Instructions to use kexinz/mistral-7b_test3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use kexinz/mistral-7b_test3 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1") model = PeftModel.from_pretrained(base_model, "kexinz/mistral-7b_test3") - Notebooks
- Google Colab
- Kaggle
mistral-7b_test3
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on an unknown dataset.
Model description
mistral-7b quantized with QLoRA and fine-tuned with 12000 randomly selected samples from FLAN V2 (trained with target only)
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2.5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 3000
MMLU accuracy
Overall: 59.30%
STEM: 49.15%
Humanities: 65.28%
Social Sciences: 70.44%
Other (business, health, misc.): 64.12%
Framework versions
- PEFT 0.9.1.dev0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- Downloads last month
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Base model
mistralai/Mistral-7B-v0.1