Humaneval Benchmark Results

Complete Guide: Humaneval Benchmark Results

This step-by-step guide will walk you through everything you need to know about humaneval benchmark results.

Prerequisites

• Python 3.8 or higher installed
• At least 16GB RAM (32GB recommended)
• NVIDIA GPU with CUDA support (optional but recommended)
• 50GB free disk space

Step 1: Environment Setup

pip install transformers torch accelerate
git clone https://github.com/example/repo
cd repo

Step 2: Download the Model

from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("model-name")
tokenizer = AutoTokenizer.from_pretrained("model-name")

Step 3: Run Inference

inputs = tokenizer("Your prompt here", return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
print(tokenizer.decode(outputs[0]))

Performance Optimization Tips

• Use quantization to reduce memory usage
• Enable Flash Attention for faster inference
• Implement batch processing for multiple requests
• Consider using inference servers like vLLM or TGI

Common Issues and Solutions

If you encounter out-of-memory errors, try:

• Loading the model in 8-bit or 4-bit precision
• Using gradient checkpointing
• Reducing batch size