Other
Transformers
Safetensors
PyTorch
English
vision-language-action
humanoid-robotics
telepathy
multimodal
robotics-control
lora
Instructions to use Veltraxor/Sigma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Veltraxor/Sigma with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Veltraxor/Sigma", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload train_args.json
Browse files
storage/sigma_lora_out/train_args.json
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{
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"sigma_env": "sigma.env",
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"output_dir": "/workspace/storage/sigma_lora_out_v2",
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"base_model_id": "lerobot/pi05_base",
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"load_in_4bit": false,
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"torch_dtype": "bf16",
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"dataset_id": null,
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"split": "train",
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"data_dir": "/workspace/storage/sigma_pickplace",
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"hf_data_repo": "Veltraxor/Sigma",
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"hf_data_subdir": "storage/sigma_pickplace",
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"prefer_hf_shards": false,
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"auto_prune_hf_cache": false,
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"hf_cache_keep_latest": 1,
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"hf_cache_dir": null,
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"num_workers": 4,
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"epochs": 3,
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"batch_size": 2,
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"grad_accum": 8,
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"lr": 0.0002,
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"warmup_ratio": 0.03,
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"weight_decay": 0.0,
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"max_steps": -1,
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"seed": 42,
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"log_every": 10,
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"alpha_a": 1.0,
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"alpha_b": 1.0,
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"alpha_c": 1.0,
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"lambda_sem": 1.2,
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"lambda_intent": 0.8,
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"lambda_tau": 0.03,
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"beta_mi": 0.1,
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"eta_var": 0.1,
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"loss_warmup_ratio": 0.4,
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"lambda_sem_start": 0.2,
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"lambda_intent_start": 0.2,
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"max_grad_norm": 1.0,
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"lora_r": 16,
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"lora_alpha": 32,
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"lora_dropout": 0.05,
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"target_modules": [
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"q_proj",
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"k_proj",
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"v_proj",
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"o_proj"
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],
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"train_base_lora": false
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}
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