Model Detail
Qwen3.8-Flash-Next-oQ4e-mtp
—Qwen3.8-Flash-Next-oQ4e-mtp is a multimodal model with 90.0B parameters released by Jundot. The model is registered under the image-text-to-text pipeline tag on Hugging Face, distributed under a other license.
Qwen3.8-Flash-Next-oQ4e-mtp ships with 90.0B parameters, distributed as a quantized weight variant for lower-VRAM inference. Total weight footprint is approximately 180.0 GB, which is the relevant figure when planning local-inference VRAM. Distribution is governed by the other license — review the exact terms before commercial deployment.
Downloads of Qwen3.8-Flash-Next-oQ4e-mtp have moved +107.0% over the trailing seven days. That puts the model in active uptrend territory; a sustained move of this size usually reflects a recent release, a viral integration, or a benchmark surprise rather than steady-state demand. These numbers are signal, not guarantee — week-over-week download counts on Hugging Face also reflect mirror traffic, CI scrapes, and one-off benchmarking runs.
Qwen3.8-Flash-Next-oQ4e-mtp is best fit for mixed text-and-image reasoning tasks such as document understanding. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.
Privacy-preserving inference on hosted models: a learned input-embedding transform for frozen Qwen3.8-27B
arXiv:2610.27817v1 Announce Type: new Abstract: Every prompt sent to a hosted language model is readable by whoever runs it. We study whether the prompt can remain on the client while the host receives transformed vectors that stand in for it. A language model converts text to vectors exactly once,
Qwen3.8-Omni: Towards Native Omni-Modal Agents
arXiv:2609.25611v1 Announce Type: new Abstract: We introduce Qwen3.8-Omni-Flash, a natively multimodal agentic model for real-world multimodal productivity. Compared with previous omni models, which primarily emphasized perception and interaction, Qwen3.8-Omni-Flash substantially improves multimodal
On the Design of Qwen3.8-Next Architecture: Evaluation, Efficiency, and Training Stability
arXiv:2608.30320v1 Announce Type: new Abstract: We describe the architecture and ablations of Qwen3.8-Flash-Next, a sparse mixture-of-experts model with 125B parameters, 6B activated per token, and additional 51B parameters of n-gram embedding tables held off the accelerator. On fourteen pre-trainin
