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Mistral-7B-Instruct-v0.2-mirage-bench-sft
nthakur/Mistral-7B-Instruct-v0.2-mirage-bench-sft
Mistral-7B-Instruct-v0.2-mirage-bench-sft is a machine learning model from nthakur. 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
- Model type
- mistral
- Access
- Public
- Created
- Aug 9, 2024
- Updated
- Mar 31, 2025
- SHA
- 67c9d62c
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. -->
nthakur/Mistral-7B-Instruct-v0.2-mirage-bench-sft
This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2 on the nthakur/mirage-gpt-4o-sft-instruct-mistral dataset. It achieves the following results on the evaluation set:
- Loss: 0.2294
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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 32
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.3189 | 0.2535 | 200 | 0.2828 |
| 0.2906 | 0.5070 | 400 | 0.2465 |
| 0.2525 | 0.7605 | 600 | 0.2326 |
Framework versions
- PEFT 0.7.1
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
- Library
- peft
- Type
- mistral
- License
- apache-2.0
- Created
- Aug 9, 2024
- Updated
- Mar 31, 2025