Instructions to use AXERA-TECH/MiniCPM-V-4.6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AXERA-TECH/MiniCPM-V-4.6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="AXERA-TECH/MiniCPM-V-4.6")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AXERA-TECH/MiniCPM-V-4.6", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AXERA-TECH/MiniCPM-V-4.6 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AXERA-TECH/MiniCPM-V-4.6" # 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/MiniCPM-V-4.6", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AXERA-TECH/MiniCPM-V-4.6
- SGLang
How to use AXERA-TECH/MiniCPM-V-4.6 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/MiniCPM-V-4.6" \ --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/MiniCPM-V-4.6", "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/MiniCPM-V-4.6" \ --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/MiniCPM-V-4.6", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AXERA-TECH/MiniCPM-V-4.6 with Docker Model Runner:
docker model run hf.co/AXERA-TECH/MiniCPM-V-4.6
| { | |
| "system_prompt": "", | |
| "model_name": "AXERA-TECH/MiniCPM-V-4.6-AX650-C128-P1152-CTX2047", | |
| "url_tokenizer_model": "minicpm_v46_tokenizer.txt", | |
| "tokenizer_type": "MiniCPMV46VL", | |
| "post_config_path": "post_config.json", | |
| "template_filename_axmodel": "qwen3_5_text_p128_l%d_together.axmodel", | |
| "axmodel_num": 24, | |
| "full_attention_interval": 4, | |
| "layer_types": [ | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "linear_attention", | |
| "full_attention" | |
| ], | |
| "filename_post_axmodel": "qwen3_5_text_post.axmodel", | |
| "filename_tokens_embed": "model.embed_tokens.weight.bfloat16.bin", | |
| "tokens_embed_num": 248094, | |
| "tokens_embed_size": 1024, | |
| "vlm_type": "MiniCPMV46VL", | |
| "filename_image_encoder_axmodel": "minicpmv4_6_vision_448.axmodel", | |
| "vision_cache_dir": "vision_cache", | |
| "vision_width": 448, | |
| "vision_height": 448, | |
| "vision_patch_size": 14, | |
| "b_use_mmap_load_embed": true, | |
| "b_use_mmap_load_layer": true, | |
| "devices": [ | |
| 0 | |
| ] | |
| } | |