Instructions to use ucsahin/TraVisionLM-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ucsahin/TraVisionLM-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="ucsahin/TraVisionLM-base", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("ucsahin/TraVisionLM-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ucsahin/TraVisionLM-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ucsahin/TraVisionLM-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ucsahin/TraVisionLM-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ucsahin/TraVisionLM-base
- SGLang
How to use ucsahin/TraVisionLM-base 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 "ucsahin/TraVisionLM-base" \ --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": "ucsahin/TraVisionLM-base", "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 "ucsahin/TraVisionLM-base" \ --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": "ucsahin/TraVisionLM-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ucsahin/TraVisionLM-base with Docker Model Runner:
docker model run hf.co/ucsahin/TraVisionLM-base
| { | |
| "_name_or_path": "ucsahin/TRaVisionLM-base-final-unregistered", | |
| "architectures": [ | |
| "TraVisionForCausalLM" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_travisionlm.TraVisionLMConfig", | |
| "AutoModelForCausalLM": "modeling_travisionlm.TraVisionForCausalLM" | |
| }, | |
| "hidden_size": 1280, | |
| "ignore_index": -100, | |
| "image_token_index": 50257, | |
| "model_type": "travisionlm", | |
| "num_image_tokens": 256, | |
| "projection_dim": 768, | |
| "text_config": { | |
| "architectures": [ | |
| "GPT2LMHeadModel" | |
| ], | |
| "bos_token_id": 0, | |
| "eos_token_id": 0, | |
| "model_type": "gpt2", | |
| "n_ctx": 1024, | |
| "n_embd": 1280, | |
| "n_head": 20, | |
| "n_layer": 36, | |
| "pad_token_id": 0, | |
| "reorder_and_upcast_attn": true, | |
| "scale_attn_by_inverse_layer_idx": true, | |
| "task_specific_params": { | |
| "text-generation": { | |
| "do_sample": true, | |
| "max_length": 50 | |
| } | |
| }, | |
| "torch_dtype": "float32", | |
| "vocab_size": 51282 | |
| }, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.44.0.dev0", | |
| "vision_config": { | |
| "image_size": 256, | |
| "model_type": "siglip_vision_model", | |
| "projection_dim": 768 | |
| } | |
| } | |