Text Generation
Transformers
Safetensors
llama
smol-course
module_1_python_colab
Generated from Trainer
text-generation-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Digish/smollm-finetuned")
model = AutoModelForCausalLM.from_pretrained("Digish/smollm-finetuned")Quick Links
smollm-finetuned
This model is a fine-tuned version of HuggingFaceTB/SmolLM2-135M on an unknown dataset.
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-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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: 3
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
- Downloads last month
- 4
Model tree for Digish/smollm-finetuned
Base model
HuggingFaceTB/SmolLM2-135M
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Digish/smollm-finetuned")