Image-Text-to-Text
Transformers
Safetensors
kimi_k25
feature-extraction
compressed-tensors
conversational
custom_code
Instructions to use fxmarty/Kimi-K2.5-tiny-2-layers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fxmarty/Kimi-K2.5-tiny-2-layers with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="fxmarty/Kimi-K2.5-tiny-2-layers", trust_remote_code=True) messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("fxmarty/Kimi-K2.5-tiny-2-layers", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use fxmarty/Kimi-K2.5-tiny-2-layers with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "fxmarty/Kimi-K2.5-tiny-2-layers" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "fxmarty/Kimi-K2.5-tiny-2-layers", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/fxmarty/Kimi-K2.5-tiny-2-layers
- SGLang
How to use fxmarty/Kimi-K2.5-tiny-2-layers with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "fxmarty/Kimi-K2.5-tiny-2-layers" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "fxmarty/Kimi-K2.5-tiny-2-layers", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "fxmarty/Kimi-K2.5-tiny-2-layers" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "fxmarty/Kimi-K2.5-tiny-2-layers", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use fxmarty/Kimi-K2.5-tiny-2-layers with Docker Model Runner:
docker model run hf.co/fxmarty/Kimi-K2.5-tiny-2-layers
| { | |
| "auto_map": { | |
| "AutoProcessor": "kimi_k25_processor.KimiK25Processor", | |
| "AutoImageProcessor": "kimi_k25_vision_processing.KimiK25VisionProcessor" | |
| }, | |
| "media_proc_cfg": { | |
| "in_patch_limit": 16384, | |
| "patch_size": 14, | |
| "image_mean": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "merge_kernel_size": 2, | |
| "fixed_output_tokens": null, | |
| "patch_limit_on_one_side": 512, | |
| "in_patch_limit_each_frame": 4096, | |
| "in_patch_limit_video": null, | |
| "sample_fps": 2.0, | |
| "max_num_frames_each_video": null, | |
| "temporal_merge_kernel_size": 4, | |
| "timestamp_mode": "hh:mm:ss.fff", | |
| "config_type": "media_proc.processors.moonvit.MoonViTMediaProcessorConfig" | |
| } | |
| } |