Feature Extraction
Transformers
Safetensors
kimi_k3
compressed-tensors
LLM Compressor
vLLM
custom_code
8-bit precision
Instructions to use RedHatAI/Kimi-K3-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RedHatAI/Kimi-K3-NVFP4 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="RedHatAI/Kimi-K3-NVFP4", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("RedHatAI/Kimi-K3-NVFP4", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
RedHatAI/Kimi-K3-NVFP4
This is a quantized version of moonshotai/Kimi-K3 with MoE layers quantized to NVFP4 for accelerated inference.
Usage
This model is intended for deployment with vLLM. You can serve the model using
vllm serve RedHatAI/Kimi-K3-NVFP4 \
--tensor-parallel-size 8 \
--trust_remote_code \
--load-format instanttensor \
--reasoning-parser kimi_k3 \
--language-model-only # optional
May require https://github.com/vllm-project/vllm/pull/50500 to run
Creation Process
This model was compressed using LLM Compressor. For more information, please contact Kyle Sayers via the vLLM Developers Slack or ksayers@redhat.com.
Evaluation
| Benchmark | moonshotai/Kimi-K3 |
RedHatAI/Kimi-K3-NVFP4 |
|---|---|---|
| GPQA | 93.5 | 91.0 |
inspect eval hf/Idavidrein/gpqa/diamond \
--model vllm/RedHatAI/Kimi-K3-NVFP4 \
--reasoning-effort high \
--model-base-url http://localhost:8000/v1
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Model tree for RedHatAI/Kimi-K3-NVFP4
Base model
moonshotai/Kimi-K3