Instructions to use Cem13/3mixi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Cem13/3mixi 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, "Cem13/3mixi") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Cem13/3mixi: direct link, hf CLI and curl.
- Browser
- Download file 1.42 kB
-
https://huggingface.co/Cem13/3mixi/resolve/main/README.md
- Command line
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hf download hf://Cem13/3mixi/README.md
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curl -L -o README.md https://huggingface.co/Cem13/3mixi/resolve/main/README.md
1.42 kB
| license: apache-2.0 | |
| library_name: peft | |
| tags: | |
| - trl | |
| - sft | |
| - generated_from_trainer | |
| base_model: mistralai/Mixtral-8x7B-Instruct-v0.1 | |
| model-index: | |
| - name: mistral_instruct_generation | |
| 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. --> | |
| # mistral_instruct_generation | |
| This model is a fine-tuned version of [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.6649 | |
| ## 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: 2 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: constant | |
| - lr_scheduler_warmup_steps: 0.03 | |
| - num_epochs: 1 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 0.7128 | 1.0 | 1271 | 0.6649 | | |
| ### Framework versions | |
| - PEFT 0.10.0 | |
| - Transformers 4.40.0 | |
| - Pytorch 2.2.1+cu121 | |
| - Tokenizers 0.19.1 |