Transformers
Safetensors
PEFT
lora
tinyllama
instruction-tuning
mathematics
machine-learning
education
domain-adaptation
two-stage-training
gsm8k
Instructions to use Nihal108-bi/tinyllama-math-instruct-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nihal108-bi/tinyllama-math-instruct-lora with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Nihal108-bi/tinyllama-math-instruct-lora", device_map="auto") - PEFT
How to use Nihal108-bi/tinyllama-math-instruct-lora with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Nihal108-bi/tinyllama-math-instruct-lora: direct link, hf CLI and curl.
- Browser
- Download file 3.62 MB
-
https://huggingface.co/Nihal108-bi/tinyllama-math-instruct-lora/resolve/main/tokenizer.json
- Command line
-
hf download hf://Nihal108-bi/tinyllama-math-instruct-lora/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Nihal108-bi/tinyllama-math-instruct-lora/resolve/main/tokenizer.json
3.62 MB
File too large to display, you can check the raw version instead.