release at mann1x/opencoti.

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GGUF quantizations of **`ManniX-ITA/Ornith-1.5-27B-A3B-Coder`** — the pure competence-map 184-expert cut of `Ornith-1.5-35B-A3B`. **→ Recipe, drop maps, the full 11-bench evaluation, the cross-family comparison and the length/deliberation analysis all live on the weights card.** This card covers the quantized files only. > Its sibling **CoderX** > uses the same map with a REAP stability floor and measures better on almost every code and > reasoning axis at the same expert budget and the same file size. Start there unless you > specifically want this arm.

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A **code-specialist expert prune** of Qwen3.6-35B-A3B: the MoE is reduced from **256 experts to 184** (72 dropped per layer, ~35B→27B, still A3B active) using a **code-targeted competence map** (LiveCodeBench + MultiPL-E competence classes). Same router, attention, norms, MTP head and vision tower as the base — only the expert keep-set changes. **Served at top-10** (`num_experts_per_tok = 10`, baked as the default). This is a routing-recovery lever: after pruning to 184 experts, activating the top-10 (vs the base top-8) recovers instruction-following at **no cost to code** (see below). No fine-tuning, no distillation — pure expert selection + a routing-width dial. 1. **Competence map**: the 256e teacher is profiled per-expert on a balanced corpus + targeted **LiveCodeBench** and **MultiPL-E** (Rust/Java/JS) PASS-response classes. 2. **Drop map**: `wmax` aggregation with the LCB + MPE classes up-weighted (1.5) → 72/256 experts dropped per layer, protecting the code-competent experts. 3. **Top-10 routing** (`num_experts_per_tok = 10`) baked into the config → the shipped default. Pass `--override-kv qwen35moe.expert_used_count=int:8` to any llama.cpp tool to A/B back to native top-8.

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GGUF quantizations of **CoderX**, a long-horizon code prune of Qwen3.6-35B-A3B: the MoE is reduced from **256 experts to 184** (72 dropped per layer, ~35B→27B, still A3B active). Same expert budget as the sibling Qwen3.6-27B-A3B-Coder, but a different **selection** — same competence map, different keep-set. **Served at top-8** — the base model's native routing, no override needed. No fine-tuning, no distillation. CoderX is **not** a strict upgrade over Coder. It is the long-horizon sibling: far stronger on multi-step competitive-programming work, a tie on short multi-language completion, and its non-code profile is not yet measured. Pick by workload.

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GGUF quantizations of `ManniX-ITA/Qwen3.6-27B-Omnimerge-v4` — the **MLP-passthrough** variant that defends against the Qwen3.6 think-policy fragility we discovered. Source dtype is BF16; this repo provides the standard bartowski quant ladder (F16 → IQ2_XXS) for `llama.cpp`. > **Source model:** `ManniX-ITA/Qwen3.6-27B-Omnimerge-v4` (BF16 weights, model card with full benchmarks and methodology). > **NOT** a quant of clean Qwen/Qwen3.6-27B — these GGUFs contain the v4 merge. > > **MTP companion (2× decode speedup):** weight-identical GGUFs with the MTP head retained for `llama.cpp --spec-type draft-mtp` self-speculative decoding are at `ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-MTP-GGUF`. Quality is statistically indistinguishable from this repo (HE 137/164 ↔ 137/164, GPQA 155/198 ↔ 154/198); aggregate decode is 2.0-2.3 × faster on a single 24 GB GPU. Use that repo for interactive / single-request workloads where latency matters. All quants made using imatrix with calibration data v5, the same calibration set bartowski uses for the Qwen3.6 base release — so quality fingerprints are directly comparable to bartowski's `Qwen_Qwen3.6-27B-GGUF` repo.

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GGUF quantizations of `ManniX-ITA/Qwen3.8-27B-Omnimerge-v6` — a task-arithmetic merge of three Qwen3.6 fine-tunes onto the **Qwen3.8-27B** base — with the **MTP head retained** (`blk.64.*`, 866 tensors) and the F16 vision projector published alongside. 19 tiers. `imatrix.dat` is archived in this repo, so the imatrix tiers are reproducible and auditable. This is a measured policy, not an omission. The imatrix crossover is a property of the quant **grid coarseness**: at the Q4 band and above the calibration bias outweighs importance-weighting and imatrix is neutral-to-harmful; below it (Q3/Q2/IQ) imatrix is the difference between a good quant and a broken one. Measured once per model family on a Q2→Q6 HE+/MPE ladder. So Q4_K_*/Q5_K_*/Q6_K* are built **without** imatrix and everything at Q3 and below — plus the IQ tiers — **with** it. Q8_0 needs none.

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PrismML's Ternary Bonsai 2 27B (a 27B dense model with end-to-end ternary weights, Qwen3.8-27B hybrid backbone, 262K context, Apache-2.0) **with a NextN / MTP draft head bundled in**, so the model self-speculates — no second file, no external drafter. The published PrismML GGUFs carry no NextN layer. These bundles add the upstream Qwen/Qwen3.8-27B MTP head (its `mtp.*` tensors, exported by upstream llama.cpp `convert_hf_to_gguf.py --mtp --outtype q8_0`) as one extra layer; every other key and tensor is the PrismML file, byte for byte. The head stays in the normal basis (it is not among the Hadamard-rotated tensors); its token-embedding lookup on the rotated table gets the inverse transform in the engine (PrismML's `518ad108`). The vision projector is **not** duplicated here: use PrismML's `Ternary-Bonsai-2-27B-mmproj-Q8_0.gguf` (600 MiB) from the base repo with `--mmproj`.

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