Instructions to use Berk/mixtral_train with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Berk/mixtral_train with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-v0.1") model = PeftModel.from_pretrained(base_model, "Berk/mixtral_train") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Berk/mixtral_train: direct link, hf CLI and curl.
- Browser
- Download file 1.36 kB
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https://huggingface.co/Berk/mixtral_train/resolve/fa934615022408c8b26c978b0ca38c7b0b869e35/README.md
- Command line
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hf download hf://Berk/mixtral_train@fa934615022408c8b26c978b0ca38c7b0b869e35/README.md
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curl -L -o README.md https://huggingface.co/Berk/mixtral_train/resolve/fa934615022408c8b26c978b0ca38c7b0b869e35/README.md
1.36 kB
metadata
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: mistralai/Mixtral-8x7B-v0.1
datasets:
- generator
model-index:
- name: mixtral_train
results: []
mixtral_train
This model is a fine-tuned version of mistralai/Mixtral-8x7B-v0.1 on the generator dataset. It achieves the following results on the evaluation set:
- eval_loss: 0.8533
- eval_runtime: 141.9062
- eval_samples_per_second: 3.622
- eval_steps_per_second: 0.458
- epoch: 5.97
- step: 400
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: 32
- 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: 0.03
- training_steps: 1000
Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2