Instructions to use amburger66/robometer-4b-lora-robotsmith-task00 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use amburger66/robometer-4b-lora-robotsmith-task00 with Transformers:
# Load model directly from transformers import AutoProcessor, RBM processor = AutoProcessor.from_pretrained("amburger66/robometer-4b-lora-robotsmith-task00") model = RBM.from_pretrained("amburger66/robometer-4b-lora-robotsmith-task00") - Notebooks
- Google Colab
- Kaggle
| custom_eval: | |
| comparisons_per_task: 5 | |
| confusion_matrix: | |
| - mw | |
| custom_eval_random_seed: 42 | |
| eval_types: | |
| - reward_alignment | |
| - policy_ranking | |
| max_comparisons: null | |
| num_examples_per_quality_pr: 5 | |
| num_partial_successes: 5 | |
| pad_frames: true | |
| policy_ranking: | |
| - amburger66_robotsmith_rbm_task00_robotsmith | |
| policy_ranking_max_tasks: 100 | |
| quality_preference: | |
| - mw | |
| reward_alignment: | |
| - amburger66_robotsmith_rbm_task00_robotsmith | |
| reward_alignment_max_trajectories: 10 | |
| subsample_n_frames: null | |
| use_frame_steps: true | |
| data: | |
| data_source_weights: null | |
| dataloader_num_workers: 8 | |
| dataloader_persistent_workers: true | |
| dataloader_pin_memory: true | |
| dataset_preference_ratio: 0.7 | |
| dataset_success_cutoff_file: robometer/data/dataset_success_cutoff.txt | |
| dataset_type: rbm | |
| eval_datasets: | |
| - amburger66_robotsmith_rbm_task00_robotsmith | |
| eval_subset_size: null | |
| load_embeddings: false | |
| max_frames: 16 | |
| max_frames_after_preprocessing: 64 | |
| max_success: 1.0 | |
| max_trajectories: -1 | |
| min_frames_per_trajectory: 5 | |
| min_success: 0.5 | |
| partial_success_threshold: 0.2 | |
| predict_last_frame_partial_progress: false | |
| preference_strategy_ratio: | |
| - 1.0 | |
| - 1.0 | |
| - 1.0 | |
| - 1.0 | |
| progress_discrete_bins: 10 | |
| progress_loss_type: discrete | |
| progress_pred_type: absolute_first_frame | |
| progress_strategy_ratio: | |
| - 1.0 | |
| - 1.0 | |
| - 1.0 | |
| - 1.0 | |
| resized_height: null | |
| resized_width: null | |
| sample_type_ratio: | |
| - 1.0 | |
| - 0.0 | |
| - 0.0 | |
| seed: 42 | |
| shuffle: true | |
| shuffle_progress_frames: false | |
| train_datasets: | |
| - amburger66_robotsmith_rbm_task00_robotsmith | |
| traj_same_source_prob: 0.5 | |
| use_multi_image: true | |
| use_per_frame_progress_token: true | |
| debug: false | |
| logging: | |
| log_level: INFO | |
| log_to: | |
| - wandb | |
| save_best: | |
| greater_is_better: | |
| - true | |
| - true | |
| hub_private: false | |
| hub_save_every: 1000 | |
| hub_token: null | |
| keep_top_k: 5 | |
| metric_names: | |
| - eval_rew_align/pearson_amburger66_robotsmith_rbm_task00_robotsmith | |
| - eval_p_rank/kendall_last_amburger66_robotsmith_rbm_task00_robotsmith | |
| save_every: 1000 | |
| upload_to_hub: false | |
| save_model: true | |
| save_processor: true | |
| wandb_entity: r-pad | |
| wandb_mode: null | |
| wandb_notes: fine-tuning Robometer on RobotSmith | |
| wandb_project: rbm-finetune-robotsmith | |
| loss: | |
| predict_last_frame_progress: false | |
| progress_discrete_bins: 10 | |
| progress_loss_type: discrete | |
| success_positive_weight: 1.0 | |
| mode: train | |
| model: | |
| average_temporal_patches: true | |
| base_model_id: Qwen/Qwen3-VL-4B-Instruct | |
| frame_pooling: mean | |
| frame_pooling_attn_temperature: 1.0 | |
| model_type: default | |
| peft_vision_encoder: false | |
| progress_discrete_bins: 10 | |
| progress_loss_type: discrete | |
| quantization: false | |
| rewind: null | |
| torch_dtype: bfloat16 | |
| train_language_model: true | |
| train_preference_head: true | |
| train_progress_head: true | |
| train_success_head: true | |
| train_vision_encoder: false | |
| trust_remote_code: true | |
| use_multi_image: true | |
| use_peft: true | |
| use_per_frame_progress_token: true | |
| use_unsloth: true | |
| peft: | |
| bias: none | |
| lora_alpha: 64 | |
| lora_dropout: 0.05 | |
| peft_vision_encoder: false | |
| r: 32 | |
| target_modules: | |
| - q_proj | |
| - k_proj | |
| - v_proj | |
| - o_proj | |
| - gate_proj | |
| - up_proj | |
| - down_proj | |
| trainer_cls: rbm_heads | |
| training: | |
| beta: 0.1 | |
| bf16: true | |
| custom_eval_steps: 50 | |
| dataloader_num_workers: 8 | |
| dataloader_persistent_workers: true | |
| dataloader_pin_memory: true | |
| ddp_bucket_cap_mb: 25 | |
| ddp_find_unused_parameters: false | |
| do_eval: true | |
| eval_steps: 50 | |
| evaluation_strategy: steps | |
| exp_name: lora_task00 | |
| fp16: false | |
| gradient_accumulation_steps: 1 | |
| gradient_checkpointing: true | |
| learning_rate: 2.0e-05 | |
| load_from_checkpoint: robometer/Robometer-4B | |
| logging_steps: 1 | |
| lr_scheduler_type: cosine | |
| max_grad_norm: 10.0 | |
| max_seq_length: 1024 | |
| max_steps: 1000 | |
| num_gpus: 2 | |
| num_train_epochs: -1 | |
| output_dir: /data/robometer/logs/task00 | |
| overwrite_output_dir: true | |
| per_device_eval_batch_size: 16 | |
| per_device_train_batch_size: 8 | |
| predict_pref_progress: true | |
| prediction_loss_only: true | |
| remove_unused_columns: false | |
| resume_from_checkpoint: null | |
| run_default_eval: false | |
| save_steps: 200 | |
| save_strategy: 'no' | |
| vision_encoder_lr: 1.0e-05 | |
| vision_encoder_num_layers: 3 | |
| warmup_ratio: 0.1 | |
| warmup_steps: 0 | |
| weight_decay: 0.01 | |