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
File size: 1,490 Bytes
aa969ca 18ff147 aa969ca 1aa6f87 aa969ca | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 | {
"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
],
"vision_num_frames": 18,
"vision_do_sample_frames": true
}
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