Instructions to use MHGanainy/gpt2-xl-lora-ecthr-random-imbalanced-uniform-cluster-8-id-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use MHGanainy/gpt2-xl-lora-ecthr-random-imbalanced-uniform-cluster-8-id-3 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2-xl") model = PeftModel.from_pretrained(base_model, "MHGanainy/gpt2-xl-lora-ecthr-random-imbalanced-uniform-cluster-8-id-3") - Notebooks
- Google Colab
- Kaggle
Download train_results.json from MHGanainy/gpt2-xl-lora-ecthr-random-imbalanced-uniform-cluster-8-id-3: direct link, hf CLI and curl.
- Browser
- Download file 204 Bytes
-
https://huggingface.co/MHGanainy/gpt2-xl-lora-ecthr-random-imbalanced-uniform-cluster-8-id-3/resolve/main/train_results.json
- Command line
-
hf download hf://MHGanainy/gpt2-xl-lora-ecthr-random-imbalanced-uniform-cluster-8-id-3/train_results.json
-
curl -L -o train_results.json https://huggingface.co/MHGanainy/gpt2-xl-lora-ecthr-random-imbalanced-uniform-cluster-8-id-3/resolve/main/train_results.json
204 Bytes
| { | |
| "epoch": 1.0, | |
| "total_flos": 7.557299011584e+16, | |
| "train_loss": 2.1402389717607737, | |
| "train_runtime": 1100.7663, | |
| "train_samples_per_second": 7.538, | |
| "train_steps_per_second": 3.769 | |
| } |