Text Generation
PEFT
Safetensors
medical
healthcare
maternal-health
sexual-health
reproductive-health
multilingual
african-languages
akan
amharic
luganda
swahili
lora
medgemma
conversational
Instructions to use KYAGABA/testmodel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use KYAGABA/testmodel with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/medgemma-27b-text-it") model = PeftModel.from_pretrained(base_model, "KYAGABA/testmodel") - Notebooks
- Google Colab
- Kaggle
| { | |
| "boi_token": "<start_of_image>", | |
| "bos_token": { | |
| "content": "<bos>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "eoi_token": "<end_of_image>", | |
| "eos_token": { | |
| "content": "<eos>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "image_token": "<image_soft_token>", | |
| "pad_token": { | |
| "content": "<pad>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "unk_token": { | |
| "content": "<unk>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| } | |
| } | |