Image-Text-to-Text
Transformers
Safetensors
paligemma
llama-factory
conversational
text-generation-inference
Instructions to use hllj/paligemma-3b-mix-224-vi-llava with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hllj/paligemma-3b-mix-224-vi-llava with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="hllj/paligemma-3b-mix-224-vi-llava") 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 AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("hllj/paligemma-3b-mix-224-vi-llava") model = AutoModelForMultimodalLM.from_pretrained("hllj/paligemma-3b-mix-224-vi-llava") 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?"} ] }, ] inputs = processor.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use hllj/paligemma-3b-mix-224-vi-llava with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "hllj/paligemma-3b-mix-224-vi-llava" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hllj/paligemma-3b-mix-224-vi-llava", "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/hllj/paligemma-3b-mix-224-vi-llava
- SGLang
How to use hllj/paligemma-3b-mix-224-vi-llava 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 "hllj/paligemma-3b-mix-224-vi-llava" \ --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": "hllj/paligemma-3b-mix-224-vi-llava", "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 "hllj/paligemma-3b-mix-224-vi-llava" \ --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": "hllj/paligemma-3b-mix-224-vi-llava", "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 hllj/paligemma-3b-mix-224-vi-llava with Docker Model Runner:
docker model run hf.co/hllj/paligemma-3b-mix-224-vi-llava
File size: 1,037 Bytes
e460d73 | 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 | {
"_name_or_path": "google/paligemma-3b-mix-224",
"architectures": [
"PaliGemmaForConditionalGeneration"
],
"bos_token_id": 2,
"eos_token_id": 1,
"hidden_size": 2048,
"ignore_index": -100,
"image_token_index": 257152,
"model_type": "paligemma",
"pad_token_id": 0,
"projection_dim": 2048,
"text_config": {
"hidden_size": 2048,
"intermediate_size": 16384,
"model_type": "gemma",
"num_attention_heads": 8,
"num_hidden_layers": 18,
"num_image_tokens": 256,
"num_key_value_heads": 1,
"torch_dtype": "float32",
"vocab_size": 257216
},
"torch_dtype": "float32",
"transformers_version": "4.42.3",
"use_cache": true,
"vision_config": {
"hidden_size": 1152,
"intermediate_size": 4304,
"model_type": "siglip_vision_model",
"num_attention_heads": 16,
"num_hidden_layers": 27,
"num_image_tokens": 256,
"patch_size": 14,
"projection_dim": 2048,
"projector_hidden_act": "gelu_fast",
"vision_use_head": false
},
"vocab_size": 257216
}
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