HobbyLM-Computer-Use (500M MoE, GUI agent / tool use)

HobbyLM-Computer-Use is the agentic variant: function calling plus a text-only GUI agent that reads a serialized accessibility tree (no pixels, no screenshots) and emits a grounded UI action. It can also decompose a multi-step goal and drive it to completion, deciding when it's finished.

It's part of the HobbyLM family — a 500M sparse-MoE model (and its variants) built from scratch on a hobby budget: FineWeb, a handful of Modal H100 hours, a lot of ablations, and a from-scratch Rust engine (hobby-rs) to run it on a laptop CPU.

Intended use

Computer-use / GUI automation over a UI-Automation accessibility tree, and general tool / function calling. Serialize the screen as SCREEN:\n[ControlType] "Name" (state) …, give it the 12-action schema, and it returns a grounded action as JSON. Powers the Computer panel in the hobby-chat app.

Architecture

Every HobbyLM variant shares one core: a sparse Mixture-of-Experts (MoE) decoder in the modern small-MoE style (DeepSeek-V3 / OLMoE lineage), where each design choice was picked by ablation rather than by guesswork.

Component	Value
Total parameters	~500M (only a fraction is active per token)
Hidden size / layers	768 / 16 (first FFN dense, the rest MoE)
Routed experts / active	36 / top-6 (+ 1 always-on shared expert)
Attention	GQA, 12 query / 3 KV heads, decoupled head-dim 128, per-head QK-norm
Router	sigmoid gating, DeepSeek-V3 aux-loss-free load balancing, no top-k renorm
Positional	RoPE (θ up to 1e6 for the 8k-context checkpoints)
Tokenizer	GPT-2 byte-level BPE (50,304 vocab, sentinel-padded)
Optimizer	Muon on the 2-D + per-expert matrices, AdamW on everything else

The full ablation log (QK-norm is the single biggest lever; aux-loss-free beats classic aux-loss; ≥32 experts and top-6 help; embedding-scaling hurt) lives in the project's architecture notes.

Benchmarks

Held-out evaluation of the v4 checkpoint (accessibility-tree grounding + multi-step planning). param-hallucination is the rate of invented element names/arguments — strict tree-grounding in the data drives it to 0.

Split	JSON-parse	Name-F1	Value-acc	Exact-match	Param-halluc
Planning (multi-step goals)	96.5%	94.7%	—	82.6%	0.0%
Grounding (real app trees)	~96%	95.5%	91%	78.4%	0.0%
Grounding (synthetic screens)	100%	90.7%	88.6%	72.5%	0.0%

For general (non-GUI) function calling, the HobbyLM tool-use lineage scores ~24% average on BFCL v3 (grammar-constrained) — strong relevance/abstention (relevance 77.8, beating the needle reference's 61.1), weaker on parallel multi-call, which is the 500M ceiling. Exact-match understates real quality: many "misses" are ambiguous numerics (e.g. "give it a minute" → wait(60) vs the reference wait(7)).

How these were measured. All language-model scores are 0-shot through our own port of EleutherAI's lm-evaluation-harness (a custom MoELMWrapper that runs log-likelihood scoring over the HobbyLM MoE + GPT-2 tokenizer). Reference models in the comparison table were run through the identical harness and task set, so the numbers are apples-to-apples with ours — they are not copied from other model cards. We validated the harness against published cards (e.g. TinyLlama 52.75 vs card 52.99). These are small research models: read the numbers in context, not as leaderboard claims.

Usage

Python (PyTorch reference implementation)

HobbyLM is a custom sparse-MoE architecture — there's no transformers AutoModel for it, so load it with the small reference implementation from the GitHub repo:

# HobbyLM is a CUSTOM sparse-MoE architecture, so load it with the reference implementation —
# NOT transformers.AutoModelForCausalLM (there is no AutoModel mapping for this arch).
# pip install torch safetensors tiktoken huggingface_hub
# git clone https://github.com/harishsg993010/HobbyLM && cd HobbyLM

import json, torch, tiktoken
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from hobbylm.config import ModelConfig
from hobbylm.model import MoETransformer
from hobbylm.generate import generate

repo = "rootxhacker/HobbyLM-Computer-Use"
cfg = ModelConfig(**{k: v for k, v in json.load(open(hf_hub_download(repo, "config.json"))).items() if k != "preset"})
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
cfg.expert_backend = "grouped" if device.type == "cuda" else "bmm"

model = MoETransformer(cfg).to(device).eval()
model.load_state_dict(load_file(hf_hub_download(repo, "model.safetensors")))

enc = tiktoken.get_encoding("gpt2")
prompt = "USER: What is 7 plus 2?\nASSISTANT:"
ids = torch.tensor([enc.encode_ordinary(prompt)], device=device)
out = generate(model, ids, max_new_tokens=64, temperature=0.7, top_k=0, device=device,
               repetition_penalty=1.3)               # temperature=0.0 for greedy
print(enc.decode(out[0].tolist()))

For GUI / tool use, the real prompt format is TOOLS: [<schema>]\nSCREEN:\n[ControlType] "Name" (state) …\nUSER: <instruction>\nASSISTANT: and the model replies with a JSON action. The end-to-end agent loop lives in agents/ in the repo.

GGUF + hobby-rs (CPU)

GGUF builds (architecture hobbylm) live in rootxhacker/HobbyLM-gguf. They load directly in the from-scratch hobby-rs CPU engine — stock llama.cpp won't load them without registering the hobbylm architecture first.

hobby-rs --model HobbyLM-Computer-Use.gguf --prompt "..." --n 64

Training

Continue-SFT from the combined tool checkpoint on synthetic accessibility-tree data (Gemini-generated, strictly tree-validated) + real-app UI trees + planning trajectories, with a weighted loss. 13-action vocabulary (12 UI actions + finish).

Limitations

• Per-step grounding is ~80% accurate; on long goals those errors compound (short tasks usually complete, long ones can drift) and there is no per-step recovery.
• Trained on trees capped at ~45 elements (2k-context era); very large raw UI trees should be filtered.
• Near-identical controls (e.g. digit buttons) occasionally mis-ground.

License

Apache-2.0. Weights aren't a substitute for judgement — this is a research / hobby model at the 500M scale, not a production system.