Instructions to use a9sl1/pi05_movetobowl_quantile with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use a9sl1/pi05_movetobowl_quantile with LeRobot:
- Notebooks
- Google Colab
- Kaggle
Upload policy weights, train config and readme
Browse files- README.md +1 -1
- config.json +1 -1
- model.safetensors +1 -1
- train_config.json +5 -5
README.md
CHANGED
|
@@ -5,9 +5,9 @@ license: apache-2.0
|
|
| 5 |
model_name: pi05
|
| 6 |
pipeline_tag: robotics
|
| 7 |
tags:
|
| 8 |
-
- robotics
|
| 9 |
- pi05
|
| 10 |
- lerobot
|
|
|
|
| 11 |
---
|
| 12 |
|
| 13 |
# Model Card for pi05
|
|
|
|
| 5 |
model_name: pi05
|
| 6 |
pipeline_tag: robotics
|
| 7 |
tags:
|
|
|
|
| 8 |
- pi05
|
| 9 |
- lerobot
|
| 10 |
+
- robotics
|
| 11 |
---
|
| 12 |
|
| 13 |
# Model Card for pi05
|
config.json
CHANGED
|
@@ -41,7 +41,7 @@
|
|
| 41 |
"private": null,
|
| 42 |
"tags": null,
|
| 43 |
"license": null,
|
| 44 |
-
"pretrained_path": "
|
| 45 |
"paligemma_variant": "gemma_2b",
|
| 46 |
"action_expert_variant": "gemma_300m",
|
| 47 |
"dtype": "bfloat16",
|
|
|
|
| 41 |
"private": null,
|
| 42 |
"tags": null,
|
| 43 |
"license": null,
|
| 44 |
+
"pretrained_path": "lerobot_output/pi05/pi05_movetobowl_quantile/checkpoints/last/pretrained_model",
|
| 45 |
"paligemma_variant": "gemma_2b",
|
| 46 |
"action_expert_variant": "gemma_300m",
|
| 47 |
"dtype": "bfloat16",
|
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 9354050752
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:278c2f049729f2f2f459cce71af525bc68eaae7479f35a084129011100778f2b
|
| 3 |
size 9354050752
|
train_config.json
CHANGED
|
@@ -123,7 +123,7 @@
|
|
| 123 |
"private": null,
|
| 124 |
"tags": null,
|
| 125 |
"license": null,
|
| 126 |
-
"pretrained_path": "
|
| 127 |
"paligemma_variant": "gemma_2b",
|
| 128 |
"action_expert_variant": "gemma_300m",
|
| 129 |
"dtype": "bfloat16",
|
|
@@ -181,17 +181,17 @@
|
|
| 181 |
},
|
| 182 |
"output_dir": "lerobot_output/pi05/pi05_movetobowl_quantile",
|
| 183 |
"job_name": "pi05_movetobowl_quantile",
|
| 184 |
-
"resume":
|
| 185 |
"seed": 1000,
|
| 186 |
"cudnn_deterministic": false,
|
| 187 |
"num_workers": 4,
|
| 188 |
"batch_size": 32,
|
| 189 |
-
"steps":
|
| 190 |
"eval_freq": 20000,
|
| 191 |
"log_freq": 200,
|
| 192 |
"tolerance_s": 0.0001,
|
| 193 |
"save_checkpoint": true,
|
| 194 |
-
"save_freq":
|
| 195 |
"use_policy_training_preset": true,
|
| 196 |
"optimizer": {
|
| 197 |
"type": "adamw",
|
|
@@ -233,5 +233,5 @@
|
|
| 233 |
"rabc_epsilon": 1e-06,
|
| 234 |
"rabc_head_mode": "sparse",
|
| 235 |
"rename_map": {},
|
| 236 |
-
"checkpoint_path":
|
| 237 |
}
|
|
|
|
| 123 |
"private": null,
|
| 124 |
"tags": null,
|
| 125 |
"license": null,
|
| 126 |
+
"pretrained_path": "lerobot_output/pi05/pi05_movetobowl_quantile/checkpoints/last/pretrained_model",
|
| 127 |
"paligemma_variant": "gemma_2b",
|
| 128 |
"action_expert_variant": "gemma_300m",
|
| 129 |
"dtype": "bfloat16",
|
|
|
|
| 181 |
},
|
| 182 |
"output_dir": "lerobot_output/pi05/pi05_movetobowl_quantile",
|
| 183 |
"job_name": "pi05_movetobowl_quantile",
|
| 184 |
+
"resume": true,
|
| 185 |
"seed": 1000,
|
| 186 |
"cudnn_deterministic": false,
|
| 187 |
"num_workers": 4,
|
| 188 |
"batch_size": 32,
|
| 189 |
+
"steps": 30000,
|
| 190 |
"eval_freq": 20000,
|
| 191 |
"log_freq": 200,
|
| 192 |
"tolerance_s": 0.0001,
|
| 193 |
"save_checkpoint": true,
|
| 194 |
+
"save_freq": 3000,
|
| 195 |
"use_policy_training_preset": true,
|
| 196 |
"optimizer": {
|
| 197 |
"type": "adamw",
|
|
|
|
| 233 |
"rabc_epsilon": 1e-06,
|
| 234 |
"rabc_head_mode": "sparse",
|
| 235 |
"rename_map": {},
|
| 236 |
+
"checkpoint_path": "lerobot_output/pi05/pi05_movetobowl_quantile/checkpoints/last"
|
| 237 |
}
|