Instructions to use ageppert/world-model-7b-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ageppert/world-model-7b-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("xlangai/OpenCUA-7B") model = PeftModel.from_pretrained(base_model, "ageppert/world-model-7b-lora") - Notebooks
- Google Colab
- Kaggle
| { | |
| "base_model": "xlangai/OpenCUA-7B", | |
| "trust_remote_code": true, | |
| "dataset_repo": "ageppert/world-model-transitions", | |
| "train_file": "transition_train.jsonl", | |
| "val_file": "transition_val.jsonl", | |
| "lora_rank": 16, | |
| "lora_alpha": 32, | |
| "lora_dropout": 0.05, | |
| "target_modules": [ | |
| "q_proj", | |
| "k_proj", | |
| "v_proj", | |
| "o_proj" | |
| ], | |
| "epochs": 3, | |
| "lr": 0.0002, | |
| "per_device_batch_size": 2, | |
| "gradient_accumulation_steps": 8, | |
| "max_seq_length": 2048, | |
| "warmup_ratio": 0.05, | |
| "weight_decay": 0.01, | |
| "bf16": true, | |
| "gradient_checkpointing": true, | |
| "save_steps": 500, | |
| "eval_steps": 500, | |
| "logging_steps": 10, | |
| "output_dir": "./world_model_output", | |
| "final_model_dir": "./world_model_final", | |
| "push_to_hub": true, | |
| "hub_model_id": "ageppert/world-model-7b-lora", | |
| "smoke_test": false | |
| } |