Token Classification
Transformers
Safetensors
llama
Generated from Trainer
text-generation-inference
# Load model directly
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("mtzig/tinyllama_run")
model = AutoModelForTokenClassification.from_pretrained("mtzig/tinyllama_run")Quick Links
tinyllama_run
This model is a fine-tuned version of TinyPixel/small-llama2 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: 2e-05
- train_batch_size: 5
- eval_batch_size: 5
- seed: 42
- gradient_accumulation_steps: 5
- total_train_batch_size: 25
- 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: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
Training results
Framework versions
- Transformers 4.46.0
- Pytorch 2.5.1
- Datasets 3.1.0
- Tokenizers 0.20.1
- Downloads last month
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Model tree for mtzig/tinyllama_run
Base model
TinyPixel/small-llama2
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="mtzig/tinyllama_run")