| --- |
| license: cc-by-nc-4.0 |
| base_model: openjev/openjev |
| tags: |
| - decision-model |
| - zero-shot-classification |
| - agents |
| - mlx |
| - apple-silicon |
| --- |
| |
| # OpenJev, MLX 8-bit (Apple silicon, text only) |
|
|
| The language model of [OpenJev](https://huggingface.co/openjev/openjev) converted for Apple silicon with mlx-lm (8-bit, group size 64), about 27 GB. Everything about the model, the API and the results is in the main card; this page says what this build is, how it was checked and how to run it. |
|
|
| ** |
|
|
| **Text only.** The converter keeps the language model and drops the vision tower, so this build answers questions about text, JSON and DOM, not screenshots. |
|
|
| ## Measured, not assumed |
|
|
| The same frozen 10,000 text questions, the same helper code, the same fixed calibration, on a Mac (M5 Max): |
|
|
| | test | 16-bit model as served on an H100 | this MLX build on a Mac | |
| |---|---|---| |
| | 10,000 text questions, 34 public sources | 84.03% (8,403) | 84.03% (8,403); 72 questions each way, 95% interval −0.24 to +0.22 points | |
|
|
| 1.5% of individual answers change between the two (150 of 10,000), in both directions. The rule fixed before the run was "no more than 0.5 points below the served model"; it passes. Speed on the M5 Max was about 0.3 s for a short text decision (the run took 80 minutes for 10,000 questions, one at a time). |
|
|
| ## Run it |
|
|
| ```bash |
| uv venv mlx --python 3.12 |
| uv pip install --python mlx/bin/python "mlx==0.32.2" "mlx-lm==0.31.3" \ |
| "transformers==5.17.0" "openai==3.16.2" "huggingface-hub==1.32.0" |
| |
| # Download the text-only weights and the two helper files. |
| mlx/bin/hf download openjev/openjev-MLX --revision ddbabafe9fbf296d767a91ad99717752530a3927 --local-dir openjev-MLX |
| mlx/bin/hf download openjev/openjev helper/shim.py helper/shim_mlx.py \ |
| --revision 1c341f65bfe5d50fdb935c71e9739c9e0938d6c4 --local-dir openjev-api |
| |
| TOKENIZER=openjev-MLX SHIM_MODEL=openjev-MLX \ |
| READOUT_T=0.85 READOUT_NOUL_T=1.829074 READOUT_NOUL_BIAS=0 \ |
| READOUT_TARGETED=1 READOUT_INSTR_STYLE=pyrepr SHIM_STAGGER=1 \ |
| mlx/bin/python openjev-api/helper/shim_mlx.py \ |
| --helper openjev-api/helper/shim.py --model openjev-MLX --port 3000 |
| ``` |
|
|
| Then call `http://localhost:3000/v1/systemone` exactly as the main card describes. `shim_mlx.py` swaps only the helper's model client for an MLX one; prompts, option layout, readout and calibration are the helper's own code. |
|
|
| ## Licence |
|
|
| Weights: CC BY-NC 4.0 (research and non-commercial use), the same as the main repository. For a commercial licence, email [support@loopai.com](mailto:support@loopai.com). Helper and serving files: Apache 2.0. The licence texts and the base model's attribution are in `LICENSE`, `LICENSE-APACHE-2.0` and `NOTICE`. |
|
|
| OpenJev is an independent project, not affiliated with TypeSafe; Jev is their product. |
|
|
