What is Llama 3.1 Nemotron 70B Instruct HF?

NVIDIA-customized 70B LLM from Llama-3.1-70B-Instruct base. Improves response helpfulness via RLHF. Tops Arena Hard at 85.0 as of Oct 2024.

How to use Llama 3.1 Nemotron 70B Instruct HF API?

Access via LLM endpoint with standard chat completions format. Supports 128k context. Integrate directly in code for inference.

What are Llama 3.1 Nemotron 70B Instruct HF benchmarks?

Arena Hard 85.0, AlpacaEval 2 LC 57.6, MT-Bench 8.98. Elo 1267 on Chatbot Arena, rank 9 as of Oct 2024.

Is Llama 3.1 Nemotron 70B Instruct HF model fast?

Outputs 44 tokens/second, TTFT 1.74s median. Below average speed but concise at 3.8M tokens on Intelligence Index.

Llama 3.1 Nemotron 70B Instruct HF alternative options?

Compare to Llama-3.1-70B-Instruct or quantized GGUF/AWQ versions. Use this API for hosted access without local setup.

What context length for llama 3.1 nemotron 70b instruct hf?

Supports 128k-130k tokens. Processes long histories and documents in one request.

Available now on ModelsLab · Language Model

Llama 3.1 Nemotron 70B Instruct HF
Helpful Responses Top Benchmarks

Try Llama 3.1 Nemotron 70B Instruct HF API Documentation

Deploy Nemotron 70B Now

Arena Leader

85.0 Arena Hard

Leads automatic alignment benchmarks over GPT-4o and Claude 3.5 Sonnet.

128K Context

Process Long Inputs

Handles 128k token context window for extended conversations and documents.

RLHF Tuned

NVIDIA Helpfulness Boost

Fine-tuned with REINFORCE on Llama-3.1-70B-Instruct for precise user responses.

Examples

See what Llama 3.1 Nemotron 70B Instruct HF can create

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

Code Review

“Review this Python function for efficiency and suggest optimizations: def fibonacci(n): if n <= 1: return n else: return fibonacci(n-1) + fibonacci(n-2)”

Tech Summary

“Summarize key advancements in transformer models since 2017, focusing on efficiency improvements and scaling laws.”

Data Analysis

“Analyze this dataset of sales figures by quarter and predict Q5 trend: Q1: 1200, Q2: 1500, Q3: 1800, Q4: 2100.”

Architecture Design

“Design a scalable microservices architecture for a cloud-based e-commerce platform handling 10k requests per second.”

For Developers

A few lines of code.
Nemotron 70B. One API 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 Llama 3.1 Nemotron 70B Instruct HF

Read the docs

Ready to create?

Start generating with Llama 3.1 Nemotron 70B Instruct HF on ModelsLab.