Generative AI Agents Developer Contest by NVIDIA and LangChain
The contest is now closed. Thank You for your participation.
Winners
Honorable Mentions
Winner Selection Criteria
Qualifying submissions will be judged by:
- Real-world application: Evaluates the impact and novelty of the project in addressing real-world challenges and the ease of use for its target audience
- Technology integration: Assesses how effectively the developer has used NVIDIA’s LLM stack and LangChain technologies in the project
- Quality of submission: Reviews the comprehensiveness and clarity of the project details, instructions, and demo
Additional Resources
FAQs
You can find the list of countries that are open for the contest in the contest terms and conditions.
In addition to NVIDIA and LangChain tools, you can use OpenAI’s API.
Yes, you can fine-tune any LLM and use it for your application. It should be noted that, to fine-tune models, you'll need your own compute infrastructure. You can also use colab notebooks using NeMo. A few examples generative AI examples can be found in GitHub.
NVIDIA AI Foundation endpoints are models developed by the community and further optimized by NVIDIA. You can use these models for inferencing.
Self-hosted NVIDIA NIM Microservices aren’t available at the moment. An alternative is to deploy the model using Hugging Face.
We encourage you to use your personal email address when registering for the contest.
Currently, tool calling isn't supported and won't be available for this contest. Here’s an example you can use if you plan to use a NIM endpoint. You're welcome to use other tools as well.
The goal of this Hackathon is to enable developers to create compelling applications using tools and frameworks offered by NVIDIA and Langchain. You can also use other tools popular in the developer community.
You can use our endpoints with deployed models. You can create an API key and get 1,000 calls for free here NVIDIA AI Foundation Endpoints in LangChain.
Yes, you can use multimodal LLMs in your application.
Submissions will be judged based on these parameters:
- Real-world application: Evaluates the impact and novelty of the project in addressing real-world challenges and the ease of use for its target audience
- Technology integration: Assesses how effectively the developer has used NVIDIA’s LLM products and LangChain technologies in the project
- Quality of submission: Reviews the comprehensiveness and clarity of the project’s details, instructions, and demo
No, using all the tools isn't a requirement, but using one of the offerings from both NVIDIA and LangChain where applicable is important.
Team entries aren't allowed to participate in the contest.
If you’re using NVIDIA NIM, keep the API key section blank. For any other service, please provide the API keys. Best practice would be to create a config file and populate the API keys there.
Details about how to use Hugging Face Inference as a Service powered by NVIDIA NIM can be found in this technical blog: NVIDIA Collaborates With Hugging Face to Simplify Generative AI Model Deployments.
Any open-source license should be good, such as Apache 2.0, BSD, or MIT.