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---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: TheBloke/Mistral-7B-Instruct-v0.2-GPTQ
model-index:
- name: mistral-ft
results: []
pipeline_tag: text2text-generation
widget:
- text: >-
Résultats :• Absence d’anomalie de densité parenchymateuse cérébrale,
cérébelleuse ou du tronc cérébral• Absence de dilatation du système
ventriculaire.• Structures médianes en place.• Absence de collection péri
cérébrale.• Absence de lésion osseuse.• Bonne pneumatisation des sinus.
example_title: Observation
---
<!-- 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-ft
This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.2-GPTQ](https://huggingface.co./TheBloke/Mistral-7B-Instruct-v0.2-GPTQ) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2527
## Model description
This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.2-GPTQ](https://huggingface.co./TheBloke/Mistral-7B-Instruct-v0.2-GPTQ) for radiology reports conclusions generation.
## 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: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.3229 | 0.97 | 27 | 1.8742 |
| 1.7299 | 1.98 | 55 | 1.6318 |
| 1.5704 | 2.99 | 83 | 1.4831 |
| 1.4553 | 4.0 | 111 | 1.4052 |
| 1.4421 | 4.97 | 138 | 1.3805 |
| 1.3759 | 5.98 | 166 | 1.3759 |
| 1.3658 | 6.99 | 194 | 1.3355 |
| 1.3271 | 8.0 | 222 | 1.2890 |
| 1.3299 | 8.97 | 249 | 1.2618 |
| 1.2296 | 9.73 | 270 | 1.2527 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.38.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 |