Instructions to use ACIDE/User-VLM-3B-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ACIDE/User-VLM-3B-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="ACIDE/User-VLM-3B-base")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("ACIDE/User-VLM-3B-base") model = AutoModelForImageTextToText.from_pretrained("ACIDE/User-VLM-3B-base") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use ACIDE/User-VLM-3B-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ACIDE/User-VLM-3B-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ACIDE/User-VLM-3B-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ACIDE/User-VLM-3B-base
- SGLang
How to use ACIDE/User-VLM-3B-base 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 "ACIDE/User-VLM-3B-base" \ --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": "ACIDE/User-VLM-3B-base", "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 "ACIDE/User-VLM-3B-base" \ --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": "ACIDE/User-VLM-3B-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ACIDE/User-VLM-3B-base with Docker Model Runner:
docker model run hf.co/ACIDE/User-VLM-3B-base
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"_name_or_path": "google/paligemma2-3b-ft-docci-448",
"_vocab_size": 257152,
"architectures": [
"PaliGemmaForConditionalGeneration"
],
"bos_token_id": 2,
"eos_token_id": 1,
"hidden_size": 2048,
"image_token_index": 257152,
"model_type": "paligemma",
"num_hidden_layers": 26,
"pad_token_id": 0,
"projection_dim": 2304,
"text_config": {
"architectures": [
"Gemma2ForCausalLM"
],
"attn_logit_softcapping": 50.0,
"cache_implementation": "hybrid",
"eos_token_id": [
1,
107
],
"final_logit_softcapping": 30.0,
"hidden_act": "gelu_pytorch_tanh",
"hidden_activation": "gelu_pytorch_tanh",
"hidden_size": 2304,
"intermediate_size": 9216,
"model_type": "gemma2",
"num_hidden_layers": 26,
"num_image_tokens": 1024,
"num_key_value_heads": 4,
"query_pre_attn_scalar": 256,
"sliding_window": 4096,
"torch_dtype": "bfloat16",
"vocab_size": 257216
},
"torch_dtype": "bfloat16",
"transformers_version": "4.48.0.dev0",
"vision_config": {
"hidden_size": 1152,
"image_size": 448,
"intermediate_size": 4304,
"model_type": "siglip_vision_model",
"num_attention_heads": 16,
"num_hidden_layers": 27,
"num_image_tokens": 1024,
"num_positions": 256,
"patch_size": 14,
"projection_dim": 2304,
"torch_dtype": "bfloat16",
"vision_use_head": false
}
}
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