Instructions to use IFM/K2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IFM/K2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="IFM/K2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("IFM/K2") model = AutoModelForCausalLM.from_pretrained("IFM/K2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use IFM/K2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "IFM/K2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "IFM/K2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/IFM/K2
- SGLang
How to use IFM/K2 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 "IFM/K2" \ --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": "IFM/K2", "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 "IFM/K2" \ --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": "IFM/K2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use IFM/K2 with Docker Model Runner:
docker model run hf.co/IFM/K2
Download pytorch_model-00002-of-00027.bin from IFM/K2: direct link, hf CLI and curl.
- Browser
- Download file 4.86 GB
-
https://huggingface.co/IFM/K2/resolve/61ffa4aff3caa9517cdc7d1c7d4cdc66ed2224d9/pytorch_model-00002-of-00027.bin
- Command line
-
hf download hf://IFM/K2@61ffa4aff3caa9517cdc7d1c7d4cdc66ed2224d9/pytorch_model-00002-of-00027.bin
-
curl -L -o pytorch_model-00002-of-00027.bin https://huggingface.co/IFM/K2/resolve/61ffa4aff3caa9517cdc7d1c7d4cdc66ed2224d9/pytorch_model-00002-of-00027.bin
4.86 GB
- Xet hash:
- 1f4d7c0247911fee5915bf5816d3ed924b12b69916bb38fbd480ba69edaaf11f
- Size of remote file:
- 4.86 GB
- SHA256:
- 5cdc52c599a3ee9c5d8bcebf26e703ec26f06998970e7840aa66bceee3a6325a
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