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
add message.content check to prevent UndefinedError
Browse filesIf an assistant message lacks content (e.g., tool-call-only messages), accessing message.content will cause a Jinja2 UndefinedError
- chat_template.jinja +1 -1
chat_template.jinja
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@@ -42,7 +42,7 @@
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{%- endif %}
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{%- if add_generation_prompt and not (message.reasoning_content is defined and message.reasoning_content is not none) %}
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{%- if '</think>' in message.content %}
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{%- set content = message.content.split('</think>'.strip())[-1].lstrip('\n') %}
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{%- endif %}
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{%- endif %}
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{%- endif %}
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{%- if add_generation_prompt and not (message.reasoning_content is defined and message.reasoning_content is not none) %}
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+
{%- if message.content is defined and '</think>' in message.content %}
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{%- set content = message.content.split('</think>'.strip())[-1].lstrip('\n') %}
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{%- endif %}
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{%- endif %}
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