Video Classification
Transformers
Safetensors
qwen2_5_vl
image-text-to-text
llama-factory
activity-recognition
human-action-recognition
qwen2.5-vl-3B
vision-language-model
fine-tuned
hmdb51
text-generation-inference
Instructions to use phronetic-ai/owlet-har-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use phronetic-ai/owlet-har-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("video-classification", model="phronetic-ai/owlet-har-1")# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("phronetic-ai/owlet-har-1") model = AutoModelForMultimodalLM.from_pretrained("phronetic-ai/owlet-har-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| # ollama modelfile auto-generated by llamafactory | |
| FROM . | |
| TEMPLATE """{{ if .System }}<|im_start|>system | |
| {{ .System }}<|im_end|> | |
| {{ end }}{{ range .Messages }}{{ if eq .Role "user" }}<|im_start|>user | |
| {{ .Content }}<|im_end|> | |
| <|im_start|>assistant | |
| {{ else if eq .Role "assistant" }}{{ .Content }}<|im_end|> | |
| {{ end }}{{ end }}""" | |
| SYSTEM """You are a helpful assistant.""" | |
| PARAMETER stop "<|im_end|>" | |
| PARAMETER num_ctx 4096 | |