Image-Text-to-Text
Transformers
Safetensors
kimi_vl
feature-extraction
agent
video
screenspot
long-context
conversational
custom_code
Instructions to use moonshotai/Kimi-VL-A3B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use moonshotai/Kimi-VL-A3B-Instruct with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="moonshotai/Kimi-VL-A3B-Instruct", 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("moonshotai/Kimi-VL-A3B-Instruct", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use moonshotai/Kimi-VL-A3B-Instruct with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "moonshotai/Kimi-VL-A3B-Instruct" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "moonshotai/Kimi-VL-A3B-Instruct", "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/moonshotai/Kimi-VL-A3B-Instruct
- SGLang
How to use moonshotai/Kimi-VL-A3B-Instruct 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 "moonshotai/Kimi-VL-A3B-Instruct" \ --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": "moonshotai/Kimi-VL-A3B-Instruct", "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 "moonshotai/Kimi-VL-A3B-Instruct" \ --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": "moonshotai/Kimi-VL-A3B-Instruct", "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 moonshotai/Kimi-VL-A3B-Instruct with Docker Model Runner:
docker model run hf.co/moonshotai/Kimi-VL-A3B-Instruct
zhouzaida commited on
Commit ·
da9637b
1
Parent(s): b13cf2f
tokenizer can decode tensor for vllm test
Browse files- tokenization_moonshot.py +3 -0
tokenization_moonshot.py
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@@ -16,6 +16,7 @@ from shutil import copyfile
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from tiktoken.load import load_tiktoken_bpe
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from tokenizers import AddedToken
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from transformers.tokenization_utils import PreTrainedTokenizer
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from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode
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if len(kwargs) > 0:
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return super().decode(token_ids, **kwargs)
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if type(token_ids) is int:
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token_ids = [token_ids]
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from tiktoken.load import load_tiktoken_bpe
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from tokenizers import AddedToken
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from transformers.tokenization_utils import PreTrainedTokenizer
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from transformers.utils import to_py_obj
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from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode
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if len(kwargs) > 0:
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return super().decode(token_ids, **kwargs)
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token_ids = to_py_obj(token_ids)
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if type(token_ids) is int:
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token_ids = [token_ids]
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