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Using Llama 3.1 70B Instruct on DeepInfra

Implementation guide · Llama 3.1 · AI at Meta

ServerlessOpen Source

Quick Start

  1. 1
    Create an account at DeepInfra and generate an API key.
  2. 2
    Use the DeepInfra SDK or REST API to call llama3.1-70b-instruct — see the documentation for request format.
  3. 3
    You'll be billed $0.40/1M input, $0.40/1M output tokens. See full pricing.

Code Examples

Install
pip install openai
API key
DEEPINFRA_API_KEY
Model ID
llama3.1-70b-instruct

DeepInfra uses "organization/model-name" format, e.g. "meta-llama/Meta-Llama-3-8B-Instruct" or "mistralai/Mistral-7B-Instruct-v0.3". See the DeepInfra model catalog for exact IDs.

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["DEEPINFRA_API_KEY"],
    base_url="https://api.deepinfra.com/v1/openai"
)
response = client.chat.completions.create(
    model="llama3.1-70b-instruct",
    messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)

About DeepInfra

DeepInfra offers serverless AI inference with a simple API, supporting hundreds of models across text generation, embeddings, and more. Pay-per-token pricing with no upfront commitments.

DeepInfra is a cloud inference platform offering cost-effective access to open-source AI models. It provides serverless inference for leading models from Meta, Mistral, Alibaba, and others with competitive token-based pricing.

Pricing on DeepInfra

TypePrice (per 1M)
Input tokens$0.40
Output tokens$0.40

Capabilities

Structured Outputs

About Llama 3.1 70B Instruct

The Llama 3.1 70B Instruct model is a cutting-edge large language model with 70 billion parameters, designed for instruction-following tasks. It features multilingual capabilities, supporting languages like English, German, French, and others. Fine-tuned using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF), it excels in understanding and responding to user instructions. The model can handle a context length of up to 128k tokens, making it suitable for complex dialogue systems and applications requiring detailed responses. It outperforms many existing open-source and proprietary models on various industry benchmarks, making it ideal for conversational AI, content generation, and data synthesis tasks. For more details, visit the Hugging Face page [1].

Model Specs

Released2024-07-23
Parameters70B
Context128K
ArchitectureDecoder Only
Knowledge cutoff2023-12

Provider

DeepInfra
DeepInfra

San Francisco, California, United States