Instructions to use ram-lexsi/aligntune-testrun-Text2LoRA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ram-lexsi/aligntune-testrun-Text2LoRA with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ram-lexsi/aligntune-testrun-Text2LoRA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
aligntune-testrun-Text2LoRA
Built using AlignTune โ supports any open-source model, any algorithm, any backend (TRL / Unsloth / ES / etc).
| Finetuned from | โ |
| Algorithm | โ |
| Backend | โ |
| Artifact | adapter |
| Published | 2026-08-26 13:00 UTC |
Usage
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained("ram-lexsi/aligntune-testrun-Text2LoRA")
tokenizer = AutoTokenizer.from_pretrained("ram-lexsi/aligntune-testrun-Text2LoRA")
This repo is a LoRA adapter. Load it on top of `` (PEFT does that from adapter_config.json).
Inference Providers NEW
This model isn't deployed by any Inference Provider. ๐ Ask for provider support

