Instructions to use Prasanna16/FineTunedLlamaWithPython with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Prasanna16/FineTunedLlamaWithPython with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf") model = PeftModel.from_pretrained(base_model, "Prasanna16/FineTunedLlamaWithPython") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from Prasanna16/FineTunedLlamaWithPython: direct link, hf CLI and curl.
- Browser
- Download file 1.3 kB
-
https://huggingface.co/Prasanna16/FineTunedLlamaWithPython/resolve/main/README.md
- Command line
-
hf download hf://Prasanna16/FineTunedLlamaWithPython/README.md
-
curl -L -H "Authorization: Bearer $HF_TOKEN" -o README.md https://huggingface.co/Prasanna16/FineTunedLlamaWithPython/resolve/main/README.md
1.3 kB
| library_name: peft | |
| tags: | |
| - trl | |
| - sft | |
| - generated_from_trainer | |
| base_model: meta-llama/Llama-2-7b-hf | |
| model-index: | |
| - name: results | |
| 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. --> | |
| # results | |
| This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an [python](https://huggingface.co/iamtarun/python_code_instructions_18k_alpaca) 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: 2 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 2 | |
| - total_train_batch_size: 4 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: constant | |
| - lr_scheduler_warmup_ratio: 0.03 | |
| - training_steps: 500 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| ### Framework versions | |
| - PEFT 0.7.1 | |
| - Transformers 4.36.2 | |
| - Pytorch 2.1.0+cu121 | |
| - Datasets 2.16.1 | |
| - Tokenizers 0.15.0 |