Instructions to use dgutierrez/llama381binstruct_summarize_short_new with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use dgutierrez/llama381binstruct_summarize_short_new 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, "dgutierrez/llama381binstruct_summarize_short_new") - Notebooks
- Google Colab
- Kaggle
llama381binstruct_summarize_short_new
This model is a fine-tuned version of NousResearch/Meta-Llama-3.1-8B-Instruct on the generator dataset.
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
- training_steps: 500
Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.4.0+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
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Model tree for dgutierrez/llama381binstruct_summarize_short_new
Base model
NousResearch/Meta-Llama-3.1-8B-Instruct