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---
base_model: mistralai/Mistral-7B-Instruct-v0.3
datasets:
- GaetanMichelet/chat-60_ft_task-1_auto
- GaetanMichelet/chat-120_ft_task-1_auto
- GaetanMichelet/chat-180_ft_task-1_auto
library_name: peft
license: apache-2.0
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
model-index:
- name: Mistral-7B_task-1_180-samples_config-1_full_auto
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_task-1_180-samples_config-1_full_auto
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) on the GaetanMichelet/chat-60_ft_task-1_auto, the GaetanMichelet/chat-120_ft_task-1_auto and the GaetanMichelet/chat-180_ft_task-1_auto datasets.
It achieves the following results on the evaluation set:
- Loss: 0.8139
## 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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.4674 | 1.0 | 17 | 1.4009 |
| 0.9198 | 2.0 | 34 | 0.9025 |
| 0.8 | 3.0 | 51 | 0.8352 |
| 0.6649 | 4.0 | 68 | 0.8139 |
| 0.5015 | 5.0 | 85 | 0.8609 |
| 0.3493 | 6.0 | 102 | 0.9102 |
| 0.2426 | 7.0 | 119 | 1.0199 |
| 0.1984 | 8.0 | 136 | 1.1415 |
| 0.1677 | 9.0 | 153 | 1.0863 |
| 0.1384 | 10.0 | 170 | 1.1544 |
| 0.1134 | 11.0 | 187 | 1.2206 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.44.0
- Pytorch 2.1.2+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1 |