Image-Text-to-Text
Transformers
PyTorch
English
llava
text-generation
dental
medical
multimodal
vision-language
clip
sam
lora
orthopantomography
opg
x-ray
diagnosis
Instructions to use jeffrey423/ToothXpert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jeffrey423/ToothXpert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="jeffrey423/ToothXpert")# Load model directly from transformers import AutoProcessor, AutoModelForCausalLM processor = AutoProcessor.from_pretrained("jeffrey423/ToothXpert") model = AutoModelForCausalLM.from_pretrained("jeffrey423/ToothXpert") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use jeffrey423/ToothXpert with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jeffrey423/ToothXpert" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jeffrey423/ToothXpert", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jeffrey423/ToothXpert
- SGLang
How to use jeffrey423/ToothXpert 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 "jeffrey423/ToothXpert" \ --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": "jeffrey423/ToothXpert", "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 "jeffrey423/ToothXpert" \ --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": "jeffrey423/ToothXpert", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use jeffrey423/ToothXpert with Docker Model Runner:
docker model run hf.co/jeffrey423/ToothXpert
File size: 1,413 Bytes
37d4441 | 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 43 44 45 46 47 48 49 | {
"_name_or_path": "/home/xyliu/.cache/huggingface/hub/models--jeffrey423--llava-v1.5-7b-med-align/",
"architectures": [
"LISAForCausalLM"
],
"bos_token_id": 1,
"eos_token_id": 2,
"freeze_mm_mlp_adapter": true,
"freeze_mm_vision_resampler": false,
"hidden_act": "silu",
"hidden_size": 4096,
"image_aspect_ratio": "square",
"image_grid_pinpoints": null,
"initializer_range": 0.02,
"intermediate_size": 11008,
"max_length": 4096,
"max_position_embeddings": 4096,
"mm_hidden_size": 1024,
"mm_projector_lr": null,
"mm_projector_type": "mlp2x_gelu",
"mm_resampler_type": null,
"mm_use_im_patch_token": false,
"mm_use_im_start_end": true,
"mm_vision_select_feature": "patch",
"mm_vision_select_layer": -2,
"mm_vision_tower": "openai/clip-vit-large-patch14",
"model_type": "llava",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 32,
"out_dim": 256,
"pad_token_id": 0,
"pretrain_mm_mlp_adapter": null,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_scaling": null,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"train_mask_decoder": true,
"transformers_version": "4.30.2",
"tune_mm_mlp_adapter": false,
"tune_mm_vision_resampler": false,
"unfreeze_mm_vision_tower": false,
"use_cache": false,
"use_mm_proj": true,
"vision_tower": "openai/clip-vit-large-patch14",
"vocab_size": 32003
}
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