clef-flash-mlx-8bit
TrevorJS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 ↗DISCOVER & COMPARE
探索 AI 模型 14
gemma-4-12B-it-uncensored
TrevorJS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-4-12B-it-uncensored-GGUF
TrevorJS> Requires a llama.cpp build from **2026-06-04 or later** (PR #24118, which added `Gemma4Unified` conversion + inference support). Earlier builds will fail to load these files. These are GGUF quantizations of TrevorJS/gemma-4-12B-it-uncensored, an abliterated (uncensored) version of google/gemma-4-12B-it. Refusal behavior has been removed using norm-preserving biprojected abliteration. See the bf16 model card for full method details, before/after refusal rates, and cross-dataset validation results.
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查看详情 ↗gemma-4-26B-A4B-it-uncensored
TrevorJS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 ↗GGUF quantizations of TrevorJS/gemma-4-26B-A4B-it-uncensored. These are GGUF quantizations of TrevorJS/gemma-4-26B-A4B-it-uncensored, an abliterated (uncensored) version of google/gemma-4-26B-A4B-it. Refusal behavior has been removed using norm-preserving biprojected abliteration with Expert-Granular Abliteration (EGA) for MoE expert weights. See the bf16 model card for full method details, before/after refusal rates, and cross-dataset validation results.
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查看详情 ↗gemma-4-31B-it-uncensored
TrevorJS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-4-31B-it-uncensored-GGUF
TrevorJSThese are GGUF quantizations of TrevorJS/gemma-4-31B-it-uncensored, an abliterated (uncensored) version of google/gemma-4-31B-it. Refusal behavior has been removed using norm-preserving biprojected abliteration. See the bf16 model card for full method details, before/after refusal rates, and cross-dataset validation results.
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查看详情 ↗gemma-4-E2B-it-uncensored
TrevorJSEvery flagged refusal was manually audited. Most are "refusal-then-comply" false positives where the model adds an AI identity disclaimer then answers the question anyway. Norm-preserving biprojected abliteration (grimjim, Nov 2025). Each weight row is decomposed into magnitude + direction, the refusal direction is projected out of the direction component only, then recombined with the original magnitude — guaranteeing `||W_new|| = ||W_orig||`. 1. Load model in bf16 with LoRA adapters on `o_proj` and `mlp.down_proj` 2. Collect residual activations for 400 harmful + 400 harmless prompts (mlabonne datasets) 3. Winsorize activations at 99.5th percentile (clamps GeGLU outlier activations in Gemma family) 4. Compute per-layer refusal direction: `normalize(mean(harmful) - mean(harmless))` 5. Orthogonalize each direction against harmless mean (double-pass Gram-Schmidt) 6. Apply norm-preserving weight modification to `o_proj` and `down_proj` in all layers 9. Merge LoRA adapters into base weights for clean tensor names
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查看详情 ↗gemma-4-E2B-it-uncensored-GGUF
TrevorJSGGUF quantizations of TrevorJS/gemma-4-E2B-it-uncensored. These are GGUF quantizations of TrevorJS/gemma-4-E2B-it-uncensored, an abliterated (uncensored) version of google/gemma-4-E2B-it. Refusal behavior has been removed using norm-preserving biprojected abliteration. See the bf16 model card for full method details, before/after refusal rates, and cross-dataset validation results.
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查看详情 ↗gemma-4-E4B-it-uncensored
TrevorJS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。
基础资料已收录 · 缺失资料后续补充
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗gemma-4-E4B-it-uncensored-GGUF
TrevorJSGGUF quantizations of TrevorJS/gemma-4-E4B-it-uncensored. These are GGUF quantizations of TrevorJS/gemma-4-E4B-it-uncensored, an abliterated (uncensored) version of google/gemma-4-E4B-it. Refusal behavior has been removed using norm-preserving biprojected abliteration. See the bf16 model card for full method details, before/after refusal rates, and cross-dataset validation results.
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查看详情 ↗MiMo-V2.6-Flash-RL-GGUF
TrevorJS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 ↗Muse-Glimmer-30B-uncensored-GGUF
TrevorJS> Requires a llama.cpp build **b10353 or later** (#26841, merged 2026-08-10, commit `62bf73d`, which added `muse_glimmer` conversion + inference support). Earlier builds will fail to load these files. The `mmproj` file is the vision projector and is required for image input; text-only inference does not need it. These are GGUF quantizations of TrevorJS/Muse-Glimmer-30B-uncensored, an abliterated (uncensored) version of meta-models/Muse-Glimmer-30B. Refusal behavior has been removed using norm-preserving biprojected abliteration.
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查看详情 ↗Qwen3-Omni-30B-A3B-GGUF
TrevorJS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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价格待核验多模态与渠道费用须分别核对
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。