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
File size: 1,959 Bytes
2fa8b70 6546558 2fa8b70 6546558 2fa8b70 6546558 45a978d 2fa8b70 48b95f4 2fa8b70 48b95f4 2fa8b70 a5a1d90 a9e4095 6fd8013 2fa8b70 a9e4095 6546558 2fa8b70 45a978d 2fa8b70 45a978d 2fa8b70 6546558 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 | ---
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: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Mistral-7B-v0.1_AviationQA
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/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 |