TinyStories: How Small Can Language Models Be and Still Speak Coherent English?
Paper • 2305.07759 • Published • 45
Text Generation
Transformers
Safetensors
English
llama
Generated from Trainer
text-generation-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("igorktech/TinyIG")
model = AutoModelForCausalLM.from_pretrained("igorktech/TinyIG")Quick Links
TinyIG
This model is a pre-trained version of LLama2 on the nampdn-ai/tiny-textbooks dataset.
Inspired by TinyStories.
It achieves the following results on the evaluation set:
- Loss: 3.5677
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: 0.0005
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 3.4647 | 2.02 | 30000 | 3.5677 |
Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="igorktech/TinyIG")