Instructions to use Dexmal/DM05-SO101-Pick-Cube with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Dexmal/DM05-SO101-Pick-Cube with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("./checkpoints/DM05") model = PeftModel.from_pretrained(base_model, "Dexmal/DM05-SO101-Pick-Cube") - Notebooks
- Google Colab
- Kaggle
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| "alpha_pattern": {}, | |
| "arrow_config": null, | |
| "auto_mapping": null, | |
| "base_model_name_or_path": "./checkpoints/DM05", | |
| "bias": "none", | |
| "corda_config": null, | |
| "ensure_weight_tying": false, | |
| "eva_config": null, | |
| "exclude_modules": null, | |
| "fan_in_fan_out": false, | |
| "inference_mode": true, | |
| "init_lora_weights": true, | |
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| "layers_to_transform": null, | |
| "loftq_config": {}, | |
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| "lora_bias": false, | |
| "lora_dropout": 0.0, | |
| "lora_ga_config": null, | |
| "megatron_config": null, | |
| "megatron_core": "megatron.core", | |
| "modules_to_save": [ | |
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| "mlp_time_modulators.8", | |
| "mlp_time_modulators.9", | |
| "mlp_time_modulators.10", | |
| "mlp_time_modulators.11", | |
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| "final_time_modulator" | |
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| "peft_type": "LORA", | |
| "peft_version": "0.19.1", | |
| "qalora_group_size": 16, | |
| "r": 32, | |
| "rank_pattern": {}, | |
| "revision": null, | |
| "target_modules": [ | |
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| "o_proj", | |
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| "down_proj", | |
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| "input_time_modulators.2", | |
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| "target_parameters": null, | |
| "task_type": "CAUSAL_LM", | |
| "trainable_token_indices": null, | |
| "use_bdlora": null, | |
| "use_dora": false, | |
| "use_qalora": false, | |
| "use_rslora": false | |
| } |