Instructions to use raulgdp/Mistral-8B-Instruct-2410-2025-II with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use raulgdp/Mistral-8B-Instruct-2410-2025-II with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Ministral-8B-Instruct-2410") model = PeftModel.from_pretrained(base_model, "raulgdp/Mistral-8B-Instruct-2410-2025-II") - Notebooks
- Google Colab
- Kaggle
End of training
Browse files- README.md +72 -0
- adapter_model.safetensors +1 -1
README.md
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---
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library_name: peft
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license: other
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base_model: mistralai/Ministral-8B-Instruct-2410
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tags:
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- generated_from_trainer
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model-index:
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- name: Mistral-8B-Instruct-2410-2025-II
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# Mistral-8B-Instruct-2410-2025-II
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This model is a fine-tuned version of [mistralai/Ministral-8B-Instruct-2410](https://huggingface.co/mistralai/Ministral-8B-Instruct-2410) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.5701
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 2e-05
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- train_batch_size: 2
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 16
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- optimizer: Use paged_adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: linear
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- num_epochs: 10
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:------:|:----:|:---------------:|
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| 1.3018 | 0.8658 | 100 | 1.3113 |
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| 1.1356 | 1.7273 | 200 | 1.1116 |
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| 0.9729 | 2.5887 | 300 | 0.9593 |
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| 0.7977 | 3.4502 | 400 | 0.8432 |
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| 0.7237 | 4.3117 | 500 | 0.7587 |
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| 0.6048 | 5.1732 | 600 | 0.6926 |
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| 0.6142 | 6.0346 | 700 | 0.6468 |
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| 0.5462 | 6.9004 | 800 | 0.6196 |
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| 0.5386 | 7.7619 | 900 | 0.5937 |
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| 0.5375 | 8.6234 | 1000 | 0.5801 |
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| 0.4623 | 9.4848 | 1100 | 0.5701 |
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### Framework versions
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- PEFT 0.8.2
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- Transformers 4.57.1
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- Pytorch 2.7.1+cu118
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- Datasets 4.2.0
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- Tokenizers 0.22.1
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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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-
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size 30709192
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version https://git-lfs.github.com/spec/v1
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size 30709192
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