Instructions to use IDFluff/oxcoder-9b-oq6e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use IDFluff/oxcoder-9b-oq6e with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download IDFluff/oxcoder-9b-oq6e --local-dir oxcoder-9b-oq6e
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Create README.md
Browse files
README.md
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---
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base_model:
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- OrionLLM/OxCoder-9B
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tags:
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- omlx
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- mlx
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---
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## about
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oq6e quantized version of OxCoder
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## imatrix used
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Default oMLX imatrix
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## rough performance metrics from quick tests/benchmarks
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- roughly 445 tks/s prefill, 16 tk/s decode on m3 pro 36gb ram in practice on a fresh pi agent
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- context benchmark from omlx for 131k context length started at 2k t/s prefill, finished at ~600 t/s
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- throughput benchmark did not upload because of certain flags used but roughly matched above numbers at 128k
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- ANE prefill with tweaked settings
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- SpecPrefill with qwen3.5 0.8b model
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