Instructions to use tekloon/llama-3-8b-customer-support-700-dataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use tekloon/llama-3-8b-customer-support-700-dataset 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, "tekloon/llama-3-8b-customer-support-700-dataset") - Notebooks
- Google Colab
- Kaggle
llama-3-8b-customer-support-700-dataset
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: 0.4177
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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.2254 | 0.2020 | 70 | 0.4380 |
| 0.28 | 0.4040 | 140 | 0.4297 |
| 0.1618 | 0.6061 | 210 | 0.4273 |
| 0.1745 | 0.8081 | 280 | 0.4177 |
Framework versions
- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for tekloon/llama-3-8b-customer-support-700-dataset
Base model
meta-llama/Meta-Llama-3-8B-Instruct