Instructions to use Nac31/runpod-test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Nac31/runpod-test 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, "Nac31/runpod-test") - Notebooks
- Google Colab
- Kaggle
|
Download README.md from Nac31/runpod-test: direct link, hf CLI and curl.
- Browser
- Download file 1.59 kB
-
https://huggingface.co/Nac31/runpod-test/resolve/main/README.md
- Command line
-
hf download hf://Nac31/runpod-test/README.md
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curl -L -o README.md https://huggingface.co/Nac31/runpod-test/resolve/main/README.md
1.59 kB
metadata
library_name: peft
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- generated_from_trainer
model-index:
- name: runpod-test
results: []
runpod-test
This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.1695
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: 5e-06
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.2093 | 1.0 | 10 | 1.2006 |
| 1.1937 | 2.0 | 20 | 1.1875 |
| 1.182 | 3.0 | 30 | 1.1778 |
| 1.1739 | 4.0 | 40 | 1.1716 |
| 1.1691 | 5.0 | 50 | 1.1695 |
Framework versions
- PEFT 0.14.0
- Transformers 4.49.0
- Pytorch 2.1.0+cu118
- Datasets 3.3.2
- Tokenizers 0.21.0