Instructions to use frankmorales2020/Mistral-7B-v0.1_AviationQA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use frankmorales2020/Mistral-7B-v0.1_AviationQA with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") model = PeftModel.from_pretrained(base_model, "frankmorales2020/Mistral-7B-v0.1_AviationQA") - Notebooks
- Google Colab
- Kaggle
metadata
library_name: peft
license: apache-2.0
base_model: mistralai/Mistral-7B-Instruct-v0.1
tags:
- generated_from_trainer
metrics:
- bleu
- f1
model-index:
- name: Mistral-7B-v0.1_AviationQA
results: []
Mistral-7B-v0.1_AviationQA
This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.1 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 10.2218
- Bleu: 0.3549
- Rougel: 0.4821
- F1: 0.0005
- Perplexity: 30611.9414
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: 0.0001
- train_batch_size: 2
- eval_batch_size: 3
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Rougel | F1 | Perplexity |
|---|---|---|---|---|---|---|---|
| 10.2565 | 1.0 | 25 | 10.2347 | 0.2507 | 0.3703 | 0.0009 | 29825.1777 |
| 10.1919 | 2.0 | 50 | 10.2232 | 0.3097 | 0.4539 | 0.0008 | 30276.8613 |
| 10.1739 | 3.0 | 75 | 10.2218 | 0.3549 | 0.4821 | 0.0005 | 30611.9414 |
Framework versions
- PEFT 0.14.0
- Transformers 4.49.0
- Pytorch 2.5.1+cu124
- Datasets 3.3.2
- Tokenizers 0.21.0