# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Yanis-Gerst/fine_tune", trust_remote_code=True, dtype="auto")Quick Links
fine_tune
This model is a fine-tuned version of microsoft/Phi-4-multimodal-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: 4e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.95) and epsilon=1e-07 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
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Model tree for Yanis-Gerst/fine_tune
Base model
microsoft/Phi-4-multimodal-instruct
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Yanis-Gerst/fine_tune", trust_remote_code=True)