Instructions to use ciocan/ornith-1.5-9b-spreadsheetbench-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use ciocan/ornith-1.5-9b-spreadsheetbench-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/root/ft9b/models/ornith-1.5-9b") model = PeftModel.from_pretrained(base_model, "ciocan/ornith-1.5-9b-spreadsheetbench-lora") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from ciocan/ornith-1.5-9b-spreadsheetbench-lora: direct link, hf CLI and curl.
- Browser
- Download file 20 MB
-
https://huggingface.co/ciocan/ornith-1.5-9b-spreadsheetbench-lora/resolve/main/tokenizer.json
- Command line
-
hf download hf://ciocan/ornith-1.5-9b-spreadsheetbench-lora/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/ciocan/ornith-1.5-9b-spreadsheetbench-lora/resolve/main/tokenizer.json
20 MB
- Xet hash:
- 777bcaa63794fa47b8f53680be9d6d176f1fcbd7ba03cdc6c3bae2b3d76b323f
- Size of remote file:
- 20 MB
- SHA256:
- 06b9509352d2af50381ab2247e083b80d32d5c0aba91c272ca9ff729b6a0e523
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