Instructions to use TuringGame/Llama-3.2-1B-Instruct-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TuringGame/Llama-3.2-1B-Instruct-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="TuringGame/Llama-3.2-1B-Instruct-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("TuringGame/Llama-3.2-1B-Instruct-classifier") model = AutoModelForSequenceClassification.from_pretrained("TuringGame/Llama-3.2-1B-Instruct-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from TuringGame/Llama-3.2-1B-Instruct-classifier: direct link, hf CLI and curl.
- Browser
- Download file 2.23 kB
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https://huggingface.co/TuringGame/Llama-3.2-1B-Instruct-classifier/resolve/main/README.md
- Command line
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hf download hf://TuringGame/Llama-3.2-1B-Instruct-classifier/README.md
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curl -L -o README.md https://huggingface.co/TuringGame/Llama-3.2-1B-Instruct-classifier/resolve/main/README.md
2.23 kB
| library_name: transformers | |
| license: llama3.2 | |
| base_model: meta-llama/Llama-3.2-1B-Instruct | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| - f1 | |
| model-index: | |
| - name: Llama-3.2-1B-Instruct-classifier | |
| 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. --> | |
| # Llama-3.2-1B-Instruct-classifier | |
| This model is a fine-tuned version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.2160 | |
| - Accuracy: 0.9308 | |
| - F1: 0.8621 | |
| ## 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: 2e-05 | |
| - train_batch_size: 32 | |
| - eval_batch_size: 32 | |
| - seed: 42 | |
| - optimizer: Use OptimizerNames.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: 2 | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | | |
| |:-------------:|:------:|:----:|:---------------:|:--------:|:------:| | |
| | No log | 0 | 0 | 1.0194 | 0.4524 | 0.3310 | | |
| | No log | 0.2020 | 79 | 0.3185 | 0.8804 | 0.7508 | | |
| | No log | 0.4041 | 158 | 0.2728 | 0.8948 | 0.7989 | | |
| | No log | 0.6061 | 237 | 0.2436 | 0.9006 | 0.8110 | | |
| | No log | 0.8082 | 316 | 0.2251 | 0.8963 | 0.7895 | | |
| | No log | 1.0102 | 395 | 0.2713 | 0.8905 | 0.81 | | |
| | No log | 1.2123 | 474 | 0.3203 | 0.9020 | 0.8162 | | |
| | 0.3016 | 1.4143 | 553 | 0.2289 | 0.9164 | 0.8324 | | |
| | 0.3016 | 1.6164 | 632 | 0.2306 | 0.9280 | 0.8641 | | |
| | 0.3016 | 1.8184 | 711 | 0.2160 | 0.9308 | 0.8621 | | |
| ### Framework versions | |
| - Transformers 4.53.3 | |
| - Pytorch 2.6.0+cu124 | |
| - Datasets 4.0.0 | |
| - Tokenizers 0.21.2 | |