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--- |
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library_name: transformers |
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license: llama3.2 |
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datasets: |
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- ConicCat/Lamp-P-ImplicitPreference |
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base_model: |
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- meta-llama/Llama-3.2-3B-Instruct |
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--- |
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# ConicCat/Lamp-P-Writing-Quality-RM |
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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)" |
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This model achieves a validation set accuracy of 100% and an eval loss of 0.0756. |
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For accurate scoring of long texts I highly reccomend that you chunk input text into paragraphs, compute scores for each paragraph, then average. |
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Compared to ConicCat/Litbench-Creative-Writing-RM-3B this model is focused on low level writing skill. |
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## Inference: |
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``` |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer |
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model = AutoModelForSequenceClassification.from_pretrained("ConicCat/Lamp-P-Writing-Quality-RM", torch_dtype="bfloat16") |
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tokenizer = AutoTokenizer.from_pretrained("ConicCat/Lamp-P-Writing-Quality-RM") |
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text = "Dummy text." #Expects raw text input, no instructions, chat template, or formatting. |
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tokenized_text = tokenizer(text, return_tensors="pt").to("cuda:0") |
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print(model(**tokenized_text).logits[0][0].item()) # Reward score |
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``` |
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