for a non programmer explain this code and implantation step by step,please notify that you dont allow to use limited api looklike claude api,just implantation free step by step :
Requirements
This code was designed for NVIDIA GPUs with CUDA using PyTorch. Support for other GPU architectures may be possible by following PyTorch guidelines. Current templates would likely take an infeasible amount of time on CPU-only machines. All code is designed to be run on Linux, other operating systems will likely require major adjustments.

Installation
conda create -n ai_scientist python=3.11
conda activate ai_scientist
# Install pdflatex
sudo apt-get install texlive-full

# Install pypi requirements
pip install -r requirements.txt
When installing texlive-full, you may need to hold Enter.

Supported Models and API Keys
We support a wide variety of models including open-weight and API-only models. In general, we recommend only using frontier models above the capability of the original GPT-4.

OpenAI API (GPT-4)
By default, this uses the OPENAI_API_KEY environment variable.

Anthropic API (Claude Sonnet 3.5)
By default, this uses the ANTHROPIC_API_KEY environment variable.

Claude models via Bedrock
For Claude models provided by Amazon Bedrock, please install these additional packages:

pip install anthropic[bedrock]
Next, specify a set of valid AWS Credentials and the target AWS Region:

Set these environment variables: AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_REGION_NAME.

Claude models via Vertex AI
For Claude models provided by Vertex AI Model Garden, please install these additional packages:

pip install google-cloud-aiplatform
pip install anthropic[vertex]
Next, set up a valid authentication for a Google Cloud project, for example by providing region and project ID like so:

export CLOUD_ML_REGION="REGION" # for Model Garden call
export ANTHROPIC_VERTEX_PROJECT_ID="PROJECT_ID" # for Model Garden call
export VERTEXAI_LOCATION="REGION" # for Aider/LiteLLM call, as per https://docs.litellm.ai/docs/providers/vertex#set-vertex-project--vertex-location
export VERTEXAI_PROJECT="PROJECT_ID" # for Aider/LiteLLM call as per https://docs.litellm.ai/docs/providers/vertex#set-vertex-project--vertex-location
DeepSeek API (DeepSeek-Coder-V2)
By default, this uses the DEEPSEEK_API_KEY environment variable.

OpenRouter API (Llama3.1)
By default, this uses the OPENROUTER_API_KEY environment variable.

Semantic Scholar API (Literature Search)
Our code can also optionally use a Semantic Scholar API Key (S2_API_KEY) for higher throughput if you have one, though in principle it should work without it.

Be sure to provide the key for the model used for your runs, e.g.

export OPENAI_API_KEY="YOUR KEY HERE"
export S2_API_KEY="YOUR KEY HERE"
Setup NanoGPT
Here, and below, we give instructions for setting up the data and baseline evaluations for each template. You can only run setup steps for templates you are interested in. This is necessary to run on your machine as training times may vary depending on your hardware.

# Prepare NanoGPT data
python data/enwik8/prepare.py
python data/shakespeare_char/prepare.py
python data/text8/prepare.py
Create baseline runs (machine dependent)
# Set up NanoGPT baseline run
# NOTE: YOU MUST FIRST RUN THE PREPARE SCRIPTS ABOVE!
cd templates/nanoGPT && python experiment.py --out_dir run_0 && python plot.py
Create NanoGPT_lite baseline run. We use this for sanity-checking
# NOTE: YOU MUST FIRST RUN THE PREPARE SCRIPTS ABOVE!
cd templates/nanoGPT_lite && python experiment.py --out_dir run_0 && python plot.py