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opendecider-nano

manjunathshiva

**Open, calibrated System 1 decision model.** Give it a **state** (text, email, ticket or JSON) and **typed questions** (`choice`, `score`, `noul`); it returns a calibrated probability for every option in a single forward pass: **17 ms on an NVIDIA L40S, 18 ms on an Apple M4 Max, ~9 ms per question batched**. ~400M parameters, Apache-2.0. It never generates text, so there is nothing to parse and nothing to hallucinate. **Ahead of Laya like for like on typed-decisions: 0.796**, against 0.766 for Laya's typed-decisions checkpoint (+0.030, 95% CI +0.014 to +0.044), both fine-tuned on its train split. TypeSafe Jev scores 0.754 there zero-shot (measured through TypeSafe's own API): a reference, not a head-to-head. **Documentation: manjunathshiva.github.io/opendecider**: getting started, choosing a model, guides for serving, LM Studio, Ollama and vLLM and automating the confident decisions, plus the Python and HTTP API reference.

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GGUF builds of OpenDecider-small, the 4B OpenDecider decision model, for **LM Studio**, **Ollama** and anything else built on llama.cpp. Best for decisions it has never seen: the best general accuracy and calibration of the 4B models (zero-shot on typed-decisions). A decision model answers typed questions (`choice`, `score`, yes/no) about any text or JSON with a probability for every option, instead of generating text. The opendecider package builds the exact prompt the model was trained on and reads the option probabilities from the server's token log-probabilities. On typed-decisions (2,000 questions), the Q8_0 build gives the same top answer as the full-precision model on 1,975 (98.8%), at the same accuracy: Measured through Ollama's llama.cpp engine; LM Studio runs the same engine and returned identical log-probabilities on the same file. **Q8_0 is the recommended file**; Q4_K_M is for machines with little memory.

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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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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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