Instructions to use NanQiangHF/Meta-Llama-3-8B-Instruct-Generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use NanQiangHF/Meta-Llama-3-8B-Instruct-Generator with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct") model = PeftModel.from_pretrained(base_model, "NanQiangHF/Meta-Llama-3-8B-Instruct-Generator") - Notebooks
- Google Colab
- Kaggle
Meta-Llama-3-8B-Instruct-Generator
This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1433
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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.426 | 0.3287 | 20 | 0.4661 |
| 0.3689 | 0.6574 | 40 | 0.3151 |
| 0.2887 | 0.9861 | 60 | 0.2654 |
| 0.2441 | 1.3148 | 80 | 0.2161 |
| 0.1863 | 1.6436 | 100 | 0.1709 |
| 0.1656 | 1.9723 | 120 | 0.1576 |
| 0.1538 | 2.3010 | 140 | 0.1491 |
| 0.1475 | 2.6297 | 160 | 0.1444 |
| 0.1449 | 2.9584 | 180 | 0.1433 |
Framework versions
- PEFT 0.10.0
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.14.7
- Tokenizers 0.19.1
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Base model
meta-llama/Meta-Llama-3-8B-Instruct