README.md

metadata

title: HobbyLM Playground
emoji: 🪶
colorFrom: indigo
colorTo: pink
sdk: gradio
sdk_version: 5.9.1
app_file: app.py
pinned: false
license: apache-2.0
short_description: Chat, see & generate with the 500M HobbyLM MoE family
models:
  - rootxhacker/HobbyLM-Base
  - rootxhacker/HobbyLM-Chat
  - rootxhacker/HobbyLM-Computer-Use
  - rootxhacker/HobbyLM-Omni
  - rootxhacker/HobbyLM-Diffusion
  - rootxhacker/HobbyLM-Image

🪶 HobbyLM Playground

An interactive demo of HobbyLM — a from-scratch 500M sparse Mixture-of-Experts language-model family (plus a 333M text-to-image DiT), all trained on a hobby budget. One Space, three things to try:

• 💬 Chat — talk to any variant: Base, Chat, Computer-Use, the multimodal Omni core, or the masked-diffusion model (which decodes by iterative denoising, not left-to-right).
• 🖼️ Ask about an image — upload a picture and question the multimodal Omni model (SigLIP2 vision encoder → MoE LLM).
• 🎨 Generate an image — text-to-image at 1024px with HobbyLM-Image (a flow-matching DiT in the DC-AE latent space, conditioned on CLIP-L).

The models use a custom hobbylm architecture, so this Space vendors the small reference implementation (hobbylm/, hobby_image/) rather than going through transformers.AutoModel.

Hardware

This Space is written for ZeroGPU (the heavy functions are decorated with @spaces.GPU). Enable ZeroGPU in the Space's hardware settings for fast chat, image understanding, and 1024px generation. It also runs on CPU (chat is slow; image generation is impractical there).

Links

• Models: https://huggingface.co/rootxhacker
• Code + the from-scratch Rust CPU engine: https://github.com/harishsg993010/HobbyLM

These are tiny research models — genuinely fluent and fun, but with the capability ceiling of a 500M model (hallucination, weak strict-format following, soft hands / multi-person in image generation). Apache-2.0.