Instructions to use JasonBounre/sft-chatbot5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use JasonBounre/sft-chatbot5 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B") model = PeftModel.from_pretrained(base_model, "JasonBounre/sft-chatbot5") - Notebooks
- Google Colab
- Kaggle
sft-chatbot5
This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.9160
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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 8
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 0.9412 | 4 | 1.3488 |
| No log | 1.8824 | 8 | 1.1554 |
| No log | 2.8235 | 12 | 1.0278 |
| No log | 4.0 | 17 | 0.9632 |
| No log | 4.9412 | 21 | 0.9355 |
| No log | 5.8824 | 25 | 0.9215 |
| No log | 6.8235 | 29 | 0.9165 |
| No log | 7.5294 | 32 | 0.9160 |
Framework versions
- PEFT 0.13.1
- Transformers 4.43.3
- Pytorch 2.4.0+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
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Base model
meta-llama/Meta-Llama-3-8B