ReformulationModel

Here's an optimized version of your code that incorporates these strategies: ```python import torch from transformers import AutoModelForSeq2SeqLM, AutoTokenizer from concurrent.futures import ThreadPoolExecutor, as_completed class Reform…

[Turn 10417] Assistant: Certainly! To achieve high throughput using Hugging Face Transformers, you can leverage batch processing and parallel execution. Here's a detailed example of how to use the library to process a large number of querie…

model = ReformulationModel() def process_queries(queries, batch_size=100, max_workers=10): with ThreadPoolExecutor(max_workers=max_workers) as executor: futures = [executor.submit(model.batch_reformulate, queries[i:i+batch_size…

import torch from transformers import AutoModelForSeq2SeqLM, AutoTokenizer from concurrent.futures import ThreadPoolExecutor, as_completed import redis class ReformulationModel: def __init__(self): self.model = AutoModelForSeq2…

outputs = self.model.generate(**inputs) reformulated_query = self.tokenizer.decode(outputs[0], skip_special_tokens=True) self.redis_client.set(query, reformulated_query, ex=3600) # Cache for 1 hour return re…

def __init__(self): self.model = ReformulationModel() def process_queries(self, queries, batch_size=100, max_workers=10): with ThreadPoolExecutor(max_workers=max_workers) as executor: futures = [executor…

def __init__(self): self.model = ReformulationModel() def process_queries(self, queries, batch_size=100, max_workers=10): with ThreadPoolExecutor(max_workers=max_workers) as executor: futures = [executor…

[Turn 10429] Assistant: To achieve the desired throughput of 3,500 queries per second, you need to address several potential bottlenecks in your current implementation. The primary areas to focus on are: 1. **Sequential Processing**: Your …