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lilkm/vf_stackblocks_recap_unaligned_frozen_vision_hl_v1

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  • classification unknown
  • files 8
  • author_summary 1 models
  • readme_text full
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Abliteration classifier · v1.0.0
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  • no classification signals present (no abliterated, uncensored, or known producer/method markers)
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Downloads · 30-day
32
↑ 97% in 90 days
Likes
0
Model age
2mo ago
created 2026-07-25
Downloads over time
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Metadata

License
apache-2.0
Tags
lerobot safetensors distributional_value_function robotics reward-model dataset:lilkm/stackblocks_recap_all_for_vf_v3 license:apache-2.0 region:us
Total size
2.10 GB
Files
8
Quantizations
1
Registered
2026-08-22 13:56
Last updated on HF
2026-07-25 16:09

Files by quantization

Auxiliary files 8 files 2.10 GB
model.safetensors 2.10 GB 70a19415 download
policy_preprocessor_step_2_normalizer_processor.safetensors 8.85 KB e4fd77c1 download
train_config.json 5.89 KB e0f84b21 download
policy_preprocessor.json 1.79 KB fc514a6d download
.gitattributes 1.48 KB a6344aac download
config.json 1.47 KB 8e76621f download
README.md 1.39 KB b74481ff download
policy_postprocessor.json 51.0 B 172a8952 download

README current version from Hugging Face


datasets: lilkm/stackblocks_recap_all_for_vf_v3
library_name: lerobot
license: apache-2.0
model_name: distributional_value_function
pipeline_tag: robotics
tags:

  • distributional_value_function
  • robotics
  • lerobot
  • reward-model

Reward Model Card for distributional_value_function

Reward model type not recognized — please update this template.

This reward model has been trained and pushed to the Hub using LeRobot.
See the full documentation at LeRobot Docs.


How to Get Started with the Reward Model

Train from scratch

lerobot-train \
  --dataset.repo_id=${HF_USER}/<dataset> \
  --reward_model.type=distributional_value_function \
  --output_dir=outputs/train/<desired_reward_model_repo_id> \
  --job_name=lerobot_reward_training \
  --reward_model.device=cuda \
  --reward_model.repo_id=${HF_USER}/<desired_reward_model_repo_id> \
  --wandb.enable=true

Writes checkpoints to outputs/train/<desired_reward_model_repo_id>/checkpoints/.

Load the reward model in Python

from lerobot.rewards import make_reward_model

reward_model = make_reward_model(pretrained_path="<hf_user>/<reward_model_repo_id>")
reward = reward_model.compute_reward(batch)

Model Details

  • License: apache-2.0

README history 1 version

The author's README evolved over time. Click a version to see its content at that point.

  1. 2026-07-25Upload reward model weights, train config and readme7fb00c11.4 KB
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