Text Generation
PEFT
Safetensors
English
medical
clinical-pharmacology
pharmacology
drug-interactions
lab-reports
samd
gemma
lora
trl
conversational
Instructions to use sandeshv12/gemma4pharma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use sandeshv12/gemma4pharma with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-4-E2B-it") model = PeftModel.from_pretrained(base_model, "sandeshv12/gemma4pharma") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from sandeshv12/gemma4pharma: direct link, hf CLI and curl.
- Browser
- Download file 32.2 MB
-
https://huggingface.co/sandeshv12/gemma4pharma/resolve/main/tokenizer.json
- Command line
-
hf download hf://sandeshv12/gemma4pharma/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/sandeshv12/gemma4pharma/resolve/main/tokenizer.json
32.2 MB
- Xet hash:
- c62336ad134cad6f154d84eb0e5a5fa9ca17cd665ef3ba5ac4fd02b1486760b4
- Size of remote file:
- 32.2 MB
- SHA256:
- cc8d3a0ce36466ccc1278bf987df5f71db1719b9ca6b4118264f45cb627bfe0f
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