Instructions to use AravD/Paul_AI-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use AravD/Paul_AI-ft with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("TheBloke/Mistral-7B-Instruct-v0.2-GPTQ") model = PeftModel.from_pretrained(base_model, "AravD/Paul_AI-ft") - Notebooks
- Google Colab
- Kaggle
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Download README.md from AravD/Paul_AI-ft: direct link, hf CLI and curl.
- Browser
- Download file 1.97 kB
-
https://huggingface.co/AravD/Paul_AI-ft/resolve/main/README.md
- Command line
-
hf download hf://AravD/Paul_AI-ft/README.md
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curl -L -o README.md https://huggingface.co/AravD/Paul_AI-ft/resolve/main/README.md
1.97 kB
| base_model: TheBloke/Mistral-7B-Instruct-v0.2-GPTQ | |
| library_name: peft | |
| license: apache-2.0 | |
| tags: | |
| - generated_from_trainer | |
| model-index: | |
| - name: Paul_AI-ft | |
| 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. --> | |
| # Paul_AI-ft | |
| This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.2-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.4433 | |
| ## 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: 4 | |
| - eval_batch_size: 4 | |
| - seed: 42 | |
| - gradient_accumulation_steps: 4 | |
| - total_train_batch_size: 16 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - lr_scheduler_warmup_steps: 2 | |
| - num_epochs: 10 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:------:|:----:|:---------------:| | |
| | 3.4026 | 0.9655 | 7 | 2.6999 | | |
| | 2.2003 | 1.9310 | 14 | 1.6326 | | |
| | 1.2189 | 2.8966 | 21 | 0.8858 | | |
| | 0.6073 | 4.0 | 29 | 0.5774 | | |
| | 0.5123 | 4.9655 | 36 | 0.5030 | | |
| | 0.4497 | 5.9310 | 43 | 0.4705 | | |
| | 0.4181 | 6.8966 | 50 | 0.4567 | | |
| | 0.3456 | 8.0 | 58 | 0.4467 | | |
| | 0.3803 | 8.9655 | 65 | 0.4441 | | |
| | 0.343 | 9.6552 | 70 | 0.4433 | | |
| ### Framework versions | |
| - PEFT 0.13.2 | |
| - Transformers 4.44.2 | |
| - Pytorch 2.4.1+cu121 | |
| - Datasets 3.0.1 | |
| - Tokenizers 0.19.1 |