Image-Text-to-Text
Transformers
Safetensors
kimi_k31
text-generation
kimi-k3.1-pro
mixture-of-experts
pruning
vllm
custom_code
Eval Results (legacy)
8-bit precision
Instructions to use DaoCloud/Kimi-K3.1-Pro-pruned75 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DaoCloud/Kimi-K3.1-Pro-pruned75 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="DaoCloud/Kimi-K3.1-Pro-pruned75", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForSeq2SeqLM model = AutoModelForSeq2SeqLM.from_pretrained("DaoCloud/Kimi-K3.1-Pro-pruned75", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use DaoCloud/Kimi-K3.1-Pro-pruned75 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DaoCloud/Kimi-K3.1-Pro-pruned75" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DaoCloud/Kimi-K3.1-Pro-pruned75", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/DaoCloud/Kimi-K3.1-Pro-pruned75
- SGLang
How to use DaoCloud/Kimi-K3.1-Pro-pruned75 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 "DaoCloud/Kimi-K3.1-Pro-pruned75" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DaoCloud/Kimi-K3.1-Pro-pruned75", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "DaoCloud/Kimi-K3.1-Pro-pruned75" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DaoCloud/Kimi-K3.1-Pro-pruned75", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use DaoCloud/Kimi-K3.1-Pro-pruned75 with Docker Model Runner:
docker model run hf.co/DaoCloud/Kimi-K3.1-Pro-pruned75
Add model-00038-of-000096.safetensors
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model-00038-of-000096.safetensors
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