Instructions to use rawsh/mirrorgemma-2-2b-prm-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rawsh/mirrorgemma-2-2b-prm-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="rawsh/mirrorgemma-2-2b-prm-base")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("rawsh/mirrorgemma-2-2b-prm-base") model = AutoModelForSequenceClassification.from_pretrained("rawsh/mirrorgemma-2-2b-prm-base") - Notebooks
- Google Colab
- Kaggle
mirrorgemma-2-2b-prm-base
This model is a fine-tuned version of google/gemma-2-2b 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: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1
Training results
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
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Model tree for rawsh/mirrorgemma-2-2b-prm-base
Base model
google/gemma-2-2b