Visual Question Answering
Transformers
Safetensors
English
llava_llama
text-generation
mol
multimodal
custom-code
Instructions to use wjdghks950/vicuna_clip_patch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wjdghks950/vicuna_clip_patch with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "visual-question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("visual-question-answering", model="wjdghks950/vicuna_clip_patch")# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("wjdghks950/vicuna_clip_patch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download generation_config.json from wjdghks950/vicuna_clip_patch: direct link, hf CLI and curl.
- Browser
- Download file 154 Bytes
-
https://huggingface.co/wjdghks950/vicuna_clip_patch/resolve/main/generation_config.json
- Command line
-
hf download hf://wjdghks950/vicuna_clip_patch/generation_config.json
-
curl -L -o generation_config.json https://huggingface.co/wjdghks950/vicuna_clip_patch/resolve/main/generation_config.json
154 Bytes
| { | |
| "_from_model_config": true, | |
| "bos_token_id": 1, | |
| "eos_token_id": 2, | |
| "max_length": 4096, | |
| "pad_token_id": 0, | |
| "transformers_version": "4.37.2" | |
| } | |