Reinforcement Learning
Transformers
English
robotics
vla
vision-language-action
openvla
omnivla
robot
qwen
dinov2
siglip
Instructions to use theguy21/openvla-micro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use theguy21/openvla-micro with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("theguy21/openvla-micro", dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,206 Bytes
dd9b4af | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 | {
"model_id": "openvla-micro",
"description": "Small-vision VLA trained on LIBERO-90 for CPU robot deployment",
"vision": {
"backbone": "dinosiglip-vit-s-b-224px",
"dino_model": "vit_small_patch14_reg4_dinov2.lvd142m",
"dino_dim": 384,
"dino_patches": 256,
"siglip_model": "vit_base_patch16_siglip_224",
"siglip_dim": 768,
"siglip_patches": 196,
"total_patches": 452,
"total_embed_dim": 1152,
"image_size": 224
},
"projector": {
"shim_hidden": 2048,
"shim_out_dim": 8704,
"proj2_in": 8704,
"proj2_out": 896,
"proj4_in": 896,
"proj4_out": 896,
"trainable_params": 38107938
},
"llm": {
"model_id": "qwen25-0_5b-extra",
"hf_id": "Qwen/Qwen2.5-0.5B",
"vocab_size": 151936,
"hidden_dim": 896,
"extra_tokens": 256
},
"action": {
"dof": 7,
"normalization": "minmax_q99",
"tokenizer_bins": 256,
"dataset": "libero_90"
},
"training": {
"optimizer_steps": 5000,
"effective_batch_size": 64,
"micro_batch": 8,
"gradient_accumulation": 8,
"learning_rate": 0.0002,
"lr_schedule": "200_warmup_cosine_to_1e-5",
"lora_rank": 8,
"checkpoint_source": "MiniVLA"
}
}
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