Any-to-Any
Transformers
Safetensors
kimi_k3
feature-extraction
kimi-k3
compressed-tensors
conversational
abliterated
uncensored
custom_code
8-bit precision
Instructions to use SHSLab/Kimi-K3-Abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SHSLab/Kimi-K3-Abliterated with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("SHSLab/Kimi-K3-Abliterated", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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base_model_relation: finetune
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tags:
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- kimi-k3
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-
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license: other
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license_name: "kimi-k3"
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library_name: transformers
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**SHS-Lab/Kimi-K3-Abliterated** is an open-weight, natively multimodal agentic model derived from [Kimi K3](https://huggingface.co/moonshotai/Kimi-K3) through a targeted abliteration procedure. The base architecture is a 2.8-trillion-parameter Mixture-of-Experts network built on Kimi Delta Attention (KDA) and Attention Residuals (AttnRes), featuring native vision capabilities and a 1-million-token context window.
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This variant has been post-processed to attenuate alignment-driven refusal mechanisms. The underlying architecture, parameter count, context length, and multimodal capabilities remain identical to the base model.
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The release is intended for alignment research, red-team evaluation, and controlled experimentation in governed environments.
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</tr>
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<tr>
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<td align="center" style="vertical-align: middle; text-align: center"><strong>Modality</strong></td>
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<td align="center" style="vertical-align: middle; text-align: center">Text, Image</td>
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</tr>
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</tbody>
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</table>
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## 4. Evaluation Results
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> [!IMPORTANT]
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> All benchmark scores in this section were reported for the **base Kimi K3 model** by its original developers. No independent evaluation campaign has been conducted on this abliterated variant. These results are reproduced as an upper-bound reference for inherited capability.
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<div align="center">
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<table>
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vllm serve SHS-Lab/Kimi-K3-Abliterated \
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--tensor-parallel-size 8 \
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--trust-remote-code \
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--max-model-len
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--enable-auto-tool-choice \
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--tool-call-parser kimi_k3
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```
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base_model_relation: finetune
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tags:
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- kimi-k3
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- compressed-tensors
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- conversational
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- abliterated
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- uncensored
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license: other
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license_name: "kimi-k3"
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library_name: transformers
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**SHS-Lab/Kimi-K3-Abliterated** is an open-weight, natively multimodal agentic model derived from [Kimi K3](https://huggingface.co/moonshotai/Kimi-K3) through a targeted abliteration procedure. The base architecture is a 2.8-trillion-parameter Mixture-of-Experts network built on Kimi Delta Attention (KDA) and Attention Residuals (AttnRes), featuring native vision capabilities and a 1-million-token context window.
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This variant has been post-processed to attenuate alignment-driven refusal mechanisms. The underlying architecture, parameter count, context length, and multimodal capabilities remain identical to the base model. Abliteration modifies weight values to suppress safeguard activations without altering the structural design or introducing new training data.
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The release is intended for alignment research, red-team evaluation, and controlled experimentation in governed environments.
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</tr>
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<tr>
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<td align="center" style="vertical-align: middle; text-align: center"><strong>Modality</strong></td>
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<td align="center" style="vertical-align: middle; text-align: center">Text, Image, Video</td>
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</tr>
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</tbody>
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</table>
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## 4. Evaluation Results
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> [!IMPORTANT]
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> All benchmark scores in this section were reported for the **base Kimi K3 model** by its original developers. No independent evaluation campaign has been conducted on this abliterated variant. These results are reproduced as an upper-bound reference for inherited capability. Abliteration targets refusal behavior and is not expected to materially alter benchmark performance, but users should validate task-specific accuracy independently before relying on this variant in production or research settings.
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<div align="center">
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<table>
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vllm serve SHS-Lab/Kimi-K3-Abliterated \
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--tensor-parallel-size 8 \
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--trust-remote-code \
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--max-model-len 1,048,576 \
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--enable-auto-tool-choice \
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--tool-call-parser kimi_k3
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```
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