Model CoverageVision Language Models (VLMs)

Kimi-VL

View as Markdown

Kimi-VL and Kimi-K25-VL are vision language models from Moonshot AI. Kimi-VL-A3B uses a MoE language backbone (3B active parameters) with a vision encoder, supporting image understanding and multimodal reasoning.

TaskImage-Text-to-Text
ArchitectureKimiVLForConditionalGeneration
Parameters~3B active (MoE)
HF Orgmoonshotai

Available Models

  • Kimi-VL-A3B-Instruct
  • Kimi-K25-VL

Architecture

  • KimiVLForConditionalGeneration

Example HF Models

ModelHF ID
Kimi-VL-A3B-Instructmoonshotai/Kimi-VL-A3B-Instruct

Example Recipes

RecipeDatasetDescription
kimi2vl_cordv2.yamlcord-v2SFT — Kimi-VL on CORD-v2
kimi25vl_medpix.yamlMedPix-VQASFT — Kimi-K25-VL on MedPix

Try with NeMo AutoModel

1. Install (full instructions):

$pip install nemo-automodel

2. Clone the repo to get the example recipes:

$git clone https://github.com/NVIDIA-NeMo/Automodel.git
$cd Automodel

3. Run the recipe from inside the repo:

$automodel --nproc-per-node=8 examples/vlm_finetune/kimi/kimi2vl_cordv2.yaml

1. Pull the container and mount a checkpoint directory:

$docker run --gpus all -it --rm \
>  --shm-size=8g \
>  -v $(pwd)/checkpoints:/opt/Automodel/checkpoints \
>  nvcr.io/nvidia/nemo-automodel:26.04.00

2. Navigate to the AutoModel directory (where the recipes are):

$cd /opt/Automodel

3. Run the recipe:

$automodel --nproc-per-node=8 examples/vlm_finetune/kimi/kimi2vl_cordv2.yaml

See the Installation Guide and VLM Fine-Tuning Guide.

Fine-Tuning

See the VLM Fine-Tuning Guide.

Hugging Face Model Cards