Instructions to use skt/A.X-K1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use skt/A.X-K1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="skt/A.X-K1", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("skt/A.X-K1", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use skt/A.X-K1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "skt/A.X-K1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "skt/A.X-K1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/skt/A.X-K1
- SGLang
How to use skt/A.X-K1 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 "skt/A.X-K1" \ --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": "skt/A.X-K1", "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 "skt/A.X-K1" \ --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": "skt/A.X-K1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use skt/A.X-K1 with Docker Model Runner:
docker model run hf.co/skt/A.X-K1
Deployment considerations for research
A.X K1 is a high-capacity Mixture-of-Experts language model designed to balance large-scale reasoning with practical inference cost. With 519B total parameters and 33B active parameters per token, 131K token context, and 61 layers (60 MoE + 1 dense), it enables deep reasoning while maintaining feasible latency for interactive use. The Post-MLP RMSNorm further stabilizes training, and the Think/Non-Think modes give users control over the depth of reasoning versus speed.
Evaluations for A.X K1 are scheduled for January 4, 2026, and the model is intended for research-grade deployments with careful resource planning. Running locally with SGLang and vLLM is supported, but practitioners should note possible limitations like potential hallucinations and domain gaps. Cite the technical report if you publish results.