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
File size: 1,145 Bytes
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"sigma_env": "sigma.env",
"output_dir": "/workspace/storage/sigma_lora_out",
"base_model_id": "lerobot/pi05_base",
"load_in_4bit": true,
"torch_dtype": "bf16",
"dataset_id": null,
"split": "train",
"data_dir": "/workspace/storage/sigma_pickplace",
"hf_data_repo": "Veltraxor/Sigma",
"hf_data_subdir": "storage/sigma_pickplace",
"prefer_hf_shards": false,
"auto_prune_hf_cache": false,
"hf_cache_keep_latest": 1,
"hf_cache_dir": null,
"num_workers": 4,
"epochs": 3,
"batch_size": 2,
"grad_accum": 8,
"lr": 0.0002,
"warmup_ratio": 0.03,
"weight_decay": 0.0,
"max_steps": -1,
"seed": 42,
"log_every": 10,
"alpha_a": 1.0,
"alpha_b": 1.0,
"alpha_c": 1.0,
"lambda_sem": 1.0,
"lambda_intent": 0.8,
"lambda_tau": 0.03,
"beta_mi": 0.1,
"eta_var": 0.2,
"hard_mining_ratio": 0.3,
"hard_mining_lambda": 1.0,
"loss_warmup_ratio": 0.6,
"lambda_sem_start": 0.1,
"lambda_intent_start": 0.1,
"max_grad_norm": 1.0,
"lora_r": 16,
"lora_alpha": 32,
"lora_dropout": 0.05,
"target_modules": [
"q_proj",
"k_proj",
"v_proj",
"o_proj"
],
"train_base_lora": false
} |