Instructions to use vikp/texify with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vikp/texify with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="vikp/texify")# Load model directly from transformers import AutoTokenizer, AutoModelForImageTextToText tokenizer = AutoTokenizer.from_pretrained("vikp/texify") model = AutoModelForImageTextToText.from_pretrained("vikp/texify") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use vikp/texify with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "vikp/texify" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "vikp/texify", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/vikp/texify
- SGLang
How to use vikp/texify 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 "vikp/texify" \ --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": "vikp/texify", "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 "vikp/texify" \ --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": "vikp/texify", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use vikp/texify with Docker Model Runner:
docker model run hf.co/vikp/texify
Upload model
Browse files- config.json +7 -7
- model.safetensors +2 -2
config.json
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"_name_or_path": "
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"VisionEncoderDecoderModel"
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"1": "LABEL_1"
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"is_decoder": false,
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"torch_dtype": null,
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"torchscript": false,
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"typical_p": 1.0,
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"use_2d_embeddings":
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"use_absolute_embeddings": false,
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"use_bfloat16": false,
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"window_size":
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"is_encoder_decoder": true,
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"model_type": "vision-encoder-decoder",
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"pad_token_id": 1,
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"tie_word_embeddings": false,
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"transformers_version": "4.
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}
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"_name_or_path": "texify/checkpoint-12000",
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"architectures": [
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"VisionEncoderDecoderModel"
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],
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"1": "LABEL_1"
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"image_size": [
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"initializer_range": 0.02,
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"is_decoder": false,
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"torch_dtype": null,
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"torchscript": false,
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"typical_p": 1.0,
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"use_2d_embeddings": false,
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"use_absolute_embeddings": false,
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"use_bfloat16": false,
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"window_size": 5
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},
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"is_encoder_decoder": true,
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"model_type": "vision-encoder-decoder",
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"pad_token_id": 1,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.36.0"
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}
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model.safetensors
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