Qwen3.6-27B-A3B-Coder
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Explore AI models 85003
Qwen3.6-27B-A3B-Coder-MTP-GGUF
ManniX-ITAA **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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View details ↗Qwen3.6-27B-A3B-CoderX-MTP-GGUF
ManniX-ITAGGUF 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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View details ↗Qwen3.6-27B-Omnimerge-v4-GGUF
ManniX-ITAGGUF 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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View details ↗Qwen3.6-27B-Omnimerge-v4-MTP-GGUF
ManniX-ITA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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View details ↗Qwen3.8-27B-Omnimerge-v6-MTP-GGUF
ManniX-ITAGGUF 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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View details ↗Ternary-Bonsai-2-27B-CRACK-MTP-GGUF
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View details ↗Ternary-Bonsai-2-27B-MTP-GGUF
ManniX-ITAPrismML'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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View details ↗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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View details ↗**PERFECT for developers who want:** - **Local AI coding assistant** (like Codex but private) - **Function calling without API costs** - **6GB VRAM compatibility** (runs on most gaming GPUs) - **Zero internet dependency** once downloaded - **Ollama integration** (one-command setup)
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View details ↗Qwen3.5-4B-Q5_K_M.gguf
Manojb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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View details ↗Qwen3.5-9B-Q5_K_M.gguf
Manojb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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View details ↗Qwen3.5-9B-Q8_0.gguf
Manojb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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View details ↗This model card focuses on the model associated with the Stable Diffusion v2-1-base model. This `stable-diffusion-2-1-base` model fine-tunes stable-diffusion-2-base (`512-base-ema.ckpt`) with 220k extra steps taken, with `punsafe=0.98` on the same dataset. - Use it with the `stablediffusion` repository: download the `v2-1_512-ema-pruned.ckpt` here. - Use it with 🧨 `diffusers`
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View details ↗stable-diffusion-2-base
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View details ↗peta1
manojpanda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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