Modelpublic

apus-ailab/APUS-OpenJev-v1-9B-MLX-8bit

sourceHugging Faceapache-2.0updated 3h agoView on Hugging Face

0likes

Model Card

APUS-OpenJev-v1-9B-MLX-8bit

English | 中文 · Source model · Collection · GGUF collection · MLX collection · MLX-4bit · GGUF / Ollama

MLX weights (8-bit affine, group size 64) of APUS-OpenJev-v1-9B for Apple Silicon Macs (mlx-lm, LM Studio).

OpenJev is a decision model: each request supplies a state, an instruction and 2–16 candidates, and the model scores candidate labels A–P. It is not a chat model.

Quick start

bash

pip install mlx-lm
hf download apus-ailab/APUS-OpenJev-v1-9B-MLX-8bit --local-dir ./openjev
python ./openjev/examples/openjev_mlx.py --model ./openjev

examples/openjev_mlx.py renders prompts with openjev_contracts.py (the training contract) and returns the exact candidate distribution.

Parity

Frozen80 with identical prompt tokens, compared with the HF BF16 release (full depth, 68/80 · 85.00%):

Run / 运行	Backend / 后端	Frozen80	= HF BF16	Max Δp
NVIDIA RTX PRO 6000 (CUDA)	mlx 0.32.2 on Linux x86_64 (Device(gpu, 0))	69/80 · 86.25%	79/80	0.0622

Converted and scored with MLX on Linux (CUDA); the files are platform-independent and load unchanged on Apple Silicon (for 4B, the same kind of file gave identical decisions on CUDA and Metal). Peak memory on Frozen80 was 11.28 GB; plan for a Mac with at least 16 GB of unified memory. Frozen80 is a reused development panel, not a blind benchmark. Per-question rows (candidate probabilities, choice, correctness; join with Frozen80 by panel_index): evaluation/per-question/.

Conversion

—mlx-lm 0.31.3 / mlx 0.32.2; 8-bit affine, group size 64.
—GDN A_log and linear_attn.norm.weight keep their source precision (FP32 in the 35B release).
—Full depth only, text only, probabilities not calibrated.

License

Apache-2.0, inherited from the source model; see LICENSE. Base model: Qwen/Qwen3.5-9B.

Authors: gumpcheng (xDAN2099), zhangxu, APUS AI-LAB.