---
license: apache-2.0
library_name: mlx
tags:
  - mlx
  - krill
  - qwen3_5
  - qwythos
  - vision-language
  - nvfp4
  - long-context
base_model: empero-ai/Qwythos-9B-Claude-Mythos-5-1M
pipeline_tag: image-text-to-text
---

# 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](https://huggingface.co/empero-ai/Qwythos-9B-Claude-Mythos-5-1M)**, a Qwen3.5-class hybrid vision-language model.

> Original model and weights by **[empero-ai](https://huggingface.co/empero-ai)** ([Qwythos-9B-Claude-Mythos-5-1M](https://huggingface.co/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_proj` and `o_proj` **protected 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)

```bash
# 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)

```bash
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](https://huggingface.co/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](https://huggingface.co/empero-ai/Qwythos-9B-Claude-Mythos-5-1M).