deepseek-r1-8b-tool-merged
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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 85003
ministral-8b-tool-merged
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查看详情 ↗Ollama_Quantised_LLaVA-NeXT-Video-7B-DPO
ManishThota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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查看详情 ↗Ollama_Video_llama_7B
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查看详情 ↗gpt-oss-20b-tq3
manjunathshiva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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查看详情 ↗NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-tq3-g64
manjunathshiva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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查看详情 ↗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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查看详情 ↗opendecider-small-GGUF
manjunathshivaGGUF 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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查看详情 ↗opendecider-small-td-GGUF
manjunathshiva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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查看详情 ↗Qwen3.6-35B-A3B-tq3a-tqTe-g64
manjunathshiva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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查看详情 ↗Qwen3.8-27B-tq3-mini-g64
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查看详情 ↗Qwen3.8-Flash-Next-tq4a-tq2e-g64
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查看详情 ↗The latest version of the model is always available online at PornWorks.com - best nsfw neural generator We have done a lot of work to create a model that follows the hint perfectly, producing the highest quality NSFW output images even without using ADetailer and Upscaler
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查看详情 ↗gemma-4-26B-A4B-it-assistant-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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查看详情 ↗gemma-4-A4B-98e-v6-coder-it-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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查看详情 ↗gemma-4-A4B-98e-v7-coder-it-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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查看详情 ↗gemma-4-A4B-98e-v7-coderx-it-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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查看详情 ↗gemma-4-E2B-it-assistant-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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查看详情 ↗JackOD-9B-Coder
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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查看详情 ↗JackOD-9B-Coder-MTP-DFlash-GGUF
ManniX-ITA**`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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查看详情 ↗JackOD-9B-Coder-MTP-GGUF
ManniX-ITAGGUF 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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查看详情 ↗opencoti-llamafile
ManniX-ITASelf-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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查看详情 ↗Ornith-1.5-27B-A3B-Coder-MTP-GGUF
ManniX-ITAGGUF 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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查看详情 ↗Ornith-1.5-27B-A3B-CoderX-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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查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。