Instructions to use acbueff/gpt-sw3-356m-is-saga-kl-sft-sdpo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use acbueff/gpt-sw3-356m-is-saga-kl-sft-sdpo with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Hodfa71/saga-is-356m-kl-sft") model = PeftModel.from_pretrained(base_model, "acbueff/gpt-sw3-356m-is-saga-kl-sft-sdpo") - Notebooks
- Google Colab
- Kaggle
GPT-SW3-356M — Icelandic Grammar-Aligned (SAGA SDPO + Antihack)
Fine-tuned with SAGA (Syntax-Aware Grammar Alignment) using Self-Distilled Policy Optimization (SDPO) with anti-hacking measures.
Pipeline: GPT-SW3-356M → KL-SFT → SDPO (this adapter)
| Metric | Base 356M | SDPO KL-SFT Antihack |
|---|---|---|
| Stanza parse success | 0.725 | 0.810 |
| Stanza mean quality | 0.471 | 0.525 |
| Stanza parse score | 0.341 | 0.426 |
| Wiki PPL | 22.85 | 23.81 |
| ScaLA AUROC | 0.680 | 0.684 |
Anti-hacking measures:
- Repetition penalty: 1.3 (generation-side)
- MATTR weight: 0.2 (reward-side lexical diversity penalty)
Training config: 1 epoch, batch 64, 8 generations, lr=1e-5, alpha=0.5, success threshold=0.3.
Cross-lingual transfer (Stanza parse score): IS→DA 0.413, IS→NB 0.409, IS→SV 0.410.
Usage
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base = AutoModelForCausalLM.from_pretrained("Hodfa71/saga-is-356m-kl-sft")
model = PeftModel.from_pretrained(base, "acbueff/gpt-sw3-356m-is-saga-kl-sft-sdpo")
tokenizer = AutoTokenizer.from_pretrained("acbueff/gpt-sw3-356m-is-saga-kl-sft-sdpo")
Oracle: Greynir (Icelandic constituency parser). Held-out eval: Stanza is.
Inherits the base model license (AI Sweden LLM License).
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Model tree for acbueff/gpt-sw3-356m-is-saga-kl-sft-sdpo
Base model
Hodfa71/saga-is-356m-kl-sft