Launched this week
Arkor
Fine-tune and Deploy Open-weight Models in TypeScript
197 followers
Fine-tune and Deploy Open-weight Models in TypeScript
197 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.
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?
Pushing a fine-tune through without touching a single Python script felt weirdly good. The handoff between Claude Code preparing the data and Arkor just clicking "Run Training" was exactly the kind of low-friction flow I keep wishing existed.
Arkor
@beyzawzzc Hi Beyza!
Thanks so much, that’s exactly the kind of experience we were hoping to create.
I’m curious what you’re building. Are you already using an LLM API inside your product?
If so, what task are you asking the model to handle today, and where does the current model fall short?
Speaking as someone without an ML background — fine-tuning a model for a specific task was never even on my radar. It sounded like something that needed a research team, a GPU cluster, and a lot of Python, so I'd just filed it under "not for me." Reading how Arkor works, it suddenly feels within reach.
The reason it clicks: I run a lot of automated workflows every day, all on general-purpose models. They work, but they're not tuned for my specific tasks. Being able to fine-tune a small dedicated model per flow — without touching any infra — could genuinely push the output quality further.
Curious: for someone with a handful of narrow, repetitive workflows, when does a fine-tuned model actually start to beat just prompting a bigger general model well?
Nice launch.
As open-weight models become easier to deploy, where do you think differentiation shifts—from infrastructure, developer experience, or deployment speed?
Hi, it is really nice to have a chance to try for free as alpha users, and it is really useful product for people care about percision and latency.
I have a question, once we trained our model, how do connect to hugging face to publish our model?
Sincerely,
Inky