Instructions to use predibase/gsm8k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use predibase/gsm8k with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1") model = PeftModel.from_pretrained(base_model, "predibase/gsm8k") - Notebooks
- Google Colab
- Kaggle
| library_name: peft | |
| base_model: mistralai/Mistral-7B-v0.1 | |
| pipeline_tag: text-generation | |
| Description: Grade school math problems\ | |
| Original dataset: https://huggingface.co/datasets/gsm8k \ | |
| ---\ | |
| Try querying this adapter for free in Lora Land at https://predibase.com/lora-land! \ | |
| The adapter_category is STEM and the name is Grade School Math (gsm8k)\ | |
| ---\ | |
| Sample input: Please answer the following question: James decides to run 3 sprints 3 times a week. He runs 60 meters each sprint. How many total meters does he run a week?\nAnswer:\ | |
| ---\ | |
| Sample output: He runs 3*3=<<3*3=9>>9 sprints a week | |
| He runs 60*9=<<60*9=540>>540 meters a week | |
| #### 540\ | |
| ---\ | |
| Try using this adapter yourself! | |
| ``` | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| model_id = "mistralai/Mistral-7B-v0.1" | |
| peft_model_id = "predibase/gsm8k" | |
| model = AutoModelForCausalLM.from_pretrained(model_id) | |
| model.load_adapter(peft_model_id) | |
| ``` |