What is DeepSeek: R1 Distill Llama 70B model?

70.6B dense transformer distilled from DeepSeek-R1 into Llama-3.3-70B-Instruct base. Focuses on reasoning, math, code. Uses 112 attention heads with RoPE.

How does DeepSeek R1 Distill Llama 70B API perform?

94.5% MATH-500, 86.7% AIME 2024, 65.2% GPQA Diamond, 57.5 LiveCodeBench. Tops distilled models in benchmarks.

What context length supports DeepSeek: R1 Distill Llama 70B?

131k tokens standard, up to 128k on some platforms. Enables complex multi-step reasoning.

Is DeepSeek R1 Distill Llama 70B API fine-tunable?

Yes, supports LoRA fine-tuning with custom data. Deploy via on-demand GPUs without rate limits.

DeepSeek: R1 Distill Llama 70B alternative to what?

Smarter than base Llama 70B in reasoning. Beats o1-mini, GPT-4o on select math/code benchmarks.

DeepSeek R1 Distill Llama 70B model hardware needs?

Dense 70B requires substantial VRAM for inference. Optimized for efficient deployment on consumer hardware.

Available now on ModelsLab · Language Model

DeepSeek: R1 Distill Llama 70B
Reason Deep. Distill Smart.

Try DeepSeek: R1 Distill Llama 70B API Documentation

Distill R1 Power Efficiently.

Math Mastery

94.5% MATH-500 Score

Leads distilled models on MATH-500 and 86.7% AIME 2024 for advanced math reasoning.

Code Precision

57.5 LiveCodeBench

Outperforms o1-mini on GPQA Diamond and LiveCodeBench for reliable code generation.

Context Scale

131k Token Window

Handles long sequences with RoPE and Flash Attention on Llama-3.3-70B base.

Examples

See what DeepSeek: R1 Distill Llama 70B can create

Copy any prompt below and try it yourself in the playground.

Math Proof

“Prove the infinitude of primes using contradiction. Provide step-by-step reasoning and formal notation.”

Code Algorithm

“Write Python code for Dijkstra's algorithm on a graph with 100 nodes. Include priority queue and edge weights.”

Logic Puzzle

“Solve this riddle: Five houses in a row, each with different color, owner nationality, drink, smoke, pet. Deduce pairings from clues.”

Physics Derivation

“Derive the Schrödinger equation from classical wave mechanics. Explain each quantum assumption step-by-step.”

For Developers

A few lines of code.
Reasoning API. One Call.

ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.

Serverless: scales to zero, scales to millions
Pay per token, no minimums
Python and JavaScript SDKs, plus REST API

API Documentation

import requests

response = requests.post(
    "https://modelslab.com/api/v7/llm/chat/completions",
    json={
  "key": "YOUR_API_KEY",
  "prompt": "",
  "model_id": ""
}
)
print(response.json())

FAQ

Common questions about DeepSeek: R1 Distill Llama 70B

Read the docs

Ready to create?

Start generating with DeepSeek: R1 Distill Llama 70B on ModelsLab.