Instructions to use kacperwikiel/slayer-v48-qwen3.5-27b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use kacperwikiel/slayer-v48-qwen3.5-27b with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-27B") model = PeftModel.from_pretrained(base_model, "kacperwikiel/slayer-v48-qwen3.5-27b") - Notebooks
- Google Colab
- Kaggle
slayer-v48-qwen3.5-27b
LoRA adapter for Qwen/Qwen3.5-27B trained for Polish Open PL style tasks.
Status
This adapter should be treated as a research checkpoint unless the attached evaluation artifacts show it was explicitly promoted.
- Local adapter directory:
/home/ubuntu/runs/qwen35_27b_sft_openpl_style_v48_full_v43_epoch_lr2e-6_steps14832_seed48027_lora - Training data:
not recorded - Local Open PL proxy score:
n/a - Promotion decision:
not attached - Bielik published target used for orientation:
65.93
The Bielik number is a published leaderboard target, not a same-harness local Bielik run, unless a separate same-harness Bielik artifact is attached.
Evaluation Artifacts
The repository may include:
eval/result_summary.jsoneval/open_pl_average.jsoneval/compare_vs_v31_broad_closed200.jsoneval/compare_vs_v48_broad_closed200.jsoneval/compare_vs_bielik_cached_broad_closed200.jsoneval/promotion_decision.json
These are local proxy artifacts. Do not report them as official leaderboard results without a full official evaluation.
Notes
v48 Qwen3.5-27B LoRA full v43-epoch SFT. Best slayer to date; closed non-RAG present avg 69.91 (>v31 63.29, >Bielik target 65.93).
Compare vs v31
{
"improved": 28,
"metrics": 38,
"missing": 0,
"regressed": 8,
"unchanged": 2
}
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Qwen/Qwen3.5-27B