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AdarshSingh7647
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Model Details
Model Provider
AdarshSingh7647
Model Tree
Qwen/Qwen3-8B
Input Modalities
Output Modalities
Supported Functionality
eklav-mask-only is an ablation, not a standalone method: it isolates
Eklav's loss-masking trick as the single changed factor relative to std-SFT,
holding everything else fixed. Concretely:
<think>...long CoT...</think> true|false), no prompt-side
reasoning hint, no lexical filtering of verdict-bearing sentences.<think>...</think> span is present in the training
target (the model sees it in context) but is masked out of the loss — only
the post-</think> answer tokens are supervised.This isolates masking (one of Eklav's three simultaneous changes vs. std-SFT) from the other two: prompt-side context placement and lexical filtering.
This is not the same as answer-only SFT, which never includes a
<think> block in its training data at all. See
AdarshSingh7647/Eklav-Reranker-AnswerOnly-Qwen3-4B
for that method.
Same underlying data as std-SFT: AdarshSingh7647/Eklav-Reranker-CotGen-Data
LoRA adapter (PEFT format, r=32, alpha=64). Load with the base model:
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizer base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-8B")model = PeftModel.from_pretrained(base, "AdarshSingh7647/Eklav-Reranker-MaskOnly-Qwen3-8B")tokenizer = AutoTokenizer.from_pretrained("AdarshSingh7647/Eklav-Reranker-MaskOnly-Qwen3-8B")Part of the Eklav project.