Instructions to use adamtc/v-HSv2q with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use adamtc/v-HSv2q with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="adamtc/v-HSv2q")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("adamtc/v-HSv2q") model = AutoModel.from_pretrained("adamtc/v-HSv2q", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Desktop
Uploaded finetuned model
- Developed by: adamtc
- License: apache-2.0
- Finetuned from model : unsloth/Qwen2.5-VL-3B-Instruct-unsloth-bnb-4bit
This qwen2_5_vl model was trained 2x faster with Unsloth and Huggingface's TRL library.
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Model tree for adamtc/v-HSv2q
Base model
Qwen/Qwen2.5-VL-3B-Instruct