Image-Text-to-Text
MLX
Safetensors
qwen3_5
krill
qwythos
vision-language
nvfp4
long-context
conversational
How to use from
PiConfigure the model in Pi
# Install Pi:
npm install -g @mariozechner/pi-coding-agent# Add to ~/.pi/agent/models.json:
{
"providers": {
"mlx-lm": {
"baseUrl": "http://localhost:8080/v1",
"api": "openai-completions",
"apiKey": "none",
"models": [
{
"id": "srv-sngh/Qwythos-9B-Claude-Mythos-5-1M-mlx-nvfp4"
}
]
}
}
}Run Pi
# Start Pi in your project directory:
piQuick Links
Qwythos-9B-Claude-Mythos-5-1M — MLX nvfp4 (complete VLM, Krill-native)
A mixed-precision nvfp4 (group 16) quantization of empero-ai/Qwythos-9B-Claude-Mythos-5-1M, a Qwen3.5-class hybrid vision-language model.
Original model and weights by empero-ai (Qwythos-9B-Claude-Mythos-5-1M). Full credit to them; this repo only re-quantizes their model.
Why this build
- 👁️ Complete vision-language model — the vision tower is included. This build keeps the full VLM (text decoder + vision tower), not a text-only strip.
- 🎯 nvfp4 mixed precision. The decoder is nvfp4 at group size 16, with
down_projando_projprotected at 8-bit and the vision tower kept at higher precision. Smaller and faster than int4 at comparable quality. - ⚡ Native Krill runtime. Runs as a native Swift + MLX model on Apple Silicon, on Krill's from-scratch runtime for the Qwen3.5 hybrid GatedDeltaNet (SSM) + full-attention decoder — not an mlx_vlm passthrough.
- 🧵 Long context. 262K native (1M via YaRN rope-scaling upstream).
Run in Krill (recommended)
# install Krill
brew tap srvsngh99/krill && brew install krill
# or:
curl -fsSL https://raw.githubusercontent.com/srvsngh99/Krill/main/install.sh | sh
# run Qwythos nvfp4 (pulls this repo)
krill run qwythos-9b-nvfp4 "Give three tips for staying focused while studying."
krill update
Run with mlx_vlm (text + vision)
pip install -U mlx-vlm
python -m mlx_vlm generate --model srv-sngh/Qwythos-9B-Claude-Mythos-5-1M-mlx-nvfp4 \
--prompt "Describe this image." --image path/to/image.jpg --max-tokens 200
About the base model
A Qwen3.5-class hybrid VLM: the text decoder interleaves GatedDeltaNet linear-attention (SSM) layers with full softmax-attention every fourth layer, plus a vision tower. Full credit to the original creators, empero-ai.
Quantization
| field | value |
|---|---|
| format | MLX nvfp4 (mixed precision) |
| group size | 16 |
| protected | down_proj, o_proj @ 8-bit affine; vision tower at higher precision |
| size | ~6.4 GB |
| contents | complete VLM (text decoder + vision tower) |
In Krill, the text decoder runs natively; the vision tower currently runs via mlx_vlm (native vision is a follow-up).
License
apache-2.0, matching the base model empero-ai/Qwythos-9B-Claude-Mythos-5-1M.
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Model size
2B params
Tensor type
U8
·
U32 ·
BF16 ·
Hardware compatibility
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Quantized
Model tree for srv-sngh/Qwythos-9B-Claude-Mythos-5-1M-mlx-nvfp4
Base model
Qwen/Qwen3.5-9B-Base Finetuned
Qwen/Qwen3.5-9B Finetuned
empero-ai/Qwythos-9B-Claude-Mythos-5-1M
Start the MLX server
# Install MLX LM: uv tool install mlx-lm# Start a local OpenAI-compatible server: mlx_lm.server --model "srv-sngh/Qwythos-9B-Claude-Mythos-5-1M-mlx-nvfp4"