Instructions to use maelghrib/ad-gen-ft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use maelghrib/ad-gen-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, "maelghrib/ad-gen-ft") - Notebooks
- Google Colab
- Kaggle
ad-gen-ft
This model is a fine-tuned version of TheBloke/Mistral-7B-Instruct-v0.2-GPTQ on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0765
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 |
|---|---|---|---|
| 1.5396 | 0.9455 | 13 | 1.0181 |
| 0.7048 | 1.9636 | 27 | 0.3883 |
| 0.3153 | 2.9818 | 41 | 0.1752 |
| 0.1968 | 4.0 | 55 | 0.1220 |
| 0.1637 | 4.9455 | 68 | 0.1000 |
| 0.1271 | 5.9636 | 82 | 0.0871 |
| 0.112 | 6.9818 | 96 | 0.0812 |
| 0.1026 | 8.0 | 110 | 0.0782 |
| 0.1038 | 8.9455 | 123 | 0.0766 |
| 0.0808 | 9.4545 | 130 | 0.0765 |
Framework versions
- PEFT 0.10.0
- Transformers 4.40.2
- Pytorch 2.1.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
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Model tree for maelghrib/ad-gen-ft
Base model
mistralai/Mistral-7B-Instruct-v0.2 Quantized
TheBloke/Mistral-7B-Instruct-v0.2-GPTQ