Instructions to use Akayy369/dpo_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Akayy369/dpo_model with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0") model = PeftModel.from_pretrained(base_model, "Akayy369/dpo_model") - Notebooks
- Google Colab
- Kaggle
dpo_model
This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 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: 5e-06
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
Training results
Framework versions
- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.5.1+cu121
- Datasets 4.5.0
- Tokenizers 0.19.1
- Downloads last month
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Model tree for Akayy369/dpo_model
Base model
TinyLlama/TinyLlama-1.1B-Chat-v1.0