Instructions to use ExponentialJet/hf_act_pick_red_v2_4_100_100 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LeRobot
How to use ExponentialJet/hf_act_pick_red_v2_4_100_100 with LeRobot:
- Notebooks
- Google Colab
- Kaggle
Upload policy weights, train config and readme
Browse files- README.md +2 -2
- model.safetensors +1 -1
- train_config.json +2 -2
README.md
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@@ -5,8 +5,8 @@ license: apache-2.0
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model_name: act
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pipeline_tag: robotics
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tags:
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- robotics
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- act
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- lerobot
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---
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| Setting | Value |
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| --- | --- |
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| Training steps |
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| Batch size | 16 |
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| Optimizer | adamw |
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| Learning rate | 1e-05 |
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model_name: act
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pipeline_tag: robotics
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tags:
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- act
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- robotics
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- lerobot
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---
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| Setting | Value |
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| --- | --- |
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| Training steps | 80000 |
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| Batch size | 16 |
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| Optimizer | adamw |
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| Learning rate | 1e-05 |
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size 206699736
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version https://git-lfs.github.com/spec/v1
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oid sha256:f3f7380083eec3afb19815ab2ccc9ad54106d825708b14814fa5e0dc43ebed21
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size 206699736
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train_config.json
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"batch_size": 16,
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"prefetch_factor": 4,
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"persistent_workers": true,
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"steps":
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"env_eval_freq": 20000,
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"log_freq": 100,
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"eval_steps": 0,
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"max_eval_samples": 0,
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"tolerance_s": 0.0001,
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"save_checkpoint": true,
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"save_freq":
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"use_policy_training_preset": true,
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"optimizer": {
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"type": "adamw",
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"batch_size": 16,
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"prefetch_factor": 4,
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"persistent_workers": true,
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"steps": 80000,
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"env_eval_freq": 20000,
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"log_freq": 100,
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"eval_steps": 0,
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"max_eval_samples": 0,
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"tolerance_s": 0.0001,
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"save_checkpoint": true,
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"save_freq": 10000,
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"use_policy_training_preset": true,
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"optimizer": {
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"type": "adamw",
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