Qwen3.8-Flash-Next-NanoSeedLM-P3

Experimental. The routed experts of this model are SeedLM seeds. We measured its quality against the 4-bit MLX model only, not against BF16 (see Quality). For normal work, use an established quantization of Qwen/Qwen3.8-Flash-Next.

Qwen3.8-Flash-Next-NanoSeedLM-P3 is Qwen/Qwen3.8-Flash-Next with its routed experts as SeedLM seeds. Each block of 8 expert weights is a 16-bit LFSR seed, 3 int4 coefficients and a 4-bit exponent code. The GPU makes the weights from the seeds inside the matrix multiply. All other weights come unchanged from mlx-community/Qwen3.8-Flash-Next-4bit-mtp (4-bit affine, group size 32), with its MTP head.

How it is different from the 4-bit MLX model

Part	This model	mlx-community 4-bit
Routed experts (120.8 B weights, 48 layers x 512)	SeedLM P = 3, 4.0 bits per weight	4-bit affine, group 32 (5.0 bits per weight)
Attention, DeltaNet, shared experts, routers, PLE table, embedding, LM head, vision, MTP head	The same tensors	4-bit affine (group 32), 8-bit routers (group 64), BF16
Size	98.2 GB	113.3 GB

The seed search tried all 65,535 seeds for each block. It weighted the error of each weight by the mean square of its input activation, from the Unsloth imatrix (imatrix_unsloth.gguf_file, 45 chunks of 18,432 tokens). Read Make a model folder.

Quality

Against mlx-community/Qwen3.8-Flash-Next-4bit-mtp, on 1,024 tokens of wikitext-2 (both models on the NanoSeedLM CUDA engine):

Model	Mean KL divergence	Median KL divergence	Top-1 agreement	Perplexity
4-bit MLX model	0	0	100%	2.396
This model (P = 3)	0.237	0.019	88.2%	2.480

The reference is a quantized model, not BF16, and one text of 1,024 tokens is a small sample. The relative error of the routed expert weights, the mean over the 48 layers:

Projection	Relative error	Activation-weighted relative error
gate_proj	0.1261	0.1122
up_proj	0.1260	0.1125
down_proj	0.1272	0.1106

Weight error does not predict KL divergence well. We will add KL divergence against the BF16 model to this card. Read the measurements.

Platforms

• Apple Silicon Macs with mlx-vlm 0.7.12 or later (--trust-remote-code). The model files nanoseedlm_qwen4.py and nanoseedlm_seeds.py are in this repository.
• oMLX: not yet. oMLX removes model_file from every qwen4_exp configuration, so it does not load the model files of this repository. A one-line change to oMLX keeps model_file when it is not the stock qwen4_exp.py.
• NanoSeedLM CUDA engine (nslm-qwen4-gen): NVIDIA GPUs (Ampere or newer). The routed experts stay in host memory, with a VRAM cache (--expert-cache-gb), or stream from the files on smaller machines (--stream-experts). Read Run and serve. The Metal engine does not run this model yet.

How to run

mlx-vlm

The command prints the prompt and generation speed in tokens per second.

python -m mlx_vlm.generate --model txgsync/Qwen3.8-Flash-Next-NanoSeedLM-P3 --trust-remote-code \
    --prompt "Why do tide pools matter?" --max-tokens 256

To read the PLE table from the SSD (128 GB Macs), make a folder view with mlx-vlm and run that folder:

from mlx_vlm.models.qwen4_exp.ple_storage import prepare_external_ple_model
prepare_external_ple_model("Qwen3.8-Flash-Next-NanoSeedLM-P3", "Qwen3.8-Flash-Next-NanoSeedLM-P3-ssd-ple")

NanoSeedLM CUDA engine

Build NanoSeedLM with CMake. On an RTX 4080 (16 GB), keep about 8 GB of the experts in VRAM:

build/bin/nslm-qwen4-gen --model Qwen3.8-Flash-Next-NanoSeedLM-P3 --expert-cache-gb 8 --prompt "Why do tide pools matter?"

Limits

• This is an experiment. Its output is not identical to the output of the original model.
• You must enable Trust Remote Code.
• On Apple Silicon, seed decode is slower than affine decode of the same size. On K2-Horizon-MoVA, the difference was 14% to 21%.
• We did not test image or video input.
• The model is about 98.2 GB. By default, macOS gives the GPU about three quarters of memory (96 GB of 128 GB). On a 128 GB Mac, keep the PLE table on the SSD (see How to run). We did not test this with this model yet.
• oMLX's MoE expert offload reads quantized experts. It may not apply to seed experts.

Files

• model-*.safetensors: the weights. NAME.seeds, NAME.nibbles, NAME.exp_bias for the routed experts.
• nanoseedlm_qwen4.py, nanoseedlm_seeds.py: the model files (MLX).
• config.json: the configuration of the 4-bit MLX model, with model_file.

Credits

Base model: Qwen, Qwen3.8-Flash-Next (Qwen Community License 1.0). MLX conversion: mlx-community/Qwen3.8-Flash-Next-4bit-mtp. Activation statistics: Unsloth imatrix. Method: SeedLM (arXiv:2410.10714). Code: NanoSeedLM.