Instructions to use Crackerjax2/mistral_instruct_generation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Crackerjax2/mistral_instruct_generation with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1") model = PeftModel.from_pretrained(base_model, "Crackerjax2/mistral_instruct_generation") - Notebooks
- Google Colab
- Kaggle
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Download README.md from Crackerjax2/mistral_instruct_generation: direct link, hf CLI and curl.
- Browser
- Download file 1.93 kB
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https://huggingface.co/Crackerjax2/mistral_instruct_generation/resolve/main/README.md
- Command line
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hf download hf://Crackerjax2/mistral_instruct_generation/README.md
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curl -L -o README.md https://huggingface.co/Crackerjax2/mistral_instruct_generation/resolve/main/README.md
1.93 kB
| license: apache-2.0 | |
| library_name: peft | |
| tags: | |
| - trl | |
| - sft | |
| - generated_from_trainer | |
| datasets: | |
| - generator | |
| base_model: mistralai/Mistral-7B-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/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on the generator dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.0225 | |
| ## 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: constant | |
| - lr_scheduler_warmup_steps: 0.03 | |
| - training_steps: 200 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 2.1172 | 1.54 | 20 | 1.9621 | | |
| | 1.7921 | 3.08 | 40 | 1.6326 | | |
| | 1.468 | 4.62 | 60 | 1.3290 | | |
| | 1.2425 | 6.15 | 80 | 1.1762 | | |
| | 1.1558 | 7.69 | 100 | 1.1167 | | |
| | 1.0912 | 9.23 | 120 | 1.0837 | | |
| | 1.0749 | 10.77 | 140 | 1.0632 | | |
| | 1.0534 | 12.31 | 160 | 1.0472 | | |
| | 1.0213 | 13.85 | 180 | 1.0332 | | |
| | 0.9962 | 15.38 | 200 | 1.0225 | | |
| ### Framework versions | |
| - PEFT 0.7.1 | |
| - Transformers 4.37.1 | |
| - Pytorch 2.1.0+cu121 | |
| - Datasets 2.16.1 | |
| - Tokenizers 0.15.1 |