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
| { | |
| "_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 | |
| } | |
| } | |