Launching today
Arkor
Fine-tune and Deploy Open-weight Models in TypeScript
55 followers
Fine-tune and Deploy Open-weight Models in TypeScript
55 followers
Start a real LLM training in 10 minutes. Tell Claude Code or Codex what the model is for; they prepare the datasets and create the training project. Click "Run Training" in the local studio. Arkor runs the training, and deploys the trained model as an OpenAI-compatible API. Think Next.js and Vercel for fine-tuning: code you can review, infrastructure you do not have to manage, and a model your app can call. No GPU setup. No ML expertise required. No Python training code to write.
Arkor
Hi Product Hunt! 👋
I’m Hina, one of the makers of Arkor.
We built Arkor because we kept running into the same gap: adding an AI feature to an application is easy, but adapting a model to a specific task still feels like joining an ML infrastructure team.
The moment you want to fine-tune, you often end up maintaining a separate Python project, converting datasets into unfamiliar formats, provisioning GPUs, and moving between scripts, notebooks, and dashboards.
We wanted model development to feel more like ordinary software development.
With Arkor, the workflow starts inside your existing repository:
Tell Claude Code or Codex what behavior you want from the model.
Your coding agent can find or prepare a dataset, write conversion scripts, create the TypeScript trainer, and add evaluation.
Review the generated code and changes.
The agent runs pnpm dev, and Arkor Studio opens locally at localhost:4000.
Click Run Training, monitor the loss and checkpoints, test the trained adapter, and deploy the result.
For example, you can give your coding agent a prompt like:
Arkor is not intended to be a magical prompt-to-model black box. It is a developer-controlled workflow where coding agents can handle much of the setup, while the training code, data transformations, evaluation, and final decisions remain visible and reviewable.
We’re especially interested in feedback on:
Whether the coding-agent workflow feels intuitive
Which parts of fine-tuning still feel unclear or intimidating
What you would need before using Arkor for a production model
Which models, datasets, and deployment workflows we should support next
Thanks for checking out Arkor. We’ll be here throughout the launch and would love to hear what you try building. 🙏
Looks super cool guys! Curious to know though, how much control do users have when choosing which llm they can fine tune?
Arkor
@lucapiekarski Thanks, Luca!
We’re starting with Gemma 4 for the first release, mainly so we can make the full workflow reliable before expanding the model catalog.
We’re already hearing requests for smaller models for low-latency or local use, plus voice and vision models, so those are definitely areas we’re exploring.
What are you hoping to build, and which model would you want to fine-tune?
Arkor makes the fine-tuning workflow feel much closer to normal app development, which is exactly where this has been too painful for most dev teams.
The part I’d want most visibility into is the dataset and eval step. If the coding agent prepares the data and trainer, does Arkor Studio surface checks for train/val leakage, near-duplicate examples, or weak eval coverage before the GPU time gets spent?
can you download the resulting weights?
Arkor
@school_4_ants Thanks for asking, Hunter!
Not yet in the current release.
We launched managed-first so people could go from training to inference without setting up GPUs or deployment infrastructure.
That said, avoiding vendor lock-in is important to us, and downloadable training artifacts are on our roadmap.
For your use case, would downloading the LoRA adapter be enough, or would you also need the merged model weights?
And would Hugging Face / safetensors be your preferred format?