Instructions to use trl-internal-testing/tiny-PaliGemmaForConditionalGeneration with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use trl-internal-testing/tiny-PaliGemmaForConditionalGeneration with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="trl-internal-testing/tiny-PaliGemmaForConditionalGeneration")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("trl-internal-testing/tiny-PaliGemmaForConditionalGeneration") model = AutoModelForImageTextToText.from_pretrained("trl-internal-testing/tiny-PaliGemmaForConditionalGeneration") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use trl-internal-testing/tiny-PaliGemmaForConditionalGeneration with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "trl-internal-testing/tiny-PaliGemmaForConditionalGeneration" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "trl-internal-testing/tiny-PaliGemmaForConditionalGeneration", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/trl-internal-testing/tiny-PaliGemmaForConditionalGeneration
- SGLang
How to use trl-internal-testing/tiny-PaliGemmaForConditionalGeneration 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 "trl-internal-testing/tiny-PaliGemmaForConditionalGeneration" \ --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": "trl-internal-testing/tiny-PaliGemmaForConditionalGeneration", "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 "trl-internal-testing/tiny-PaliGemmaForConditionalGeneration" \ --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": "trl-internal-testing/tiny-PaliGemmaForConditionalGeneration", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use trl-internal-testing/tiny-PaliGemmaForConditionalGeneration with Docker Model Runner:
docker model run hf.co/trl-internal-testing/tiny-PaliGemmaForConditionalGeneration
Upload PaliGemmaForConditionalGeneration
Browse files- config.json +2 -1
- generation_config.json +1 -1
- model.safetensors +1 -1
config.json
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"dtype": "float32",
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"head_dim": 256,
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"hidden_act": "gelu_pytorch_tanh",
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"hidden_size": 16,
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"initializer_range": 0.02,
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"intermediate_size": 16384,
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"use_cache": true,
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"vocab_size": 257216
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},
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"transformers_version": "4.57.
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"vision_config": {
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"attention_dropout": 0.0,
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"embed_dim": 64,
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"dtype": "float32",
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"head_dim": 256,
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"hidden_act": "gelu_pytorch_tanh",
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"hidden_activation": null,
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"hidden_size": 16,
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"initializer_range": 0.02,
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"intermediate_size": 16384,
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"use_cache": true,
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"vocab_size": 257216
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},
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"transformers_version": "4.57.3",
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"vision_config": {
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"attention_dropout": 0.0,
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"embed_dim": 64,
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generation_config.json
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"bos_token_id": 2,
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"eos_token_id": 1,
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"transformers_version": "4.57.
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}
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"bos_token_id": 2,
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"eos_token_id": 1,
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"transformers_version": "4.57.3"
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}
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model.safetensors
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size 12250448
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