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ankile/openpi-pi05-franka-insert-marker-v2-dagger-r4-ft-delta
openpi-pi05-franka-insert-marker-v2-dagger-r4-ft-delta is a robotics model from ankile. Use it for the robotics task on the model card, and read the license before you ship it in a product. It is set up for openpi. The card lists the license as apache-2.0.
A Pi0.5 model fine-tuned using the OpenPI framework.
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Updated Feb 25, 2026
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From the Hugging Face model README
A Pi0.5 model fine-tuned using the OpenPI framework.
| Property | Value |
|---|---|
| OpenPI config | pi05_sir_droid_finetune |
| Checkpoint step | 1999 |
| Training data | N/A |
| Precision | bfloat16 |
| Parameter size | ~6.7 GB |
| Source checkpoint | /work/hdd/bgdg/lankile/sir-workspace/real-world-worktree/deps/openpi/checkpoints/pi05_sir_droid_finetune/sir_r4_full_2k_bs128_delta/1999 |
| Hugging Face repo | ankile/openpi-pi05-franka-insert-marker-v2-dagger-r4-ft-delta |
| W&B run | link |
| SLURM job ID | 16208908 |
# Download checkpoint from HF Hub
huggingface-cli download ankile/openpi-pi05-franka-insert-marker-v2-dagger-r4-ft-delta --local-dir <local_path>
# Run inference server
cd deps/openpi
uv run python scripts/serve_policy.py pi05_sir_droid_finetune \
--checkpoint-dir <local_path>
from openpi.training import config as openpi_config
from openpi.policies import policy_config as openpi_policy_config
train_config = openpi_config.get_config("pi05_sir_droid_finetune")
policy = openpi_policy_config.create_trained_policy(
train_config, "<local_path>"
)
result = policy.infer(obs_dict)
actions = result["actions"]
Orbax format, all parameters in bfloat16.
├── _CHECKPOINT_METADATA
├── checkpoint_provenance.json
├── openpi_config.json
├── assets/
│ └── (normalization stats)
└── params/
└── (orbax checkpoint files)
Apache 2.0