Instructions to use AXERA-TECH/MiniCPM5-1B-AX637 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AXERA-TECH/MiniCPM5-1B-AX637 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AXERA-TECH/MiniCPM5-1B-AX637")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AXERA-TECH/MiniCPM5-1B-AX637", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AXERA-TECH/MiniCPM5-1B-AX637 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AXERA-TECH/MiniCPM5-1B-AX637" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AXERA-TECH/MiniCPM5-1B-AX637", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AXERA-TECH/MiniCPM5-1B-AX637
- SGLang
How to use AXERA-TECH/MiniCPM5-1B-AX637 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 "AXERA-TECH/MiniCPM5-1B-AX637" \ --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": "AXERA-TECH/MiniCPM5-1B-AX637", "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 "AXERA-TECH/MiniCPM5-1B-AX637" \ --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": "AXERA-TECH/MiniCPM5-1B-AX637", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AXERA-TECH/MiniCPM5-1B-AX637 with Docker Model Runner:
docker model run hf.co/AXERA-TECH/MiniCPM5-1B-AX637
File size: 696 Bytes
aa5c065 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | {
"binary": "bin/axllm",
"target": "AX637 aarch64",
"ax_llm_branch": "ax-minicpm-v-4-6",
"ax_llm_commit": "68bc8e3",
"sha256": "a2192f8ea1dac22b08ffe40e7de724b63396d6958f7dd01c433453c572e4753c",
"model": "AXERA-TECH/MiniCPM5-1B-AX637-C128-P896-CTX1024",
"profile": {
"chip": "AX637",
"prefill_len": 128,
"prefill_max_token_num": 896,
"kv_cache_len": 1024,
"last_kv_cache_len": [128, 256, 384, 512, 640, 768]
},
"verified": {
"date": "2026-07-23",
"board": "AX637",
"status": "passed",
"result": "All 24 decoder layers and post model loaded; a 846-token request exercised all six prefill ladder groups and returned the expected answer."
}
}
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