DeepSeek-R1-Distill-Llama-70B

DeepSeek-R1-Distill-Llama-70B is a distilled version of DeepSeek's R1 model, fine-tuned from the Llama-3.3-70B-Instruct base model. This model leverages knowledge distillation to retain robust reasoning capabilities while enhancing efficiency.

Try now on Groq

Key Technical Specifications

• Model Architecture: Built upon the Llama-3.3-70B-Instruct framework, the model comprises 70 billion parameters. The distillation process fine-tunes the base model using outputs from DeepSeek-R1, effectively transferring reasoning patterns.

• Performance Metrics : The model demonstrates strong performance across various benchmarks:

  • AIME 2024: Pass@1 score of 70.0.
  • MATH-500: Pass@1 score of 94.5.
  • CodeForces Rating: Achieved a rating of 1,633.

Technical Details Table

Capabilities and Features

DeepSeek-R1-Distill-Llama-70B excels in the following areas:

Supported Features

Use Cases

• Mathematical Problem-Solving: Effectively addresses complex mathematical queries, making it valuable for educational tools and research applications.
• Coding Assistance: Supports code generation and debugging, beneficial for software development.
• Logical Reasoning: Performs tasks requiring structured thinking and deduction, applicable in data analysis and strategic planning.

Best Practices

• Prompt Engineering: Set the temperature parameter between 0.5 and 0.7 (ideally 0.6) to prevent repetitive or incoherent outputs.
• System Prompt: Avoid adding a system prompt and include all instructions within the user prompt.

Get Started with DeepSeek-R1-Distill-Llama-70B

Unlock the full potential of logical reasoning with DeepSeek-R1-Distill-Llama-70B - engineered for the future of AI-driven problem-solving and optimized for exceptional performance on Groq hardware with near-instant reasoning now:

pip install groq

1import os
2
3from groq import Groq
4
5client = Groq(
6    api_key=os.environ.get("GROQ_API_KEY"),
7)
8
9chat_completion = client.chat.completions.create(
10    messages=[
11        {
12            "role": "user",
13            "content": "Explain why fast inference is critical for reasoning models",
14        }
15    ],
16    model="deepseek-r1-distill-llama-70b",
17)
18
19print(chat_completion.choices[0].message.content)