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openjev/openjev-MLX-4bit
openjev-MLX-4bit is a zero-shot classification model from openjev. Use it when you need labels you did not train the model on. It is set up for mlx. The card lists the license as cc-by-nc-4.0.
OpenJev decisions on Apple silicon, in about 15 GB. This text-only build uses 4-bit affine quantization with group size 64. Give it text, JSON or browser DOM, your labels and your rules. It returns a typed choice, yes…
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How the weights are stored.
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From the Hugging Face model README
OpenJev decisions on Apple silicon, in about 15 GB. This text-only build uses 4-bit affine quantization with group size 64. Give it text, JSON or browser DOM, your labels and your rules. It returns a typed choice, yes/no probability or score through the same OpenJev API.
Labels are supplied at request time. Each question with up to 52 options uses one forward pass. See the main model for the API and available formats.
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"
mlx/bin/hf download openjev/openjev-MLX-4bit --local-dir openjev-MLX-4bit
mlx/bin/hf download openjev/openjev helper/shim.py helper/shim_mlx.py \
--revision 1c341f65bfe5d50fdb935c71e9739c9e0938d6c4 --local-dir openjev-api
TOKENIZER=openjev-MLX-4bit SHIM_MODEL=openjev-MLX-4bit \
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-4bit --port 3000
Send requests to http://localhost:3000/v1/systemone using the main model's examples.
Weights: CC BY-NC 4.0 for research and non-commercial use, with attribution. Helper code: Apache 2.0. Upstream notices are included in NOTICE and LICENSE-APACHE-2.0.
OpenJev is an independent project, not affiliated with TypeSafe; Jev is their product.