Text Generation
Transformers
Safetensors
English
k2_horizon
k2-horizon
375b
Mixture of Experts
open-weights
ifm
conversational
custom_code
Instructions to use IFM/K2-Horizon-375B-A23B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IFM/K2-Horizon-375B-A23B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="IFM/K2-Horizon-375B-A23B", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("IFM/K2-Horizon-375B-A23B", trust_remote_code=True, device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use IFM/K2-Horizon-375B-A23B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "IFM/K2-Horizon-375B-A23B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "IFM/K2-Horizon-375B-A23B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/IFM/K2-Horizon-375B-A23B
- SGLang
How to use IFM/K2-Horizon-375B-A23B 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-Horizon-375B-A23B" \ --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": "IFM/K2-Horizon-375B-A23B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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-Horizon-375B-A23B" \ --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": "IFM/K2-Horizon-375B-A23B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use IFM/K2-Horizon-375B-A23B with Docker Model Runner:
docker model run hf.co/IFM/K2-Horizon-375B-A23B
Download generation_config.json from IFM/K2-Horizon-375B-A23B: direct link, hf CLI and curl.
- Browser
- Download file 81 Bytes
-
https://huggingface.co/IFM/K2-Horizon-375B-A23B/resolve/f1d14854aa6ef3bc08bc0e4f2966af64ebabe7ef/generation_config.json
- Command line
-
hf download hf://IFM/K2-Horizon-375B-A23B@f1d14854aa6ef3bc08bc0e4f2966af64ebabe7ef/generation_config.json
-
curl -L -o generation_config.json https://huggingface.co/IFM/K2-Horizon-375B-A23B/resolve/f1d14854aa6ef3bc08bc0e4f2966af64ebabe7ef/generation_config.json
81 Bytes
| { | |
| "bos_token_id": 0, | |
| "eos_token_id": [ | |
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| 250019 | |
| ] | |
| } | |