Instructions to use AngoHF/EssayGPT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use AngoHF/EssayGPT with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen1.5-1.8B-Chat") model = PeftModel.from_pretrained(base_model, "AngoHF/EssayGPT") - Notebooks
- Google Colab
- Kaggle
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Download README.md from AngoHF/EssayGPT: direct link, hf CLI and curl.
- Browser
- Download file 1.17 kB
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https://huggingface.co/AngoHF/EssayGPT/resolve/main/README.md
- Command line
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hf download hf://AngoHF/EssayGPT/README.md
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curl -L -o README.md https://huggingface.co/AngoHF/EssayGPT/resolve/main/README.md
1.17 kB
metadata
license: other
library_name: peft
tags:
- lora
- generated_from_trainer
base_model: Qwen/Qwen1.5-1.8B-Chat
model-index:
- name: Qwen/Qwen1.5-1.8B-Chat
results: []
Qwen-1.8B
This model is a fine-tuned version of Qwen1.5-1.8B-Chat on the essay_dataset dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 8.0
Training results
Framework versions
- PEFT 0.7.1
- Transformers 4.37.2
- Pytorch 1.13.1+cu116
- Datasets 2.14.7
- Tokenizers 0.15.1