# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("mtzig/lltransformer-fwe-test2", dtype="auto")Quick Links
lltransformer-fwe-test2
This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:
- Loss: 10.4539
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: 4
- eval_batch_size: 4
- seed: 1234
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 93.3809 | 0.1860 | 3 | 11.5802 |
| 88.4358 | 0.3721 | 6 | 11.0265 |
| 84.6683 | 0.5581 | 9 | 10.6591 |
| 83.1422 | 0.7442 | 12 | 10.5015 |
| 82.4536 | 0.9302 | 15 | 10.4557 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu128
- Datasets 3.5.0
- Tokenizers 0.21.1
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mtzig/lltransformer-fwe-test2")