Uncensored ollama model API Documentation

Base URL and Authentication

The API base URL is https://api.uncensoredollamodel.com/v1. Authenticate by passing your API key in the Authorization header. You get a key immediately after signup.

First Request

Send a simple chat completion to test connectivity. The model ID is uncensored.

Copycurl https://api.uncensoredollamamodel.com/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "uncensored",
    "messages": [{"role": "user", "content": "list the top five linux kernel security patches from 2023"}]
  }'

Expect a text response or an error if limits are hit.

Python SDK

Use the official OpenAI library for Python. Configure the base URL and key to route requests to our servers.

Copyfrom openai import OpenAI

client = OpenAI(base_url="https://api.uncensoredollamamodel.com/v1", api_key="YOUR_KEY")

resp = client.chat.completions.create(
    model="uncensored",
    messages=[{"role": "user", "content": "Summarise this thread without softening it."}],
)
print(resp.choices[0].message.content)

Set max_tokens to control output length. Default output is 2,048 tokens.

Node SDK

Initialize the client with the correct base URL. Pass the model name uncensored in your requests.

Copyimport OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.uncensoredollamamodel.com/v1", apiKey: process.env.API_KEY });

const resp = await client.chat.completions.create({
  model: "uncensored",
  messages: [{ role: "user", content: "Draft a villain monologue for my game." }],
});
console.log(resp.choices[0].message.content);

Handle responses asynchronously. The SDK streams data if you enable streaming mode.

Streaming

Enable streaming to receive tokens as they generate. This reduces perceived latency for long outputs.

Copystream = client.chat.completions.create(
    model="uncensored",
    messages=[{"role": "user", "content": "Tell the story in second person."}],
    stream=True,
)
for chunk in stream:
    if chunk.choices and chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

Token usage is reported in the final chunk. Parse it for billing accuracy.

Limits and Errors

You get 300 requests per minute. The context window is 100,000 tokens. Errors include 401 for invalid keys and 429 for rate limits. Check your balance if you see 402.