| --- |
| license: apache-2.0 |
| base_model: Qwen/Qwen3.5-4B |
| library_name: mlx |
| pipeline_tag: text-classification |
| tags: [computer-use, agents, decision-model, calibration, mlx, apple-silicon, local-llm, deskmind] |
| language: [en, zh] |
| --- |
| |
| # DeskMind Brain 4b · 得心 |
|
|
| **得心，应手。** Brain is the decision model of [DeskMind](https://github.com/deskmind-ai/deskmind), open-source models and |
| tools that let an agent see your screen, decide the next step and act on your own Mac. It answers System One–style |
| typed questions (which operation next, which element, is the goal met, should I ask the user first) and returns |
| calibrated probabilities read from answer-letter logits: nothing is generated or parsed. The server is wire-compatible |
| with `POST /v1/systemone`. |
|
|
| **Try it and read more:** the [DeskMind Mac app](https://deskmind.dev) runs this model on your Mac · [how the decision models were trained](https://huggingface.co/blog/gxcsoccer/deskmind-local-decision-models) · [code, app and bench](https://github.com/deskmind-ai/deskmind) |
|
|
| This model is the **strong tier** of the Brain router: it answers the steps that [brain-0.8b](https://huggingface.co/deskmind/brain-0.8b) escalates (about 70% of steps in this release). It can also be served on its own. |
|
|
| - **Release:** G18b, revision `g18b-q8` (8-bit MLX, prompt format 3). `main` follows the current release. |
| - **Download:** 4.5 GB. |
| - **Code and docs:** [deskmind-ai/brain](https://github.com/deskmind-ai/brain). Keep `deskmind.json` next to the |
| weights: it records the prompt format the model was trained with. |
|
|
| ## Revisions |
|
|
| | Revision | What it is | |
| |---|---| |
| | `g18b-q8` | **Current release.** G18b, router threshold 0.96; used by the DeskMind app v0.3.0. | |
| | `g17-q8` | Test build, not a release. | |
| | `g14-q8` | Earlier release (G14, prompt format 3, router threshold 0.94); used by the DeskMind app v0.2.0. | |
| | `g13-q8` | Earlier release (G13, prompt format 3). | |
| | `v7b-q8` | First published build (checkpoint G11b, prompt format 2). | |
|
|
| ## Use |
|
|
| ```bash |
| git clone https://github.com/deskmind-ai/brain && cd brain |
| uv sync --extra mlx |
| uv run hf download deskmind/brain-0.8b --revision g18b-q8 --local-dir models/brain-0.8b |
| uv run hf download deskmind/brain-4b --revision g18b-q8 --local-dir models/brain-4b |
| uv run deskmind-brain-serve --predictor mlx:models/brain-0.8b --escalate-to mlx:models/brain-4b \ |
| --two-stage --port 8796 |
| ``` |
|
|
| The 4B can also answer every step on its own: |
|
|
| ```bash |
| uv run deskmind-brain-serve --predictor mlx:models/brain-4b --port 8793 --two-stage |
| ``` |
|
|
| The 4B is 4.5 GB to download and the 0.8B 0.8 GB. If `hf download` fails with `CAS Client Error`, retry with |
| `HF_HUB_DISABLE_XET=1` in front of the command. In mainland China, ModelScope carries the same files: |
|
|
| ```bash |
| uvx modelscope download --model gxcsoccer/brain-4b --revision g18b-q8 --local-dir models/brain-4b |
| ``` |
|
|
| Each reply carries a `routing` record: who answered, and why. Full walkthrough: |
| [deskmind-ai/brain](https://github.com/deskmind-ai/brain#quick-start). |
|
|
| ## Results |
|
|
| All numbers are our own runs; method and full tables are in |
| [docs/results.md](https://github.com/deskmind-ai/brain/blob/main/docs/results.md). |
|
|
| **Real macOS desktop**, [bench](https://github.com/deskmind-ai/bench) suite v25, 13 sandbox tasks × 3 runs, strict |
| pass, run through the DeskMind app on an M4 Pro (48 GB): |
|
|
| | config | pass | false "done" | decision time p50 / p95 | |
| |---|---|---|---| |
| | **Router G18b** (0.8B → 4B, 8-bit, threshold 0.96) | **39/39** | **0** | 2.85 / 9.82 s (208 decisions) | |
| | Router G14 (earlier release, threshold 0.94) | 36/39 | 0 | 0.57 / 5.25 s | |
|
|
| - Steps the 0.8B answers itself take p50 0.48 s / p95 0.66 s; steps escalated to the 4B take p50 3.6 s / p95 9.8 s. |
| About 70% of steps escalate. |
| - The G18b runs had the app's optional checks and notes off; there were no environment errors and no no-progress |
| loops. |
| - 39 runs over 13 tasks is a small sample, and runs cluster by task (a task tends to pass 3/3 or 0/3). |
| - On the earlier suite v23, the G14 router passed 35/38 (92%) and Jev (TypeSafe, hosted) 33/38 (87%). There is no |
| Jev run on v25. |
|
|
| **JevBench v1.4.2, public set** (231 items, the board's `public_accuracy` column), run locally with the official |
| `jevbench.cli`: |
|
|
| | | easy (48) | original (72) | hard (111) | public (231) | |
| |---|---|---|---|---| |
| | **Brain 4B, G18b** | 48 | 69 | 76 | **0.835** | |
| | Brain 0.8B, G18b | 48 | 56 | 63 | 0.723 | |
| | Router G18b (threshold 0.96) | 48 | 65 | 71 | 0.797 | |
| | Brain 4B, G14 | 48 | 67 | 85 | 0.866 | |
|
|
| - Public items only. The board's headline JevBench Score also weighs 308 sealed items, calibration, speed and cost; |
| we have not been scored on the sealed set. |
| - Calibration of the G18b 4B: Brier 0.269, ECE 0.089 (G14: 0.230, 0.079). |
| - Contamination check: the G18b training mix (100,703 items) shares no word 13-gram and no option set with the 231 |
| public items (0/231). |
|
|
| ## Limitations |
|
|
| - **Speed:** the G18b 0.8B's confidences sit in a narrow band (about 0.94–0.97), so at threshold 0.96 most steps go |
| to the 4B and a typical decision takes about 3 s, slower than hosted models. |
| - **General judgement:** against G14, the 4B dropped on JevBench's hard tier (85 → 76 of 111), mostly |
| temporal/numeric, hard-judgement and multi-hop items. G18b was kept for its real-desktop reliability. |
| - **Saying "done":** on the Chinese exact-text task the file was right in all 3 runs, but the model never said "done" |
| and used the full 20-step budget. The grader checks the final state, so these count as passes. |
| - **Scope:** trained and tested on macOS Finder and TextEdit sandbox tasks plus web and form decisions; untested |
| elsewhere. |
| - **Probabilities are not guarantees:** a probability is the model's own weighting of the options, not proof that the |
| step is right. |
|
|
| ## Training |
|
|
| LoRA distillation on Qwen/Qwen3.5-4B (KL to teacher distributions plus cross-entropy to labels), merged and quantized to 8 |
| bits. Desktop data comes from DAgger in sandboxed macOS tasks, with every visited state labelled by a desktop oracle, |
| plus counterexamples that break label shortcuts. Web, form and evidence items come from public datasets and synthetic |
| tasks, generated and labelled with hosted frontier-model teachers. No Jev outputs were used as labels, and the data |
| contains no real user data. Details: |
| [docs/training.md](https://github.com/deskmind-ai/brain/blob/main/docs/training.md). |
|
|
| --- |
|
|
| **中文：** 得心（DeskMind）Brain 的强档：回答 0.8B 交上来的步骤（本版约占 70%），也可以单独运行。给电脑操作 agent 的每一步做带类型、带把握程度的决策，用 MLX 在 Apple Silicon 本地运行。 |
| - **当前发布版：** G18b，版本 `g18b-q8`（8 位 MLX）；DeskMind app v0.3.0 使用此版本。 |
| - **真机成绩：** bench v25，13 个沙箱任务各跑 3 轮，通过 DeskMind app 运行，39/39 通过，没做完就说完成 0 次。0.8B |
| 直接回答的步骤中位 0.48 秒；约 70% 的步骤交给 4B，中位 3.6 秒。 |
| - **JevBench v1.4.2 公开题（231 道）：** 4B 0.835，0.8B 0.723，路由 0.797；训练数据与公开题无重合（0/231）。 |
| - **下载：** `hf download` 加 `--revision g18b-q8`；国内可用 ModelScope： |
| `uvx modelscope download --model gxcsoccer/brain-4b --revision g18b-q8 --local-dir models/brain-4b`。 |
| - 详见 [deskmind-ai/brain](https://github.com/deskmind-ai/brain/blob/main/README.zh-CN.md)。 |
|
|
| ## License |
|
|
| Apache-2.0 (see `LICENSE` and `NOTICE`). Fine-tuned from Qwen/Qwen3.5-4B (Copyright Alibaba Cloud, Apache-2.0). The DeskMind |
| name, 得心 and the logo are not covered by this licence. |
|
|
