Robotics
Transformers
Safetensors
English
glm4v
image-text-to-text
computer-vision
spatial-reasoning
vision-language-model
multi-modal
fine-tuned
Instructions to use hany01rye/TIGeR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hany01rye/TIGeR with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("hany01rye/TIGeR") model = AutoModelForMultimodalLM.from_pretrained("hany01rye/TIGeR", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download train_results.json from hany01rye/TIGeR: direct link, hf CLI and curl.
- Browser
- Download file 207 Bytes
-
https://huggingface.co/hany01rye/TIGeR/resolve/main/train_results.json
- Command line
-
hf download hf://hany01rye/TIGeR/train_results.json
-
curl -L -o train_results.json https://huggingface.co/hany01rye/TIGeR/resolve/main/train_results.json
207 Bytes
| { | |
| "epoch": 2.0, | |
| "total_flos": 3771710697373696.0, | |
| "train_loss": 0.06704422599041909, | |
| "train_runtime": 34253.9666, | |
| "train_samples_per_second": 17.026, | |
| "train_steps_per_second": 0.089 | |
| } |