Instructions to use WangXFng/Instruments2-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use WangXFng/Instruments2-1B with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B-Instruct") model = PeftModel.from_pretrained(base_model, "WangXFng/Instruments2-1B") - Notebooks
- Google Colab
- Kaggle
Instruments2-1B
This model is a fine-tuned version of meta-llama/Llama-3.2-1B-Instruct on an unknown 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
Framework versions
- PEFT 0.13.0
- Transformers 4.45.2
- Pytorch 2.4.0
- Tokenizers 0.20.0
- Downloads last month
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Base model
meta-llama/Llama-3.2-1B-Instruct