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
| { | |
| "_name_or_path": "checkpoints/jat_small_v100/checkpoint-250000", | |
| "action_loss_coef": 0.995, | |
| "activation_function": "gelu_new", | |
| "architectures": [ | |
| "JatModel" | |
| ], | |
| "attention_dropout": 0.0, | |
| "attention_layers": [ | |
| "global", | |
| "local", | |
| "global", | |
| "local", | |
| "global", | |
| "local", | |
| "global", | |
| "local", | |
| "global", | |
| "local", | |
| "global", | |
| "local" | |
| ], | |
| "attention_types": [ | |
| [ | |
| [ | |
| "global", | |
| "local" | |
| ], | |
| 6 | |
| ] | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_jat.JatConfig", | |
| "AutoModelForCausalLM": "modeling_jat.JatModel" | |
| }, | |
| "bos_token_id": 50256, | |
| "classifier_dropout": 0.1, | |
| "embed_dropout": 0.0, | |
| "eos_token_id": 50256, | |
| "hidden_size": 768, | |
| "image_size": 224, | |
| "initializer_range": 0.02, | |
| "intermediate_size": null, | |
| "layer_norm_epsilon": 1e-05, | |
| "max_continuous_size": 377, | |
| "max_discrete_value": 212, | |
| "max_position_embeddings": 512, | |
| "model_type": "jat", | |
| "num_channels": 3, | |
| "num_heads": 12, | |
| "num_layers": 12, | |
| "observation_loss_coef": 0.005, | |
| "patch_size": 16, | |
| "resid_dropout": 0.0, | |
| "tokenizer_class": "GPT2TokenizerFast", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.36.1", | |
| "use_cache": true, | |
| "vocab_size": 50257, | |
| "window_size": 256 | |
| } | |