Model Quantization

args=training_args, train_dataset=tokenized_dataset["train"], eval_dataset=tokenized_dataset["validation"] ) # Train the model trainer.train() ``` #### 3. Self-Hosted Model Deployment ##### Environment Setup - **Hardware**: …

[Turn 8949] Assistant: Reducing inference latency while maintaining accuracy is a common challenge in deploying machine learning models, especially when using large models like those from Hugging Face Transformers. Here are several strategi…

Here's how you can implement these optimizations: #### 1. Batch Processing Process multiple texts in a single batch to take advantage of parallel processing. #### 2. Model Quantization Use quantization to reduce the precision of the mod…

# Test the batch inference function texts = ["This is a sample text"] * 5000 # Create a list of 5000 texts start_time = time.time() outputs = perform_batch_inference(texts) end_time = time.time() print(f"Inference time: {end_time - start_t…

quantized_model.to(device) # Define a function to perform batch inference with the quantized model def perform_quantized_batch_inference(texts): # Tokenize the input texts inputs = tokenizer(texts, return_tensors="pt", padding=True…

return outputs.last_hidden_state[:, 0, :] # Test the inference function text = "This is a sample text" output = perform_inference(text) print(output) ``` ->-> 10,15 [Turn 9751] Assistant: To optimize the inference time for your Huggin…

inputs = {k: v.to(device) for k, v in inputs.items()} # Perform inference with torch.no_grad(): outputs = quantized_model(**inputs) # Return the output return outputs.last_hidden_state[:, 0, :] # Test the quanti…

for future in as_completed(futures): results.extend(future.result()) return results # Example usage: queries = ["What is the capital of France?", "Who is the president of the United States?", ...] reformulated_q…

for future in as_completed(futures): results.extend(future.result()) return results # Example usage: queries = ["What is the capital of France?", "Who is the president of the United States?", ...] reformulated_q…

- `batch_size` parameter controls the number of queries processed in each batch. 4. **Caching with Redis**: - Check if the query is already cached in Redis before processing. - Store the reformulated query in Redis with an expirat…

- `batch_reformulate` method processes multiple queries in a single batch. - This reduces the overhead of tokenization and leverages parallel processing. 4. **Parallel Execution with `ThreadPoolExecutor`**: - `ThreadPoolExecutor` …

- The `max_workers` parameter controls the number of threads used for parallel processing. - The `batch_size` parameter controls the number of queries processed in each batch. 3. **Caching**: - The `reformulate` method checks if t…