Open Flux AI - Empowering developers with AI-driven Continuous Delivery solutions.

Welcome to Open Flux AI, a community initiative stemming from the Kube-7B project, dedicated to advancing AI expertise in Flux, Flagger, and Continuous Delivery technologies. Our mission is to use the power of AI to simplify and enhance the way developers interact with Flux and technologies around it.

Our first focus is on fine-tuning AI models to specialize in key areas such as Flux, Flagger, GitOps, and SOPS. By leveraging the raw data from Kube-7B and applying targeted Embedding techniques, we aim to create models that are highly proficient in these specific domains.

Our first major deliverable is flux-7b, a model based on Mistral 7B. flux-7b currently understands the basic knowledge of Flux, Flagger, GitOps, and SOPS. flux-7b has demonstrated to be better than ChatGPT in these contexts. See the screenshot (tested with flux-7b:v0.1).

To begin using flux-7b, follow this simple command:

ollama run chanwit/flux-7b:v0.2

We are planning to deliver our models in other formats like Llamafiles and Docker Containers. Please stay tuned.

• flux-7b-v0.1: Our first model, built on a variant of Mistral 7B, is designed to provide assistance in Flux, Flagger, GitOps, and SOPS. GGUF files.
• flux-7b-v0.2: The second version of flux-7b, finetuned from our own base model (flux-base-optimized). This version adds some augmentations to the dataset. HuggingFace.
• flux-base-optimized: Our base model, hierarchical SLERP merged from 5 Mistral-based models. HuggingFace repo.

We use Zephyr's tokenizer and prompt, which supports system prompting. In the Zephyr prompt format, the system and user inputs are encapsulated within specific tokens. The format appears as follows:

<|system|>\n
{{ system }}</s>\n
<|user|>\n
{{ user }}</s>\n
<|assistant|>\n
{{ assistant }}</s>\n

The datasets used for training are also tokenized using this structure. For the current training, the system prompt set for the model is You are a Flux AI assistant.

The chat completion endpoint is supported, and when using Ollama, there is no need to specify a system-level prompt as the model already incorporates the prompt template. However, if opting for the instruction-based completion endpoint, you must define the prompt yourself, including four specific stop words: </s>, <|system|>, <|user|>, and <|assistant|>.

At the beginning, the Open Flux AI project shares its foundational dataset with the Kube-7B project but refines it to focus on specific areas. We continuously work on expanding our dataset, especially in areas like Flux commands and Custom Resources, to further enhance the model's capabilities.

We welcome and greatly appreciate contributions, especially in the form of question and answer pairs. We are seeking contributions for new datasets centered around knowledge of Flux commands and CR generations.