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
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| WORLD MODEL EVALUATION STATISTICS | |
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| Model: xlangai/OpenCUA-7B + ageppert/world-model-7b-lora | |
| Validation examples: 4210 | |
| Total inference time: 28496.8s (474.9 min) | |
| Average time per example: 6.77s | |
| Prediction lengths (characters): | |
| min: 447 | |
| median: 795 | |
| mean: 878 | |
| max: 2964 | |
| Ground truth lengths (characters): | |
| min: 340 | |
| median: 796 | |
| mean: 802 | |
| max: 1367 | |
| Empty predictions: 0 (0.0%) | |
| Generation config: | |
| max_new_tokens: 512 | |
| temperature: 0.1 | |
| top_p: 0.9 | |