We use essential cookies to make our site work. With your consent, we may also use non-essential cookies to improve user experience and analyze website traffic…

DeepInfra raises $107M Series B to scale the inference cloud — read the announcement

How to deploy Databricks Dolly v2 12b, instruction tuned casual language model.
Published on 2023.04.12 by Yessen Kanapin
How to deploy Databricks Dolly v2 12b, instruction tuned casual language model.

Databricks Dolly is instruction tuned 12 billion parameter casual language model based on EleutherAI's pythia-12b. It was pretrained on The Pile, GPT-J's pretraining corpus. databricks-dolly-15k open source instruction following dataset was used to tune the model.

Getting started

To get started, you'll need an API key from DeepInfra.

  1. Sign up or log in to your DeepInfra account
  2. Navigate to the Dashboard / API Keys section
  3. Create a new API key if you don't have one already

Deployment

You can deploy the databricks/dolly-v2-12b model easily through the web dashboard or by using our API. The model will be automatically deployed when you first run an inference request.

Inference

You can use it with our REST API. Here's how to call the model using curl:

curl -X POST \
    -d '{"prompt": "Who is Elvis Presley?"}' \
    -H 'Content-Type: application/json' \
    -H "Authorization: Bearer YOUR_API_KEY" \
    'https://api.deepinfra.com/v1/inference/databricks/dolly-v2-12b'
copy

We charge per inference request execution time, $0.0005 per second. Inference runs on Nvidia A100 cards. To see the full documentation of how to call this model, check out the model page on our website.

You can browse all available models on our models page.

If you have any question, just reach out to us on our Discord server.

Related articles
Gemma 4 Model Overview: Features, Architecture & Use CasesGemma 4 Model Overview: Features, Architecture & Use Cases<p>Gemma 4 is Google DeepMind&#8217;s latest family of open-weight models, released on April 3, 2026 under the Apache 2.0 license. The family spans four model sizes — from edge-optimized variants for mobile devices to a 31B dense model for server-side deployments — with every model supporting multimodal input, built-in reasoning, and a context window of [&hellip;]</p>
DeepSeek V3.2 API Benchmarks: Latency, Throughput & CostDeepSeek V3.2 API Benchmarks: Latency, Throughput & Cost<p>About DeepSeek V3.2 DeepSeek V3.2 is a state-of-the-art large language model that unifies conversational speed and deep reasoning in a single 685B parameter Mixture of Experts (MoE) architecture with 37B parameters activated per token. It is built around three key technical breakthroughs: DeepSeek V3.2 achieved gold-medal performance in the 2025 International Mathematical Olympiad (IMO) and [&hellip;]</p>
OpenClaw Use Cases That Deliver Real ROIOpenClaw Use Cases That Deliver Real ROI<p>An OpenClaw agent that reads your email, opens pull requests, and watches a server is only useful if running it doesn&#8217;t feel like leaving the meter running. That&#8217;s the quiet constraint behind every OpenClaw use cases discussion. Most of the workflows people show off (morning briefings, multi-agent research, ambient monitoring) only make sense if each [&hellip;]</p>