Image-Text-to-Text
Transformers
Safetensors
vision
multimodal
decision-engine
non-autoregressive
classification
structured-output
agent-tools
function-calling
Instructions to use jaswanthsanjay88/rev-vision with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jaswanthsanjay88/rev-vision with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="jaswanthsanjay88/rev-vision")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jaswanthsanjay88/rev-vision", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use jaswanthsanjay88/rev-vision with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jaswanthsanjay88/rev-vision" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jaswanthsanjay88/rev-vision", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/jaswanthsanjay88/rev-vision
- SGLang
How to use jaswanthsanjay88/rev-vision 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 "jaswanthsanjay88/rev-vision" \ --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": "jaswanthsanjay88/rev-vision", "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 "jaswanthsanjay88/rev-vision" \ --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": "jaswanthsanjay88/rev-vision", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use jaswanthsanjay88/rev-vision with Docker Model Runner:
docker model run hf.co/jaswanthsanjay88/rev-vision
File size: 587 Bytes
3d77291 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | {
"add_prefix_space": false,
"backend": "tokenizers",
"bos_token": "<|im_start|>",
"clean_up_tokenization_spaces": false,
"eos_token": "<end_of_utterance>",
"errors": "replace",
"extra_special_tokens": [
"<fake_token_around_image>",
"<image>",
"<end_of_utterance>"
],
"is_local": false,
"legacy": false,
"local_files_only": false,
"model_max_length": 8192,
"pad_token": "<|im_end|>",
"processor_class": "Idefics3Processor",
"tokenizer_class": "GPT2Tokenizer",
"truncation_side": "left",
"unk_token": "<|endoftext|>",
"vocab_size": 49152
}
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