TFG
Collection
Datasets and models leveraged and developed during my final degree work (TFG). Info and code can be found at https://github.com/enriquesaou/tfg-lm-qa • 18 items • Updated • 2
phi-3-mrqa
This model is a fine-tuned version of microsoft/Phi-3-mini-4k-instruct on a MRQA sample. It achieves the following results on the evaluation set:
- Loss: 1.7651
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: 3e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 300
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.7895 | 0.0267 | 100 | 1.8179 |
| 1.7531 | 0.0533 | 200 | 1.7730 |
| 1.7567 | 0.08 | 300 | 1.7651 |
Framework versions
- PEFT 0.11.2.dev0
- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
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Model tree for enriquesaou/phi-3-mrqa
Base model
microsoft/Phi-3-mini-4k-instruct
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-4k-instruct") model = PeftModel.from_pretrained(base_model, "enriquesaou/phi-3-mrqa")