The DwarfStar (ds4) Q2 build of DeepSeek-V4.1-Flash-1017 with DeepSeek's refusal direction projected out of the weights. Built by the team behind Atomic Chat , the app that runs open-weight models locally and offline. - This is antirez/deepseek-v4.1-flash-gguf's `DeepSeek-V4.1-Flash-1017-IQ2_XXS-Q2_K-imatrix.gguf` (revision `9dcee51692e6`) with **only the 122 tensors that write into the residual stream replaced**: attention output (`attn_output_b`), shared-expert down projection, the down projection of all 384 routed experts of every block, and Engram's value projection. Every other byte, the header included, is the original's, so it runs wherever that file runs, in the same memory. - The replaced tensors carry the edit of AtomicChat/DeepSeek-V4.1-Flash-1017-Uncensored-GGUF (one direction, strength 2.0), baked into DeepSeek's weights at full precision and quantized to the original file's types (Q8_0, Q2_K with an importance matrix, F16). After the bake, 99.98% of the edit is in every writer.

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DeepSeek-V4.1-Flash-1017-Uncensored-GGUF

AtomicChat

DeepSeek's refusal direction projected out of the released weights, baked into GGUF builds and measured against the original. **`2.67bpw-IQ1_M` costs knowledge.** It answers without refusals like the other build, but at 1 bit the edit takes MMLU from 85.6% to 81.8% (500 questions, p = 0.003; our gate allows 1.5 points), and it did not pass that gate. With thinking on it also goes round more often (3/30 looping traces at low effort and 5/30 at high on JailbreakBench). It is here for machines with about 140 GB for the weights; if `3.58bpw-Q2_K` fits, take that one. Neutral held-out text, 24 chunks of 4096 tokens, against Unsloth's UD-Q8_K_XL (the converter's BF16 + native MXFP4 output of the released checkpoint) as the reference.

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DeepSeek-V4-Flash-0731-GGUF

AtomicChat

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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embeddinggemma-2-GGUF

AtomicChat

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。

向量与相似度

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Flint-GGUF

AtomicChat

Built from RecursiveSI's original weights with our own importance matrix. The calibration corpora behind our builds are public. Built by the team behind Atomic Chat , the app that runs open-weight models locally and offline. Flint is RecursiveSI's 39.8B mixture-of-experts coding model. About 2.5B parameters are active per token. See our measurements below, including how these files compare to other builds of Flint. Generate about 1.5 times faster with the DFlash2 draft model: the command and our speed measurements are in Faster with DFlash2 . Every number below is measured, not estimated. How we measured it is at the bottom, and the raw logs are in the metrics repo so you can check any of it yourself.

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COST EXPLORER

好模型，也要算好成本。

费用分析

调整月度 Token 用量，直观比较不同模型的预估 API 开支。

每月输入 Token 10 M每月输出 Token 2 M1 M = 100 万 Token · 美元计价
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

选型与常见问题

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。

找到适合你的 AI 模型。

探索 AI 模型 84991

Ornith-1.5-9B-Heretic-MultiFormat

Qwable-Claude-Fable-5-Distilled-NPU2

Qwen3.8-27B-Heretic-MTP-GGUF

Qwen3.8-27B-NPU2

Qwen3.8-35B-A3B-Distill-GGUF

Qwen3.8-9B-Distill-GGUF

Qwen3.8-9B-Distill-uncensored-heretic-Q4_K_M-GGUF

Qwen3.8-Distilled-2B-NPU2

Qwen3.8-Distilled-4B-NPU2

Qwen3.8-Distilled-9B-NPU2

Qwen3.8-Distilled-9B-NPU2-GGUF

Qwen3.8-Flash-Next-APEX-GGUF

Qwen3.8-Flash-Next-Prune11264-124B-GGUF

Qwen3.8-Whittle-MoE-27B-A17.8B

Qwen3.8-Whittle-MoE-27B-A17.8B-GGUF

Qwopus3.5-9B-Coder-NPU2

Beam-1.0-501B-A23B-GGUF

d1-3B-GGUF

d1-omni-600M-GGUF

DeepSeek-V4.1-Flash-1017-Uncensored-DwarfStar-GGUF

DeepSeek-V4.1-Flash-1017-Uncensored-GGUF

DeepSeek-V4-Flash-0731-GGUF

embeddinggemma-2-GGUF

Flint-GGUF

暂未找到匹配模型

好模型，也要算好成本。

读懂模型，再做选择

资讯看趋势，工具提效率，模型筑可能。