MLX
Safetensors
English
qwen3_5
decision-making
classification
calibration
routing
guardrails
prompt-injection
agents
games
on-device
system-one
jev
Instructions to use labqoat/qev-0.8b-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use labqoat/qev-0.8b-MLX with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download labqoat/qev-0.8b-MLX --local-dir qev-0.8b-MLX
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Commit History
9 days ago
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Card: link to squad-0.8b (was qev-s-0.8b)
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Card: link to qev-s-0.8b (MoQLE)
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10 days ago
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Correct: benchmarks guided checkpoint choice and some training data shares benchmark formats; release is SG50
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11 days ago
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Card: rename the Images section to 'Image input' (the Hub drops id=images, so #images links did not resolve)
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Card: 'Which version should I use?', per_question recommendation (JevVision fast engine 0.783), completeness fixes
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Images on the fast path: compiled + batched image requests in decide_batch, vision tower batched by grid size, per_question / cache_mb image prefixes (cache keyed by image content), image-embedding LRU; card speed (Linux) and numbers
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Image input: vision tower + merger in MLX (vision.safetensors, bf16, also in q8/), qev_mlx.py images=, card
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SHA256SUMS: the updated README.md
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Card: image input is in the transformers checkpoint labqoat/qev-0.8b (qev_image.py); this build is text-only
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12 days ago
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Model card: the full qev-0.8b card (demo, benchmarks, evaluation, training, data, limitations) with the MLX parts
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qev-0.8b-MLX: initial release
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tokenizer.json (root and q8) matches the training pre-tokenizer
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qev-0.8b-MLX: initial release
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initial commit
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