Instructions to use gmedrano/llama381binstruct_summarize_short with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use gmedrano/llama381binstruct_summarize_short with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("NousResearch/Meta-Llama-3.1-8B-Instruct") model = PeftModel.from_pretrained(base_model, "gmedrano/llama381binstruct_summarize_short") - Notebooks
- Google Colab
- Kaggle
llama381binstruct_summarize_short
This model is a fine-tuned version of NousResearch/Meta-Llama-3.1-8B-Instruct on the generator dataset. It achieves the following results on the evaluation set:
- Loss: 1.3245
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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 30
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.7511 | 1.0 | 20 | 1.2202 |
| 1.2643 | 2.0 | 40 | 1.0353 |
| 0.8173 | 3.0 | 60 | 1.0906 |
| 0.4104 | 4.0 | 80 | 1.2269 |
| 0.2604 | 5.0 | 100 | 1.3245 |
Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
- Downloads last month
- 1
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support
Model tree for gmedrano/llama381binstruct_summarize_short
Base model
NousResearch/Meta-Llama-3.1-8B-Instruct