README.md

---
license: apache-2.0
language: [en]
library_name: safetensors
pipeline_tag: text-generation
tags: [hobbylm, mixture-of-experts, moe, sparse-moe]
---

# 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 `finish`ed.

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`](https://github.com/harishsg993010/HobbyLM)) 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](https://github.com/harishsg993010/HobbyLM):

```python
# 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`](https://huggingface.co/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.

```bash
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.