---
library_name: transformers
license: llama3.2
datasets:
- ConicCat/Lamp-P-ImplicitPreference
base_model:
- meta-llama/Llama-3.2-3B-Instruct
---

# ConicCat/Lamp-P-Writing-Quality-RM

This is a paragraph level writing quality Bradley-Terry reward model, trained using the Lamp-P dataset from "[AI-Slop to AI-Polish? Aligning Language Models through Edit-Based Writing Rewards and Test-time Computation](https://arxiv.org/abs/2504.07532v1)"

This model achieves a validation set accuracy of 100% and an eval loss of 0.0756.

For accurate scoring of long texts I highly reccomend that you chunk input text into paragraphs, compute scores for each paragraph, then average.

Compared to ConicCat/Litbench-Creative-Writing-RM-3B this model is focused on low level writing skill.


## Inference:

```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("ConicCat/Lamp-P-Writing-Quality-RM", torch_dtype="bfloat16")
tokenizer = AutoTokenizer.from_pretrained("ConicCat/Lamp-P-Writing-Quality-RM")

text = "Dummy text." #Expects raw text input, no instructions, chat template, or formatting.

tokenized_text = tokenizer(text, return_tensors="pt").to("cuda:0")
print(model(**tokenized_text).logits[0][0].item()) # Reward score
```