---
license: apache-2.0
base_model: THUDM/GLM-Z1-9B-0414
tags:
  - lora
  - passage-reranking
  - answer-only-sft
  - eklav
---

# Eklav-Reranker-AnswerOnly-GLM-Z1-9B

LoRA adapter for **passage reranking**, trained with the **answer-only SFT**
method on base model `THUDM/GLM-Z1-9B-0414`, 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](https://huggingface.co/AdarshSingh7647/Eklav-Reranker-MaskOnly-Qwen3-8B)
for that ablation.

## Training data

[AdarshSingh7647/Eklav-Reranker-AnswerOnly-Data](https://huggingface.co/datasets/AdarshSingh7647/Eklav-Reranker-AnswerOnly-Data)

## Checkpoint

Final checkpoint, step **5968** (training complete).

## Usage

LoRA adapter (PEFT format, r=32, alpha=64) using the `glmz1` chat template.
Load with the base model:

```python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = AutoModelForCausalLM.from_pretrained("THUDM/GLM-Z1-9B-0414")
model = PeftModel.from_pretrained(base, "AdarshSingh7647/Eklav-Reranker-AnswerOnly-GLM-Z1-9B")
tokenizer = AutoTokenizer.from_pretrained("AdarshSingh7647/Eklav-Reranker-AnswerOnly-GLM-Z1-9B")
```

Part of the [Eklav](https://github.com/AdarshSingh7647/Eklav) project.