Reinforcement Learning
Transformers
Safetensors
jat
text-generation
atari
babyai
metaworld
mujoco-ant
mujoco
custom_code
Eval Results (legacy)
Instructions to use jat-project/jat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jat-project/jat with Transformers:
# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("jat-project/jat", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ee916f091185c483bf12841fa1e56cb08ae4a4e303cb1318e836f826208a19e1
- Size of remote file:
- 109 MB
- SHA256:
- ee5df8a03b32775aa5644de9cd1c35e5ba3b7269229030555162a24c2468566f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.