fer PRO

ferferefer

AI & ML interests

None yet

Recent Activity

posted an update 3 days ago

## Open Interpreter: A Game-Changer for Productivity Open Interpreter is a versatile AI tool that simplifies tasks like code debugging, web scraping, article summarization, content creation, and email automation. Whether using OpenAI’s GPT-4 or Hugging Face models, it’s a game-changer for professionals. ### Key Uses - **Debugging Code:** Fix errors and optimize code. - **Web Scraping:** Extract website data efficiently. - **Summarization:** Get concise summaries of articles and emails. - **Content Creation:** Generate high-quality articles and blogs. - **Email Automation:** Create automatic replies with ease. ### What You Need To get started, you need: - **Open Interpreter Framework** - **AI Model** (GPT-4 or Hugging Face) - **Server Access** ### Switch to Hugging Face Models Prefer open-source? Use Hugging Face’s Qwen model with this setup: ```python interpreter.llm.api_key = "YOUR KEY" interpreter.llm.model = "huggingface/Qwen/Qwen2.5-72B-Instruct" interpreter.llm.api_base = "https://api-inference.huggingface.co/models/Qwen/Qwen2.5-72B-Instruct" # Set context window and max tokens interpreter.llm.context_window = 8192 interpreter.llm.max_tokens = 4096

updated a Space 10 days ago

ferferefer/cornea_AI

updated a Space 10 days ago

ferferefer/cornea_AI

View all activity

Organizations

None yet

Posts 1

Post

427

## Open Interpreter: A Game-Changer for Productivity

Open Interpreter is a versatile AI tool that simplifies tasks like code debugging, web scraping, article summarization, content creation, and email automation. Whether using OpenAI’s GPT-4 or Hugging Face models, it’s a game-changer for professionals.

### Key Uses
- **Debugging Code:** Fix errors and optimize code.
- **Web Scraping:** Extract website data efficiently.
- **Summarization:** Get concise summaries of articles and emails.
- **Content Creation:** Generate high-quality articles and blogs.
- **Email Automation:** Create automatic replies with ease.

### What You Need
To get started, you need:
- **Open Interpreter Framework**
- **AI Model** (GPT-4 or Hugging Face)
- **Server Access**

### Switch to Hugging Face Models
Prefer open-source? Use Hugging Face’s Qwen model with this setup:

`python
interpreter.llm.api_key = "YOUR KEY"
interpreter.llm.model = "huggingface/Qwen/Qwen2.5-72B-Instruct"
interpreter.llm.api_base = "https://api-inference.huggingface.co/models/Qwen/Qwen2.5-72B-Instruct"

# Set context window and max tokens
interpreter.llm.context_window = 8192
interpreter.llm.max_tokens = 4096