Instructions to use rdoshi21/hw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use rdoshi21/hw with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B-Instruct") model = PeftModel.from_pretrained(base_model, "rdoshi21/hw") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from rdoshi21/hw: direct link, hf CLI and curl.
- Browser
- Download file 1.3 kB
-
https://huggingface.co/rdoshi21/hw/resolve/main/README.md
- Command line
-
hf download hf://rdoshi21/hw/README.md
-
curl -L -o README.md https://huggingface.co/rdoshi21/hw/resolve/main/README.md
1.3 kB
| library_name: peft | |
| license: llama3.1 | |
| base_model: meta-llama/Llama-3.1-8B-Instruct | |
| tags: | |
| - trl | |
| - dpo | |
| - generated_from_trainer | |
| model-index: | |
| - name: dpo-hw0 | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # dpo-hw0 | |
| This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on the None 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: 0.0002 | |
| - train_batch_size: 1 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 8 | |
| - total_train_batch_size: 8 | |
| - optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_steps: 0.1 | |
| - num_epochs: 4 | |
| ### Training results | |
| ### Framework versions | |
| - PEFT 0.10.0 | |
| - Transformers 5.19.0 | |
| - Pytorch 2.14.1+cu130 | |
| - Datasets 2.21.0 | |
| - Tokenizers 0.23.3 |