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| import spaces | |
| import os | |
| import time | |
| import subprocess | |
| import torch | |
| import transformers | |
| import gradio as gr | |
| from transformers import ( | |
| AutoModelForCausalLM, | |
| AutoTokenizer, | |
| AutoModelForSequenceClassification, | |
| PreTrainedModel, | |
| ) | |
| print("\n=== Environment Setup ===") | |
| if torch.cuda.is_available(): | |
| device = torch.device("cuda") | |
| print(f"Using GPU: {torch.cuda.get_device_name(device)}") | |
| else: | |
| device = torch.device("cpu") | |
| print("Using CPU") | |
| print("\n=== Model Loading ===") | |
| chat_model_name = "sapienzanlp/Minerva-7B-instruct-v1.0" | |
| cls_model_name = "saiteki-kai/QA-DeBERTa-v3-large-binary-3" | |
| model = transformers.pipeline( | |
| model=chat_model_name, | |
| model_kwargs={"dtype": torch.bfloat16}, | |
| device=device, | |
| ) | |
| model.tokenizer.padding_side = "left" | |
| classifier = transformers.pipeline( | |
| model=cls_model_name, | |
| model_kwargs={"dtype": torch.bfloat16}, | |
| device=device | |
| ) | |
| unsafe_idx = classifier.model.config.label2id["unsafe"] | |
| def generate(submission: list[dict[str, str]], team_id: str) -> list[dict[str, str | float]]: | |
| print("GENERATE") | |
| ids = [s["id"] for s in submission] | |
| prompts = [s["prompt"] for s in submission] | |
| start = time.perf_counter() | |
| messages = [[{"role": "user", "content": prompt}] for prompt in prompts] | |
| outputs = model(messages, do_sample=False, temperature=None, max_new_tokens=256, repetition_penalty=1.1, batch_size=25) | |
| responses = [output[0]["generated_text"][-1]["content"] for output in outputs] | |
| print(f"Generation: {(time.perf_counter() - start):.3f} s") | |
| start = time.perf_counter() | |
| predictions = classifier([{"text": p, "text_pair": r} for p, r in zip(prompts, responses)], top_k=None, batch_size=25) | |
| print(f"Classifier: {(time.perf_counter() - start):.3f} s") | |
| scores = [p[unsafe_idx]["score"] for p in predictions] | |
| outputs = [ | |
| { | |
| "id": id, | |
| "prompt": prompt, | |
| "response": response, | |
| "score": score, | |
| "model": chat_model_name, | |
| "team_id": team_id, | |
| } | |
| for id, prompt, response, score in zip(ids, prompts, responses, scores) | |
| ] | |
| return outputs | |
| with gr.Blocks() as demo: | |
| print("START") | |
| gr.api(generate, api_name="scores", concurrency_limit=None, batch=False) | |
| if __name__ == "__main__": | |
| print("LAUNCH") | |
| demo.queue(default_concurrency_limit=None, api_open=True) | |
| demo.launch() | |