Instructions to use devmanpreet/gemma-3-4b-pt-object-detection-aug with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devmanpreet/gemma-3-4b-pt-object-detection-aug with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="devmanpreet/gemma-3-4b-pt-object-detection-aug")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("devmanpreet/gemma-3-4b-pt-object-detection-aug") model = AutoModelForMultimodalLM.from_pretrained("devmanpreet/gemma-3-4b-pt-object-detection-aug", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use devmanpreet/gemma-3-4b-pt-object-detection-aug with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "devmanpreet/gemma-3-4b-pt-object-detection-aug" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "devmanpreet/gemma-3-4b-pt-object-detection-aug", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/devmanpreet/gemma-3-4b-pt-object-detection-aug
- SGLang
How to use devmanpreet/gemma-3-4b-pt-object-detection-aug 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 "devmanpreet/gemma-3-4b-pt-object-detection-aug" \ --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": "devmanpreet/gemma-3-4b-pt-object-detection-aug", "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 "devmanpreet/gemma-3-4b-pt-object-detection-aug" \ --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": "devmanpreet/gemma-3-4b-pt-object-detection-aug", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use devmanpreet/gemma-3-4b-pt-object-detection-aug with Docker Model Runner:
docker model run hf.co/devmanpreet/gemma-3-4b-pt-object-detection-aug
| { | |
| "architectures": [ | |
| "Gemma3ForConditionalGeneration" | |
| ], | |
| "boi_token_index": 255999, | |
| "bos_token_id": 2, | |
| "eoi_token_index": 256000, | |
| "eos_token_id": 1, | |
| "hidden_size": 2048, | |
| "ignore_index": -100, | |
| "image_token_index": 257152, | |
| "initializer_range": 0.02, | |
| "mm_tokens_per_image": 256, | |
| "model_type": "gemma3", | |
| "pad_token_id": 0, | |
| "projection_dim": 2048, | |
| "text_config": { | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "attn_logit_softcapping": null, | |
| "cache_implementation": "hybrid", | |
| "final_logit_softcapping": null, | |
| "head_dim": 256, | |
| "hidden_activation": "gelu_pytorch_tanh", | |
| "hidden_size": 2048, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 16384, | |
| "max_position_embeddings": 131072, | |
| "model_type": "gemma3_text", | |
| "num_attention_heads": 8, | |
| "num_hidden_layers": 18, | |
| "num_image_tokens": 256, | |
| "num_key_value_heads": 1, | |
| "query_pre_attn_scalar": 256, | |
| "rms_norm_eps": 1e-06, | |
| "rope_local_base_freq": 10000.0, | |
| "rope_scaling": null, | |
| "rope_theta": 1000000.0, | |
| "sliding_window": 4096, | |
| "sliding_window_pattern": 6, | |
| "torch_dtype": "bfloat16", | |
| "use_cache": true, | |
| "vocab_size": 257155 | |
| }, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.52.3", | |
| "vision_config": { | |
| "attention_dropout": 0.0, | |
| "hidden_act": "gelu_pytorch_tanh", | |
| "hidden_size": 1152, | |
| "image_size": 224, | |
| "intermediate_size": 4304, | |
| "layer_norm_eps": 1e-06, | |
| "model_type": "siglip_vision_model", | |
| "num_attention_heads": 16, | |
| "num_channels": 3, | |
| "num_hidden_layers": 27, | |
| "num_image_tokens": 256, | |
| "patch_size": 14, | |
| "projection_dim": 2048, | |
| "projector_hidden_act": "gelu_fast", | |
| "torch_dtype": "bfloat16", | |
| "vision_use_head": false | |
| }, | |
| "vocab_size": 257216 | |
| } | |