Text Classification
MLX
Safetensors
English
multilingual
apple-silicon
decision-model
system-one
typed-decisions
calibrated-probabilities
structured-output
non-autoregressive
document-understanding
qwen3.8
lora
bf16
Instructions to use FrenchCastle/Solomon-MLX-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use FrenchCastle/Solomon-MLX-bf16 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download FrenchCastle/Solomon-MLX-bf16 --local-dir Solomon-MLX-bf16
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
model card
Browse files
README.md
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---
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license: apache-2.0
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base_model: Qwen/Qwen3.8-27B
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library_name: mlx
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tags:
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- mlx
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- decision-model
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- solomon
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- customs
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- hs-code
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---
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# Solomon (DoccyHealth) — full BF16 MLX package
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A ready-to-load MLX build of [DoccyHealth/Solomon](https://huggingface.co/DoccyHealth/Solomon), the open-weights
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decision model (LoRA adapter + trained decision heads on `Qwen/Qwen3.8-27B`), assembled with the release's own
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converter so that nothing has to be re-downloaded or re-converted on Apple Silicon. Everything in this repo is
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Apache-2.0: the Qwen3.8-27B backbone (Alibaba Cloud), Solomon's adapter and heads (DoccyHealth), and the
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conversion. The upstream `LICENSE`, `NOTICE` and `MODIFICATIONS.md` are included verbatim.
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## What is inside
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| part | files | provenance |
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|---|---|---|
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| backbone, BF16 (norms promoted before the unit offset, FP32-sensitive parameters kept in FP32) | `backbone/` (18 safetensors shards + tokenizer/config) | `Qwen/Qwen3.8-27B` @ `1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0`, converted by `solomon-mlx` (converter sha256 `810ec77e6a1e…`) |
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| Solomon LoRA adapter | `adapter.safetensors` | `DoccyHealth/Solomon` @ `ac4f9ccf087861451e47277607c59b2ac3782ce4`, sha256 `2addaf841ecc…` |
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| Solomon decision heads | `heads.npz` | same release, sha256 `126a9b5487dc…` |
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| binding | `binding.json` | every file's sha256, the base hashes, the converter identity and the runtime contract (`solomon-mlx-binding-v1`, profile `quality`) |
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Total 55.6 GB. `python -m solomon_mlx_hub verify <dir>` re-checks every pin without allocating tensors.
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## Load
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```python
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from solomon_mlx import Solomon # pip install from the DoccyHealth/Solomon repo, mlx/ package
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model = Solomon.load("path/to/this/repo")
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```
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The MLX package needs Apple Silicon and about 60 GB of unified memory at the `quality` (BF16) profile. The
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`choice` question type takes 2–8 options; label questions take up to 64 candidates with one independent
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probability each.
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## Provenance note
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Solomon's hub loader (`solomon_mlx_hub.prepare`) checks the licence texts of the release against hashes recorded
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in its `release.json`. At revision `ac4f9ccf` the upstream `mlx/bf16/NOTICE` and `MODIFICATIONS.md` were edited
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(commit `5c0a4a8`) without `release.json` being refreshed, so the check cannot pass on any copy of the repository.
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This build tolerated that mismatch on those two licence texts only (warning emitted, patch documented in the
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building project); the adapter, heads, base-manifest, converter-identity and every backbone checksum were
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enforced as released. The `binding.json` here records the actual sha256 of the two texts.
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## Why this exists
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Built for the HS-code classification benchmark in `FrancoisChastel/HSCodeAgent` (decision-model arm next to
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TypeSafe's Jev), where Solomon adjudicates a retrieved pool of customs precedents. Results and the evaluation
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key are in that repository.
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