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探索 AI 模型 84999
This is an uncensored version of oyildirim/CyberStrike-OffSec-35B created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. - **Risk of Sensitive or Controversial Outputs**: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs. - **Not Suitable for All Audiences**: Due to limited content filtering, the model’s outputs may be inappropriate for public settings, underage users, or applications requiring high security.
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查看详情 ↗This time the Maya weights are not provided directly; you need to run the command yourself to apply the patch. For details, Please refer to README-Maya.md in the Maya directory. This time the Strata weights are not provided directly; you need to run the command yourself to apply the patch. For details, Please refer to README-Strata.md in the Strata-deepseek-v4.1-flash-IST-DAS directory. This portion of the weights actually comes from TA-DASLab/DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF. If you want to run it under b11870 or newer, please refer to the llamacpp instructions for the necessary modifications.
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查看详情 ↗This is an uncensored version of deepseek-ai/DeepSeek-V4-Flash-0731 created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. **Note** These GGUFs support llama.cpp and ds4 and all expert modules were not ablated. Q4 used a stronger ablation intensity than Q2, so the rejection rate was smaller.
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查看详情 ↗This is an uncensored version of deepseek-ai/DeepSeek-V4-Flash created with abliteration. This quants are specific for the DS4(antirez/ds4) and llama.cpp inference engine. They may work with other inference engines or not (they should, but not the MTP model which requires a specific loader). 1. The Q2 version has a certain refusal rate. It should be fine for writing code, while the other versions are still under testing.
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查看详情 ↗This is an uncensored version of deepseek-ai/DeepSeek-V4-Flash-Vision-Exp created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. Layers 11-28 are being ablated (1-based indexing) and all expert modules were not ablated. - **Risk of Sensitive or Controversial Outputs**: This model’s safety filtering has been significantly reduced, potentially generating sensitive, controversial, or inappropriate content. Users should exercise caution and rigorously review generated outputs.
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查看详情 ↗This is an uncensored version of yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2-GGUF created with abliteration. If you like it, please click 'like' and follow us for more updates. You can follow x.com/support_huihui to get the latest model information from huihui.ai.
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查看详情 ↗library_name: transformers license: apache-2.0 license_link: https://ai.google.dev/gemma/docs/gemma_4_license pipeline_tag: any-to-any base_model: - google/gemma-4-12B-it-qat-q4_0-unquantized tags: - abliterated - uncensored - GGUF - MTP --- This is an uncensored version of google/gemma-4-12B-it-qat-q4_0-unquantized created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. **Note**: For this model, both the thinking mode and the non-thinking mode have been completely abliterated. Only layers 23-36 have been abliterated.
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查看详情 ↗library_name: transformers license: apache-2.0 license_link: https://ai.google.dev/gemma/docs/gemma_4_license pipeline_tag: any-to-any base_model: - google/gemma-4-26B-A4B-it-qat-q4_0-unquantized tags: - abliterated - uncensored --- This is an uncensored version of google/gemma-4-26B-A4B-it-qat-q4_0-unquantized created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. Please use the latest version of ollama
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查看详情 ↗library_name: transformers license: apache-2.0 license_link: https://ai.google.dev/gemma/docs/gemma_4_license pipeline_tag: any-to-any base_model: - google/gemma-4-31B-it-qat-q4_0-unquantized tags: - abliterated - uncensored --- This is an uncensored version of google/gemma-4-31B-it-qat-q4_0-unquantized created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. Please use the latest version of ollama
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查看详情 ↗This is an uncensored version of google/gemma-4-E2B-it-qat-q4_0-unquantized created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens.
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查看详情 ↗library_name: transformers license: apache-2.0 license_link: https://ai.google.dev/gemma/docs/gemma_4_license pipeline_tag: any-to-any base_model: - google/gemma-4-E4B-it-qat-q4_0-unquantized tags: - abliterated - uncensored --- This is an uncensored version of google/gemma-4-E4B-it-qat-q4_0-unquantized created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. Please use the latest version of ollama
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查看详情 ↗This is an uncensored version of zai-org/GLM-5.3-Flash created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model. The latest version of llama.cpp now officially supports zai-org/GLM-5.3-Flash. We have updated the first file of each quantized version, so you only need to download the first file.
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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。