How to Try Uncensored Deepseek R1 with Perplexity R1 1776

Perplexity AI's introduction of the uncensored DeepSeek R1 1776 model represents a significant development in accessible, open-source artificial intelligence. This modified version of China's DeepSeek R1 reasoning model removes previous content restrictions while maintaining strong technical capabilities in mathematics, coding, and logical analysis. As an open-source model released under MIT licensing, it offers developers and researchers unprecedented flexibility in AI implementation.

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Key Features of R1 1776

The 1776 variant distinguishes itself through three core characteristics. First, it eliminates the original model's Chinese government-mandated content filters, enabling discussions of historically sensitive topics. Second, Perplexity retrained the model using constitutional AI techniques to preserve factual accuracy while removing ideological biases. Third, all user interactions remain stored on U.S.-based servers with enterprise-grade encryption, addressing privacy concerns about data sovereignty.

The model retains the original DeepSeek R1's technical capabilities - a 671B parameter architecture with 37B active parameters during inference. Benchmarks show equivalent performance to OpenAI's original models in mathematical reasoning (GSM8K: 84.3%), code generation (HumanEval: 79.6%), and general knowledge tasks (MMLU: 76.5%), while operating at significantly lower computational costs.

Implementation Through OpenRouter

OpenRouter provides the most accessible method for integrating R1 1776 into applications. This API aggregation platform handles authentication, rate limiting, and billing while offering OpenAI-compatible endpoints. Here's how to implement it:

1. API Setup
Create an OpenRouter account and generate an API key from the dashboard. The platform offers:

Free tier: 100 monthly queries (model: deepseek/deepseek-r1:free)

Paid plans: $0.80 per million input tokens, $2.40 per million output tokens

2. Client Configuration
Use the OpenAI SDK with modified base URL:pythonfrom openai import OpenAI client = OpenAI(    base_url="https://openrouter.ai/api/v1",    api_key="your_openrouter_key_here")

For JavaScript implementations:javascriptconst OpenAI = require('openai');const openai = new OpenAI({  apiKey: 'your_openrouter_key_here',  baseURL: 'https://openrouter.ai/api/v1'});

3. API Request Structure
Send requests using the standard chat completions format:pythonresponse = client.chat.completions.create(  model="deepseek/deepseek-r1",  messages=[    {"role": "system", "content": "You are an uncensored AI assistant"},    {"role": "user", "content": "Explain quantum entanglement"}  ],  temperature=0.7,  max_tokens=500)

Key parameters:

temperature: Controls randomness (0-2)

top_p: Nucleus sampling threshold

max_tokens: Response length limit

stream: Enable real-time token streaming

4. Response Handling
Process responses using standard OpenAI format:pythonif response.choices:    print(response.choices[0].message.content)else:    print("Error: Empty response")

For streaming responses:pythonstream = client.chat.completions.create(    model="deepseek/deepseek-r1",    messages=[...],    stream=True)for chunk in stream:    content = chunk.choices[0].delta.content     if content:        print(content, end="")

Advanced Implementation

The model supports several specialized features:

Structured Output Generation
Force JSON responses using response_format:pythonresponse = client.chat.completions.create(  model="deepseek/deepseek-r1",  response_format={"type": "json_object"},  messages=[    {"role": "system", "content": "Output JSON"},    {"role": "user", "content": "List 3 programming languages with typing systems"}  ])

Context Management
Implement conversation history tracking:pythonconversation = [    {"role": "system", "content": "Maintain context for programming discussions"}]def chat_round(user_input):    conversation.append({"role": "user", "content": user_input})    response = client.chat.completions.create(        model="deepseek/deepseek-r1",        messages=conversation     )    assistant_reply = response.choices[0].message.content     conversation.append({"role": "assistant", "content": assistant_reply})    return assistant_reply

Error Handling
Implement robust error recovery:pythonfrom tenacity import retry, stop_after_attempt, wait_exponential @retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))def safe_completion(prompt):    try:        response = client.chat.completions.create(...)        return response     except APIError as e:        print(f"API Error: {e}")        raise

Ethical Considerations

While the uncensored nature enables open inquiry, developers should implement:

Content moderation layers for public-facing applications

Usage logging for abuse detection

Clear user disclosures about AI-generated content

Rate limiting to prevent automated misuse

The combination of Perplexity's modified R1 1776 model with OpenRouter's infrastructure creates powerful opportunities for developing uncensored AI applications. By following proper implementation practices and ethical guidelines, developers can leverage this technology for research, creative projects, and specialized analytical tools while maintaining responsible oversight.

from Anakin Blog http://anakin.ai/blog/deepseek-uncensored/
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