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AI Models

Run any open-source LLM on your own Mac or PC — no cloud needed, no API bill.

# Pull a model and chat instantly
ollama run llama3.1

# Use from Python
import ollama
response = ollama.chat(model='llama3.1', messages=[
    {'role': 'user', 'content': 'Explain RAG in simple terms'}
])
print(response['message']['content'])

The GitHub of AI — 500K+ open models, datasets, and Spaces. Download, fine-tune, and deploy anything.

from transformers import pipeline

# Load any model from the hub in 2 lines
classifier = pipeline("text-classification",
    model="distilbert-base-uncased-finetuned-sst-2-english")

result = classifier("This research paper is excellent!")
print(result)  # [{'label': 'POSITIVE', 'score': 0.9998}]

Build LLM-powered apps that chain prompts, tools, memory, and retrieval together. Works with any model.

from langchain_openai import ChatOpenAI
from langchain.chains import RetrievalQA
from langchain_community.vectorstores import FAISS

# Build a document Q&A chatbot in minutes
llm = ChatOpenAI(model="gpt-4o-mini")
qa_chain = RetrievalQA.from_chain_type(
    llm=llm, retriever=vectorstore.as_retriever()
)
answer = qa_chain.invoke("What does the paper conclude?")

Build teams of AI agents that talk to each other and collaborate on complex tasks — like having a mini startup inside your code.

from crewai import Agent, Task, Crew

researcher = Agent(role="AI Researcher",
    goal="Find the latest LLM benchmark results",
    backstory="Expert at reading papers and extracting key findings")

writer = Agent(role="Technical Writer",
    goal="Write a clear summary for engineers",
    backstory="Explains complex AI research in plain English")

task = Task(description="Summarise GPT-4o vs Claude benchmarks",
    agent=researcher)

crew = Crew(agents=[researcher, writer], tasks=[task])
result = crew.kickoff()

The official Python/Node SDK for GPT-4o, DALL·E, and Whisper. Perfect for production automation workflows.

from openai import OpenAI

client = OpenAI()  # reads OPENAI_API_KEY from env

# Structured JSON output — great for automation
response = client.chat.completions.create(
    model="gpt-4o",
    response_format={"type": "json_object"},
    messages=[{"role": "user",
               "content": "List top 3 AI trends as JSON array"}]
)
print(response.choices[0].message.content)

Build with Claude 3.5 Sonnet — best for long documents, complex coding, and safe enterprise automation.

import anthropic

client = anthropic.Anthropic()  # reads ANTHROPIC_API_KEY

# 200K context — send a whole codebase
message = client.messages.create(
    model="claude-3-5-sonnet-20241022",
    max_tokens=1024,
    messages=[{"role": "user",
               "content": "Review this code for security issues:\n" + code}]
)
print(message.content[0].text)