Instructions to use ram-lexsi/aligntune-testrun-MoA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ram-lexsi/aligntune-testrun-MoA with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ram-lexsi/aligntune-testrun-MoA", device_map="auto") - Notebooks
- Google Colab
- Kaggle
aligntune-testrun-MoA
Built using AlignTune — supports any open-source model, any algorithm, any backend (TRL / Unsloth / ES / etc).
| Finetuned from | Qwen/Qwen2.5-0.5B-Instruct |
| Algorithm | finetune |
| Backend | trl |
| Artifact | adapter |
| Published | 2026-08-26 13:08 UTC |
Usage
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
model = AutoPeftModelForCausalLM.from_pretrained("ram-lexsi/aligntune-testrun-MoA")
tokenizer = AutoTokenizer.from_pretrained("ram-lexsi/aligntune-testrun-MoA")
This repo is a LoRA adapter. Load it on top of Qwen/Qwen2.5-0.5B-Instruct (PEFT does that from adapter_config.json).
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