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
- Xet hash:
- 57065e224688b331b883b025721eb26f357c476c2cffd48c2b7934e1cfc7fd27
- Size of remote file:
- 17 GB
- SHA256:
- a23d133889b2485931ea7b64e33c00dc93605f2ddb9593a66af2b86845749d13
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.