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
| license: mit | |
| base_model: | |
| - moonshotai/Kimi-K3 | |
| library_name: transformers | |
| tags: | |
| - compressed-tensors | |
| - LLM Compressor | |
| - vLLM | |
| # 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 | |
| ```bash | |
| 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](https://communityinviter.com/apps/vllm-dev/join-vllm-developers-slack) or ksayers@redhat.com. | |
| ## Evaluation ## | |
| | Benchmark | `moonshotai/Kimi-K3` | `RedHatAI/Kimi-K3-NVFP4` | |
| | - | - | -| | |
| | GPQA | 93.5 | 91.0 | | |
| ```bash | |
| inspect eval hf/Idavidrein/gpqa/diamond \ | |
| --model vllm/RedHatAI/Kimi-K3-NVFP4 \ | |
| --reasoning-effort high \ | |
| --model-base-url http://localhost:8000/v1 | |
| ``` | |