Video-Text-to-Text
Transformers
Safetensors
English
qwen2
text-generation
Action
Video
MQA
multimodal
VLM
LLaVAction
MLLMs
Eval Results (legacy)
text-generation-inference
Instructions to use MLAdaptiveIntelligence/LLaVAction-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MLAdaptiveIntelligence/LLaVAction-7B with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MLAdaptiveIntelligence/LLaVAction-7B") model = AutoModelForCausalLM.from_pretrained("MLAdaptiveIntelligence/LLaVAction-7B") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 04fe21a71f8d9ce5ffc19fd16629248ee890fa6fecc1d0c64459777fe9d98911
- Size of remote file:
- 7.86 kB
- SHA256:
- 4b2abce1a1da7724b2b79a659c403cad25216a055ad91614bb2325d85d752663
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.