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zaydiscold/Qwopus3.6-35B-A3B-v1-MLX-bf16
Qwopus3.6-35B-A3B-v1-MLX-bf16 is a machine learning model from zaydiscold. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for mlx. The card lists the license as other.
Full non-quantized MLX bfloat16 conversion of Jackrong/Qwopus3.6-35B-A3B-v1. Reference build for this MLX ladder.
Downloads · 30 days
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.safetensors69.3 GB · 100%
From the Hugging Face model README
Full non-quantized MLX bfloat16 conversion of Jackrong/Qwopus3.6-35B-A3B-v1. Reference build for this MLX ladder.
Converted directly from the original HF bf16 safetensors. The 3/4/6/8-bit siblings are independently quantized from the same source — not chained from this repo, not converted from GGUF.
| Variant | Repo | Disk | ~Min unified RAM | Role |
|---|---|---|---|---|
| MLX bf16 (this repo) | this | 69.3 GB | ~72 GB | Reference |
| MLX 8bit | Qwopus3.6-35B-A3B-v1-MLX-8bit | 36.8 GB | ~40 GB | Near-lossless |
| MLX 6bit | Qwopus3.6-35B-A3B-v1-MLX-6bit | 28.2 GB | ~32 GB | Quality / size middle |
| MLX 4bit | Qwopus3.6-35B-A3B-v1-MLX-4bit | 19.5 GB | ~22 GB | Standard daily-use tier |
| MLX 3bit | Qwopus3.6-35B-A3B-v1-MLX-3bit | 15.2 GB | ~18 GB | Smallest practical |
Collection: Qwopus3.6-35B-A3B-v1 MLX
pip install mlx-lm
mlx_lm.generate --model zaydiscold/Qwopus3.6-35B-A3B-v1-MLX-bf16 \
--prompt "Explain quantum entanglement in one paragraph" --max-tokens 200
python -m mlx_lm convert \
--hf-path Jackrong/Qwopus3.6-35B-A3B-v1 \
--mlx-path ./Qwopus3.6-35B-A3B-v1-MLX-bf16 \
--dtype bfloat16