Instructions to use Aniket9747/tiny_llama_symptom_classifier_head with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Aniket9747/tiny_llama_symptom_classifier_head with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Aniket9747/tiny_llama_symptom_classifier_head")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Aniket9747/tiny_llama_symptom_classifier_head") model = AutoModelForSequenceClassification.from_pretrained("Aniket9747/tiny_llama_symptom_classifier_head", device_map="auto") - Notebooks
- Google Colab
- Kaggle
tiny_llama_symptom_classifier_head
This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.0006
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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 |
|---|---|---|---|
| 0.0122 | 0.8 | 100 | 0.0118 |
| 0.0046 | 1.6 | 200 | 0.0045 |
| 0.0017 | 2.4 | 300 | 0.0018 |
| 0.0008 | 3.2 | 400 | 0.0009 |
| 0.0005 | 4.0 | 500 | 0.0007 |
| 0.0004 | 4.8 | 600 | 0.0006 |
Framework versions
- Transformers 5.1.0
- Pytorch 2.9.0+cu126
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for Aniket9747/tiny_llama_symptom_classifier_head
Base model
TinyLlama/TinyLlama-1.1B-Chat-v1.0