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Model: CNCL-Penn-State/CrPO-sft-llama-3.1-8b-instruct
Source: Original Platform

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README.md

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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}, 
+}
+```

config.json

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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
+}

generation_config.json

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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"
+}

handler.py

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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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