Image-Text-to-Text
Transformers
Safetensors
trinity_vlm
text-generation
vision-language-model
multimodal
custom_code
trinity
moondream
conversational
Instructions to use NyxKrage/TrinityVLM-Nano with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NyxKrage/TrinityVLM-Nano with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="NyxKrage/TrinityVLM-Nano", trust_remote_code=True) messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("NyxKrage/TrinityVLM-Nano", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use NyxKrage/TrinityVLM-Nano with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "NyxKrage/TrinityVLM-Nano" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "NyxKrage/TrinityVLM-Nano", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/NyxKrage/TrinityVLM-Nano
- SGLang
How to use NyxKrage/TrinityVLM-Nano 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 "NyxKrage/TrinityVLM-Nano" \ --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": "NyxKrage/TrinityVLM-Nano", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'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 "NyxKrage/TrinityVLM-Nano" \ --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": "NyxKrage/TrinityVLM-Nano", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Docker Model Runner
How to use NyxKrage/TrinityVLM-Nano with Docker Model Runner:
docker model run hf.co/NyxKrage/TrinityVLM-Nano
File size: 3,568 Bytes
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"architectures": [
"TrinityVLMForConditionalGeneration"
],
"auto_map": {
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"AutoModelForCausalLM": "modeling_trinity_vlm.TrinityVLMForConditionalGeneration"
},
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"model_type": "trinity_vlm",
"output_router_logits": false,
"pad_token_id": 12,
"text_config": {
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"model_type": "afmoe",
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"mup_enabled": true,
"n_group": 1,
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"num_dense_layers": 2,
"num_expert_groups": 1,
"num_experts": 128,
"num_experts_per_tok": 8,
"num_hidden_layers": 56,
"num_key_value_heads": 2,
"num_limited_groups": 1,
"num_shared_experts": 1,
"pad_token_id": 12,
"rms_norm_eps": 1e-05,
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}
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