Instructions to use a2ran/kor_chatGLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use a2ran/kor_chatGLM with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("THUDM/chatglm-6b") model = PeftModel.from_pretrained(base_model, "a2ran/kor_chatGLM") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from a2ran/kor_chatGLM: direct link, hf CLI and curl.
- Browser
- Download file 419 Bytes
-
https://huggingface.co/a2ran/kor_chatGLM/resolve/main/README.md
- Command line
-
hf download hf://a2ran/kor_chatGLM/README.md
-
curl -L -o README.md https://huggingface.co/a2ran/kor_chatGLM/resolve/main/README.md
419 Bytes
| library_name: peft | |
| - **WIP** | |
| Data used : https://raw.githubusercontent.com/Beomi/KoAlpaca/main/alpaca_data.json | |
| training_args = TrainingArguments( | |
| "output", | |
| fp16 =True, | |
| gradient_accumulation_steps=1, | |
| per_device_train_batch_size = 1, | |
| learning_rate = 1e-4, | |
| max_steps=3000, | |
| logging_steps=100, | |
| remove_unused_columns=False, | |
| seed=0, | |
| data_seed=0, | |
| group_by_length=False, | |
| ) |