DeepSeek R1 Distill Llama 70B

Preview

deepseek-r1-distill-llama-70b

Try it in Playground

TOKEN SPEED

~260 tps

INPUT

Text

OUTPUT

Text

CAPABILITIES

Tool Use, JSON Mode, Reasoning

DeepSeek / Meta

model card

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 and deliver exceptional performance on mathematical and logical reasoning tasks with Groq's industry-leading speed.

PRICING

Input

$0.75

1.3M / $1

Output

$0.99

1.0M / $1

LIMITS

CONTEXT WINDOW

131,072

MAX OUTPUT TOKENS

131,072

Key Technical Specifications

Model Architecture

Built upon the Llama-3.3-70B-Instruct framework, the model is comprised of 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

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

Experience the reasoning capabilities of deepseek-r1-distill-llama-70b with Groq speed now:

shell

pip install groq

Python

from groq import Groq
client = Groq()
completion = client.chat.completions.create(
    model="deepseek-r1-distill-llama-70b",
    messages=[
        {
            "role": "user",
            "content": "Explain why fast inference is critical for reasoning models"
        }
    ]
)
print(completion.choices[0].message.content)