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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("vikp/texify") model = AutoModelForMultimodalLM.from_pretrained("vikp/texify", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- 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
Commit History
-
Upload processor
ce49c1f -
Upload model
b172999
-
Upload processor
35cc518 -
Upload model
555ea2a
-
Update README.md
8bcdc99
-
Upload processor
72d4010 -
Upload model
92f0394 -
Upload processor
dfcae78 -
Upload model
36d4227 -
Upload processor
67566c3 -
Upload model
25099af -
Upload processor
adbdce6 -
Upload model
f40afec
-
Create README.md
2beeba9 -
Upload processor
950b0f9 -
Upload model
7b0b371 -
Upload processor
229f81a -
Upload model
6a194c9 -
Upload processor
bb2297f -
Upload model
0ed9a53
-
Upload processor
4a375f0 -
Upload model
56184e3 -
Upload processor
f432883 -
Upload model
8912572 -
Upload processor
f107b59 -
Upload model
1bcf988 -
Upload processor
91b17c9 -
Upload model
8a6e048
-
Training in progress, step 8000
37694d6 -
Training in progress, step 4000
0db1950 -
Training in progress, step 8000
b02dfdc -
Training in progress, step 4000
946a756 -
Training in progress, step 8000
47557de
-
Training in progress, step 4000
29e469e -
Upload processor
0376a4e -
Upload model
1c08483 -
initial commit
0689e12