Qwythos-9B-Claude-Mythos-5-1M-MLX-bf16
Full-precision (bf16) MLX-format conversion of empero-ai/Qwythos-9B-Claude-Mythos-5-1M,
a Qwen3.5-9B–based multimodal (vision + text) reasoning model with a 1M-context configuration.
This is the unquantized MLX variant — weights are unchanged from the source, just re-saved in MLX's native layout with a sanitised config. For a smaller download see the 4-bit version.
Details
| Base | empero-ai/Qwythos-9B-Claude-Mythos-5-1M (Qwen3_5ForConditionalGeneration, VLM) |
| Precision | bfloat16 (no quantization) |
| Size | ~18 GB |
| Tool | mlx_vlm.convert --dtype bfloat16 (mlx-vlm 0.4.4) |
| Architecture | qwen3_5 VLM (qwen3_vl-style vision tower + linear-attention text model) |
Why bf16?
This build is for maximum fidelity on Apple Silicon: the weights are bit-for-bit the original's, so output quality is identical — but it runs on MLX's Metal backend, which generates ~2× faster than the original on PyTorch-MPS at the same memory footprint. Pick this when you want the original's exact behaviour with a native Apple Silicon speedup, and you have the RAM to spare.
vs the original
Everything below is on the same Apple M4 Pro: identical 104-token chat prompt, 128 new
tokens, greedy. "Original (PyTorch-MPS)" = the upstream empero-ai bf16 weights run through
🤗 transformers with its default attention path (no flash-linear-attention — the typical
out-of-the-box experience). Perplexity is on a fixed 252-token English passage.
| Metric | Original (PyTorch-MPS) | This build — MLX bf16 |
|---|---|---|
| Generation | 7.4 tok/s | 14.5 tok/s (~2×) |
| Prefill | 235 tok/s | 270 tok/s |
| Peak memory | 19.1 GB | 19.1 GB (same) |
| Perplexity | 5.74 | 5.74 (identical) |
Bottom line: same weights, same quality, ~2× faster generation than the original's PyTorch-MPS path — pure framework gain, no quantization tradeoff. If you want a ~70%-smaller download and ~3× less memory (at ~25% higher perplexity), see the 4-bit build.
Usage
pip install mlx-vlm
python -m mlx_vlm generate \
--model BRlin/Qwythos-9B-Claude-Mythos-5-1M-MLX-bf16 \
--prompt "Who are you?" --max-tokens 128
Also runnable with oMLX as an OpenAI-compatible chat model. Peak memory for text generation is ~19 GB.
Conversion notes
The source config needs two sanitisations before mlx_vlm.convert (mlx-vlm 0.4.x) accepts
this model; both are baked into this repo's config.json:
text_config.rope_parameters.partial_rotary_factor— the YaRN-edited source moved this key to the top level oftext_config; mlx-vlm reads it from insiderope_parameters. Re-added as0.25.vision_config.model_type— source uses HF'sqwen3_5_vision; mlx-vlm'sVisionModelonly acceptsqwen3_vl/qwen3_5/qwen3_5_moe. Set toqwen3_5.
torchvision must also be importable in the conversion env (transformers' AutoProcessor
builds Qwen3VLVideoProcessor). mlx-vlm's qwen3_5 rope does not apply YaRN scaling, so the
YaRN keys in rope_parameters are inert; max_position_embeddings is left at 1,048,576.
Attribution
All model behaviour, identity, and training credit belong to Empero AI (the
empero-ai/Qwythos-9B-Claude-Mythos-5-1M authors). This repo only provides an MLX-format
conversion for Apple Silicon.