Instructions to use sks1998/tinyllama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use sks1998/tinyllama with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("PY007/TinyLlama-1.1B-step-50K-105b") model = PeftModel.from_pretrained(base_model, "sks1998/tinyllama") - Notebooks
- Google Colab
- Kaggle
metadata
base_model: PY007/TinyLlama-1.1B-step-50K-105b
library_name: peft
license: apache-2.0
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: tinyllama
results: []
tinyllama
This model is a fine-tuned version of PY007/TinyLlama-1.1B-step-50K-105b on the None 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: 0.002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 200
Training results
Framework versions
- PEFT 0.12.1.dev0
- Transformers 4.43.3
- Pytorch 2.2.2
- Datasets 2.20.0
- Tokenizers 0.19.1