HiT-MiniLM-L12-WordNetNoun
Hierarchy-Transformers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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从语言到影像,从开源到 API。读懂模型能力、比较真实成本,
为你的下一个想法,找到合适的智能引擎。
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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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This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
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A **baked refusal-ablation** (abliteration) of XiaomiMiMo/MiMo-V2.6-Distill-Qwen-9B (BF16, 9.41B). The refusal direction is **written into the weights themselves**: only **12 MLP tensors at layers 16–19** are edited (rank-1 directional ablation, output side `W ← W − 6.0·u(uᵀW)`, input side `W ← W − 1.0·(Wu)uᵀ`), plus per-layer surface-policy attenuation, a 128-neuron deflection mask on `mlp.down_proj`, and a small `lm_head` term. The other 748 of 760 tensors are bit-identical to the parent (full-tensor verified). - **No runtime hooks, no extra checkpoint keys, no LoRA, no quantization.** It is a plain drop-in BF16 checkpoint: load it anywhere you load the parent. - Config, tokenizer, and chat template are unchanged from the parent.
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> **🚧 PREVIEW RELEASE (v0.1) — feedback wanted.** > This is an early preview of a quant-native abliteration of the Bonsai 2 pack. The > numbers below are from my own small harness, so I would rather have the community > poke at it than call it finished. Please open a **Discussion** in the Community tab > with anything you find — good or bad. **Status: preview (v0.1) — not a final release.** A refusal-reduced build of **`prism-ml/Ternary-Bonsai-2-27B-gguf`** (PQ2_0, 2.13 bpw, 7.21 GB). The pack is edited **in its own deployed quantization format**: there is no BF16 dequantization anywhere in the pipeline and no re-quantization. The official pack's stored 2-bit codes were updated directly, in place, so the shipped artifact *is* the edited pack: same geometry, same size, same kernel path, no runtime hook, no adapter. A full-precision refusal-free reference of the same base family was used only to define *what* to change; that change was then written onto the pack's existing quantization lattice with an **unbiased** code-rounding schedule, so that the *expected* weight movement equals the intended edit even though each individual code can only move in whole lattice steps. The edit was amplitude-swept and selected on measured behaviour.
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> Hard-convert **38/40** on a fixed harmful pilot while keeping coding near the parent — not a max-HC-only scar model. Optional speculative decode: use Prism’s official **DSpark** drafter unchanged (`Ternary-Bonsai-27B-dspark-Q4_1.gguf` from the base repo). Accept-rate A/B on this weight vs parent: **≈1.00×** (n=30, CI overlap) → no draft retrain needed. 1. Residual / direction code-edit on ternary writers over the parent Q2_0 pack 2. Escalate to a high hard-convert seed (`esc-ALL-f65`) 3. **Seed-stage surgical rollback**: restore parent bytes on selected **L0–7 writers** (minimal mask `minM23`, ~1.5 MB changed vs full L0–7 rollback) 4. Gate on paired harmful eval + capability pilots; keep low-degradation winner
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An **integrated MTP (Multi-Token Prediction) GGUF** build of the abliterated `Qwen3.8-Flash-Next-Uncensored` model, quantized to **Q4_K_M** with the MTP draft head **merged into the same 4-shard split** — no sidecar file needed. This model is an **abliterated (refusal-removed)** build. It will comply with harmful, unethical, or illegal requests the original `Qwen3.8-Flash-Next` would refuse. Released **strictly for legitimate research** — interpretability, AI-safety / refusal-mechanism study, red-teaming, and robustness evaluation. **You assume full responsibility** for how you use it and everything it generates; add your own safety and moderation layers before any deployment. **Qwen Community License 1.0** (see LICENSE), inherited from the base model `Qwen/Qwen3.8-Flash-Next`. Abliteration and quantization do not change the underlying license obligations.
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可以调整厂商、分类或输入输出条件;新模型正持续收录。
调整月度 Token 用量,直观比较不同模型的预估 API 开支。
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。
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。
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。
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。