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…

NVIDIA Nemotron 3 Super - blazing-fast agentic AI, ready to deploy today!

Fork of Text Generation Inference.
Published on 2023.08.09 by Nikola Borisov
Fork of Text Generation Inference.

The text generation inference open source project by huggingface looked like a promising framework for serving large language models (LLM). However, huggingface announced that they will change the license of code with version v1.0.0. While the previous license Apache 2.0 was permissive, the new one is restrictive for our use cases.

Forking the project

We decided to fork the project and continue to maintain it under the Apache 2.0 license. We will continue to contribute to the project and keep it up to date. We will accept pull requests from the community, and we will keep the project truly open source and free to use.

Here is a link to the code: https://github.com/deepinfra/text-generation-inference

We hope that in time a community of other developers and organizations that want to keep this project truly open source will form around it.

License changes mid-flight

Sadly it is becoming more and more common for popular open source projects to change their license after they gain some traction. This happened with MongoDB, Grafana, ElasticSearch, and many others. As a developer, when you decide to adopt a particular open source project, you start investing time and effort into using it. You build your application around it, and you start depending on it. Then, suddenly, the license changes, and you might be forced to find an alternative.

Imagine if meta changes the license of pytorch. Or if tomorrow huggingface decides to change the license of transformers in a similar way to prohibit commercial use.

We believe that the changing of the license of open source projects mid-flight is a unfriendly move towards the community.

If you need any help, just reach out to us on our Discord server.

Related articles
From Precision to Quantization: A Practical Guide to Faster, Cheaper LLMsFrom Precision to Quantization: A Practical Guide to Faster, Cheaper LLMs<p>Large language models live and die by numbers—literally trillions of them. How finely we store those numbers (their precision) determines how much memory a model needs, how fast it runs, and sometimes how good its answers are. This article walks from the basics to the deep end: we’ll start with how computers even store a [&hellip;]</p>
Best API for Kimi K2.5: Why DeepInfra Leads in Speed, TTFT, and ScalabilityBest API for Kimi K2.5: Why DeepInfra Leads in Speed, TTFT, and Scalability<p>Kimi K2.5 is positioned as Moonshot AI’s “do-it-all” model for modern product workflows: native multimodality (text + vision/video), Instant vs. Thinking modes, and support for agentic / multi-agent (“swarm”) execution patterns. In real applications, though, model capability is only half the story. The provider’s inference stack determines the things your users actually feel: time-to-first-token (TTFT), [&hellip;]</p>
Pricing 101: Token Math & Cost-Per-Completion ExplainedPricing 101: Token Math & Cost-Per-Completion Explained<p>LLM pricing can feel opaque until you translate it into a few simple numbers: input tokens, output tokens, and price per million. Every request you send—system prompt, chat history, RAG context, tool-call JSON—counts as input; everything the model writes back counts as output. Once you know those two counts, the cost of a completion is [&hellip;]</p>