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mistral-journal-finetune
dendimaki/mistral-journal-finetune
mistral-journal-finetune is a machine learning model from dendimaki. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as apache-2.0.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- peft
- License
- apache-2.0
- Access
- Public
- Created
- Apr 22, 2024
- Updated
- Apr 22, 2024
- SHA
- 3886ce1f
Base models
Model card
From the Hugging Face model README
<!-- 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-journal-finetune
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on an unknown dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.1399
- eval_runtime: 1839.75
- eval_samples_per_second: 0.231
- eval_steps_per_second: 0.029
- epoch: 0.0294
- step: 50
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: 2.5e-05
- train_batch_size: 2
- 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_steps: 1
- training_steps: 100
Framework versions
- PEFT 0.10.1.dev0
- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
- Library
- peft
- License
- apache-2.0
- Created
- Apr 22, 2024
- Updated
- Apr 22, 2024