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DeepInfra

Using DBRX Instruct on DeepInfra

Implementation guide · DBRX · Databricks Mosaic

Serverless

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 dbrx-instruct — see the documentation for request format.
  3. 3
    You'll be billed $0.60/1M input, $1.20/1M output tokens. See full pricing.

Code Examples

Install
pip install openai
API key
DEEPINFRA_API_KEY
Model ID
dbrx-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="dbrx-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$0.60
Output tokens$1.20

Capabilities

Structured Outputs

About DBRX Instruct

DBRX Instruct, developed by Databricks, is a cutting-edge large language model designed for various natural language processing tasks. It excels in text summarization, question answering, information extraction, and code generation, utilizing a fine-grained mixture-of-experts architecture with 132 billion parameters. With advanced features like rotary position encodings, gated linear units, and grouped query attention, it performs exceptionally across multiple benchmarks, even outperforming some closed-source models. Trained on a vast 12 trillion token dataset, it supports contexts up to 32,000 tokens. Although primarily effective in English, its multilingual strength isn't fully explored. Users should be cautious as it may generate inaccurate or biased outputs.

Model Specs

Released2024-03-27
Parameters132B
Context32K
ArchitectureMixture of Experts

Provider

DeepInfra
DeepInfra

San Francisco, California, United States