HobbyLM-GGUF
GGUF builds of every HobbyLM language model — one file per variant, all sharing the same 500M sparse-MoE core. These are the files you actually run on a laptop CPU.
| File | Model | What it's for | Headline number |
|---|---|---|---|
HobbyLM-Base.gguf |
Base | pretrained foundation LM | 44.05 avg (0-shot, our harness) |
HobbyLM-Chat.gguf |
Chat | instruction / chat | 42.5 avg (alignment-tax dip from base) |
HobbyLM-Computer-Use.gguf |
Computer-Use | GUI agent + tool calling | 95% name-F1, 0% param-hallucination |
HobbyLM-Omni.gguf |
Omni | multimodal core (text+image+audio) | VQAv2 47.0 / GQA 39.2 |
HobbyLM-Diffusion.gguf |
Diffusion | masked-diffusion LM | 117 tok/s on H100 (~2.7× AR) |
Full benchmark tables, methodology, and limitations are on each model's own card (linked above).
Running them
# from https://github.com/harishsg993010/HobbyLM
hobby-rs --model HobbyLM-Chat.gguf --prompt "The capital of France is" --n 48
⚠️ These use a custom hobbylm architecture
Every GGUF sets general.architecture = hobbylm (all metadata keys are hobbylm.*). Stock llama.cpp will
not load them — they need the from-scratch hobby-rs engine,
or a llama.cpp patched to register the hobbylm arch (GQA + per-head QK-norm + sigmoid-gated MoE + aux-free
routing bias + 1 shared expert + a leading dense layer). HobbyLM-Diffusion additionally carries diffusion.*
metadata and needs the diffusion-aware (bidirectional, iterative-denoise) decoder.
License
Apache-2.0.