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popfido/Qwythos-9B-Claude-Mythos-5-1M-BF16-MLX-MTP-Visual
Qwythos-9B-Claude-Mythos-5-1M-BF16-MLX-MTP-Visual is a image-text-to-text model from popfido. Use it for the image-text-to-text 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 apache-2.0.
Full-precision MLX conversion of empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF (Qwen3.5-9B, hybrid GatedDeltaNet + attention) including the MTP head and the Qwen3.5-9B vision tower.
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From the Hugging Face model README
Full-precision MLX conversion of empero-ai/Qwythos-9B-Claude-Mythos-5-1M-GGUF
(Qwen3.5-9B, hybrid GatedDeltaNet + attention) including the MTP head and
the Qwen3.5-9B vision tower.
Converted from the MTP-BF16 GGUF (+ the F16 mmproj) to float16 MLX
weights — the highest-fidelity, non-quantized variant (no Q8/4-bit step).
For smaller/faster options see the Q8 and oQ4 repos. Tool:
popfido/gguf2mlx @ feat/qwen35-mtp-vision.
Activations were compared layer-by-layer against llama.cpp (build 9590,
eval-callback) on identical token ids:
out_proj. Greedy generation is correct (factual
recall, counting, arithmetic); chat is coherent.mtp.* (loads via MTP-aware loaders; stock mlx-lm
strips it automatically).mlx-vlm qwen3_5 model;
image descriptions match llama.cpp mtmd output.−1 to undo llama.cpp's +1).A_log = log(−ssm_a).[k0_v0, k1_v1, …]); permuted to the grouped layout the mlx kernel expects.from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template
model, processor = load("popfido/Qwythos-9B-Claude-Mythos-5-1M-BF16-MLX-MTP-Visual")
prompt = apply_chat_template(processor, model.config, "Describe this image.", num_images=1)
print(generate(model, processor, prompt, image=["image.png"], max_tokens=128).text)
Recommended sampling: temperature 0.6, top_p 0.95, top_k 20, repetition_penalty 1.05.
Inherits apache-2.0 from the base model. All credit for the model to empero-ai; this repo is an MLX-format conversion only.