Introduction of Eklav-Reranker-AnswerOnly-Qwen3-4B
Model Details of Eklav-Reranker-AnswerOnly-Qwen3-4B
Eklav-Reranker-AnswerOnly-Qwen3-4B
LoRA adapter for
passage reranking
, trained with the
answer-only SFT
method on base model
Qwen/Qwen3-4B
, from the Eklav project.
Method
Answer-only SFT is a lean baseline: the prompt carries no reasoning hint, and
the training target is just the bare verdict (
true
/
false
) with no
<think>...</think>
chain-of-thought at all. It is trained on the full
(short) response with no loss masking, since there is nothing to mask.
This is
not
the same as the
eklav-mask-only
ablation, which keeps the
full reasoning trace in the response and masks it from the loss instead of
omitting it from the data. See
AdarshSingh7647/Eklav-Reranker-MaskOnly-Qwen3-8B
for that ablation.
LoRA adapter (PEFT format, r=32, alpha=64). Load with the base model:
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B")
model = PeftModel.from_pretrained(base, "AdarshSingh7647/Eklav-Reranker-AnswerOnly-Qwen3-4B")
tokenizer = AutoTokenizer.from_pretrained("AdarshSingh7647/Eklav-Reranker-AnswerOnly-Qwen3-4B")
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