Instructions to use Crackerjax2/Mixtral_Alpace_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Crackerjax2/Mixtral_Alpace_v2 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-Instruct-v0.1") model = PeftModel.from_pretrained(base_model, "Crackerjax2/Mixtral_Alpace_v2") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Crackerjax2/Mixtral_Alpace_v2: direct link, hf CLI and curl.
- Browser
- Download file 2.42 kB
-
https://huggingface.co/Crackerjax2/Mixtral_Alpace_v2/resolve/main/README.md
- Command line
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hf download hf://Crackerjax2/Mixtral_Alpace_v2/README.md
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curl -L -H "Authorization: Bearer $HF_TOKEN" -o README.md https://huggingface.co/Crackerjax2/Mixtral_Alpace_v2/resolve/main/README.md
2.42 kB
| license: apache-2.0 | |
| library_name: peft | |
| tags: | |
| - trl | |
| - sft | |
| - generated_from_trainer | |
| datasets: | |
| - generator | |
| base_model: mistralai/Mixtral-8x7B-Instruct-v0.1 | |
| model-index: | |
| - name: Mixtral_Alpace_v2 | |
| results: [] | |
| <!-- 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. --> | |
| # Mixtral_Alpace_v2 | |
| This model is a fine-tuned version of [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) on the generator dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.4981 | |
| ## 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: 200 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 3.9673 | 0.83 | 10 | 3.6269 | | |
| | 3.4669 | 1.67 | 20 | 3.0746 | | |
| | 2.9203 | 2.5 | 30 | 2.5429 | | |
| | 2.4531 | 3.33 | 40 | 2.2297 | | |
| | 2.2242 | 4.17 | 50 | 2.1009 | | |
| | 2.1178 | 5.0 | 60 | 2.0032 | | |
| | 2.0242 | 5.83 | 70 | 1.9231 | | |
| | 1.9462 | 6.67 | 80 | 1.8525 | | |
| | 1.8682 | 7.5 | 90 | 1.7921 | | |
| | 1.8218 | 8.33 | 100 | 1.7383 | | |
| | 1.754 | 9.17 | 110 | 1.6900 | | |
| | 1.7197 | 10.0 | 120 | 1.6468 | | |
| | 1.672 | 10.83 | 130 | 1.6101 | | |
| | 1.6248 | 11.67 | 140 | 1.5791 | | |
| | 1.5936 | 12.5 | 150 | 1.5526 | | |
| | 1.581 | 13.33 | 160 | 1.5324 | | |
| | 1.5688 | 14.17 | 170 | 1.5168 | | |
| | 1.5307 | 15.0 | 180 | 1.5066 | | |
| | 1.532 | 15.83 | 190 | 1.5001 | | |
| | 1.5254 | 16.67 | 200 | 1.4981 | | |
| ### Framework versions | |
| - PEFT 0.7.1 | |
| - Transformers 4.36.2 | |
| - Pytorch 2.1.2+cu121 | |
| - Datasets 2.16.1 | |
| - Tokenizers 0.15.0 |