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
Training in progress, step 4000
Browse files- config.json +2 -2
- model.safetensors +2 -2
- special_tokens_map.json +0 -25
- tokenizer.json +0 -0
- tokenizer_config.json +0 -0
- training_args.bin +1 -1
config.json
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"decoder_attention_heads": 16,
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"decoder_ffn_dim": 4096,
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"decoder_layerdrop": 0.0,
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"decoder_layers":
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"decoder_start_token_id": null,
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"diversity_penalty": 0.0,
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"do_sample": false,
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"typical_p": 1.0,
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"use_bfloat16": false,
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"use_cache": true,
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"vocab_size":
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"decoder_start_token_id": 0,
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"encoder": {
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"decoder_attention_heads": 16,
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"decoder_ffn_dim": 4096,
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"decoder_layerdrop": 0.0,
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"decoder_layers": 8,
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"decoder_start_token_id": null,
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"diversity_penalty": 0.0,
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"do_sample": false,
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"typical_p": 1.0,
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"use_bfloat16": false,
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"use_cache": true,
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"vocab_size": 50000
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},
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"decoder_start_token_id": 0,
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"encoder": {
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model.safetensors
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special_tokens_map.json
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{
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"additional_special_tokens": [
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"<s_iitcdip>",
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"<s_synthdog>"
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],
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"bos_token": {
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"lstrip": false,
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"rstrip": false,
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"cls_token": {
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"eos_token": {
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"mask_token": {
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"pad_token": {
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"rstrip": false,
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tokenizer.json
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tokenizer_config.json
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training_args.bin
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size 4536
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