CNCL-Penn-State/CrPO-sft-llama-3.1-8b-instruct
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Model: CNCL-Penn-State/CrPO-sft-llama-3.1-8b-instruct
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---
library_name: transformers
license: mit
datasets:
- CNCL-Penn-State/MuCE-SFT
language:
- en
base_model:
- meta-llama/Llama-3.1-8B-Instruct
---
# CrPO-SFT-Llama-3.1-8B-Instruct
This is a Llama-3.1-8B-Instruct model supervised-finetuned (SFT) on the [MuCE-SFT](https://huggingface.co/datasets/CNCL-Penn-State/MuCE-SFT) dataset from the [Creative Preference Optimization](https://arxiv.org/abs/2505.14442) paper.
## Citation
```
@misc{ismayilzada2025creativepreferenceoptimization,
title={Creative Preference Optimization},
author={Mete Ismayilzada and Antonio Laverghetta Jr. and Simone A. Luchini and Reet Patel and Antoine Bosselut and Lonneke van der Plas and Roger E. Beaty},
year={2025},
eprint={2505.14442},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.14442},
}
```

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{
"architectures": [
"LlamaForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 128000,
"eos_token_id": [
128001,
128008,
128009
],
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 4096,
"initializer_range": 0.02,
"intermediate_size": 14336,
"max_position_embeddings": 131072,
"mlp_bias": false,
"model_type": "llama",
"num_attention_heads": 32,
"num_hidden_layers": 32,
"num_key_value_heads": 8,
"pretraining_tp": 1,
"rms_norm_eps": 1e-05,
"rope_scaling": {
"factor": 8.0,
"high_freq_factor": 4.0,
"low_freq_factor": 1.0,
"original_max_position_embeddings": 8192,
"rope_type": "llama3"
},
"rope_theta": 500000.0,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.50.3",
"use_cache": true,
"vocab_size": 128256
}

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{
"bos_token_id": 128000,
"do_sample": true,
"eos_token_id": [
128001,
128008,
128009
],
"temperature": 0.7,
"top_p": 0.95,
"max_new_tokens": 512,
"transformers_version": "4.50.3"
}

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import torch
from typing import Dict, List, Any
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
# get dtype
dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] == 8 else torch.float16
class EndpointHandler:
def __init__(self, path=""):
# load the model
tokenizer = AutoTokenizer.from_pretrained(path)
tokenizer.padding_side = "left"
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
model = AutoModelForCausalLM.from_pretrained(path, device_map="auto", torch_dtype=dtype)
# create inference pipeline
self.pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
inputs = data.pop("inputs", data)
parameters = data.pop("parameters", None)
if isinstance(inputs, str):
inputs = [{"role": "user", "content": inputs}]
elif isinstance(inputs, list):
if all(isinstance(i, str) for i in inputs):
inputs = [[{"role": "user", "content": i}] for i in inputs]
elif all(isinstance(i, dict) for i in inputs):
# assume the list is already in the correct format
pass
else:
raise ValueError("Inputs must be a string or a list of strings or dictionaries.")
# pass inputs with all kwargs in data
if parameters is not None:
predictions = self.pipeline(inputs, **parameters)
else:
predictions = self.pipeline(inputs)
# postprocess the prediction
responses = [{"prompt": prediction[0]["generated_text"][0]["content"], "response": prediction[0]["generated_text"][1]["content"]} for prediction in predictions]
return responses
if __name__ == "__main__":
import json
from dotenv import load_dotenv
load_dotenv()
# Example usage
handler = EndpointHandler(path="CNCL-Penn-State/CrPO-sft-llama-3.1-8b-instruct")
input_data = {
"inputs": ["Come up with an original and creative use for the following object: rope",
"Finish the sentence with an original and creative ending: When I got on the school bus...."],
"parameters": {"top_p": 0.95, "temperature": 0.7, "max_new_tokens": 256, "do_sample": True}
}
# Simulate receiving data from a request
response = handler(input_data)
# Print the response
print(json.dumps(response, indent=2))

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