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
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Download README.md from IDFluff/oxcoder-9b-oq6e: direct link, hf CLI and curl.
- Browser
- Download file 716 Bytes
-
https://huggingface.co/IDFluff/oxcoder-9b-oq6e/resolve/main/README.md
- Command line
-
hf download hf://IDFluff/oxcoder-9b-oq6e/README.md
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curl -L -o README.md https://huggingface.co/IDFluff/oxcoder-9b-oq6e/resolve/main/README.md
716 Bytes
metadata
base_model:
- OrionLLM/OxCoder-9B
tags:
- omlx
- mlx
about
oq6e quantized version of OxCoder
imatrix used
Default oMLX imatrix
rough performance metrics from quick tests/benchmarks
- roughly 445 tks/s prefill, 16 tk/s decode on m3 pro 36gb ram in practice on a fresh pi agent
- context benchmark from omlx for 131k context length started at 2k t/s prefill, finished at ~600 t/s
- throughput benchmark did not upload because of certain flags used but roughly matched above numbers at 128k
- ANE prefill with tweaked settings
- SpecPrefill with qwen3.5 0.8b model
omlx throughput test with ane prefill disabled
https://omlx.ai/benchmarks/performance/tflpzdtq
omlx version used
0.7.0 dev4