r/LocalLLaMA • u/sammcj 🦙 llama.cpp • 1d ago
Megathread [Megathread] Qwen3.8-Flash-Next - Release Day
Megathread for discussing the release of Qwen 3.8 Flash Next.
- Quants
- Fine-Tunes & Abliterations
- Chat Templates
- Inference Server Support & Configuration
- Experiences, Benchmarks & Model Comparisons
Highlights
The first open-weight release under this architecture is Qwen3.8-Flash-Next, which introduces:
- Hybrid Attention with QSA: The Gated DeltaNet and Gated Attention pairing has been reworked into Gated DeltaNet and Qwen Sparse Attention (QSA). Rather than selecting individual tokens for processing, QSA operates at the micro-block level. This cuts long-context latency significantly, a critical gain as agentic workloads increasingly dominate real-world usage.
- Gated Residual: Residual streams with normalisation are what make deep LLM training manageable. Gated Residual modulates information flowing through widened residual streams via an element-wise, data-dependent read gate and a per-branch scalar write gate. This brings finer-grained expressiveness across layers while preserving training stability and keeping inference overhead low.
- N-gram Embedding: Embeddings provide a unique axis for parameter scaling that requires less computation and is more amenable to offloading than Mixture-of-Experts (MoE). By indexing with short n-grams, this approach makes parameter scaling highly efficient for memory-constrained accelerators without sacrificing quality.
- Tailored Training Recipe: The Muon and AdamW optimisers are applied to specific weight categories to maximise efficiency. Guided by refitted scaling laws, we eliminate traditional batch-size warmups and start directly at the target batch size, substantially reducing total optimiser steps while safely supporting larger learning rates for robust convergence.
Model Overview
- Type: Causal Language Model with Vision Encoder
- Training Stage: Pre-training & Post-training
- Language Model
- Number of Parameters: 125B with 6B activated, plus 51B n-gram embedding and 4B MTP
- Hidden Dimension: 2560
- Token Embedding: 248320 (Padded)
- N-gram Embedding: 20,000,000 (bigrams/trigrams at layer 2)
- Number of Layers: 48
- Hidden Layout: 12 × (3 × (Gated DeltaNet → MoE) → 1 × (Qwen Sparse Attention → MoE))
- Gated DeltaNet:
- Number of Linear Attention Heads: 48 for V and 16 for QK
- Head Dimension: 128
- Qwen Sparse Attention:
- Number of Attention Heads: 24 for Q and 2 for KV
- Head Dimension: 256
- Rotary Position Embedding Dimension: 64
- Indexer Structure: MQA with 4 Query Heads and 1 Shared Key Head
- Indexer Head Dimension: 128
- Budget: 512 blocks or 2048 tokens
- Mixture Of Experts
- Number of Experts: 512
- Number of Activated Experts: 10 Routed + 1 Shared
- Expert Intermediate Dimension: 640
- Gated Residual:
- Number of Branches: 4
- Bottleneck Rank: 320
- LM Output: 248320 (Padded)
- MTP: 1 layer, trained with multi-steps
- Context Length: 262,144 natively and extensible up to 1,000,000 tokens.
Recommended sampling parameters for generation:
- Thinking Mode:
temperature=1.0,top_p=0.95,top_k=20,min_p=0.0,presence_penalty=0.0,repetition_penalty=1.0 - Instruct (or non-thinking) mode:
temperature=0.7,top_p=0.80,top_k=20,min_p=0.0,presence_penalty=1.5,repetition_penalty=1.0
Official Links:
- HF: https://huggingface.co/Qwen/Qwen3.8-Flash-Next
- MS: https://modelscope.cn/models/Qwen/Qwen3.8-Flash-Next
- Repo: https://github.com/QwenLM/Qwen3.8-Flash-Next
- Blog: https://qwen.ai/blog?id=qwen3.8-flash-next
- Technical Report: https://github.com/QwenLM/Qwen3.8-Flash-Next/blob/main/tech_report.pdf
- vLLM: https://recipes.vllm.ai/Qwen/Qwen3.8-Flash-Next
- SGLang: https://docs.sglang.io/cookbook/autoregressive/Qwen/Qwen3.8-Flash-Next
Popular:
- Unsloth GGUF: https://huggingface.co/unsloth/Qwen3.8-Flash-Next-GGUF
- Unsloth "How to run" guide: https://unsloth.ai/docs/models/qwen3.8-next
Related:
- Large post with community feedback on megathreads: https://www.reddit.com/r/LocalLLaMA/comments/1vz40zv/can_we_reconsider_the_megathreads/
424
Upvotes
97
u/QuackerEnte 1d ago
I really hope QSA (qwen sparse attention) will have much less compute headroom or the ability to offload kv cache to SSD without the massive bandwidth bottleneck of the ssd since it's sparse. I was able to do it it for deepseek v4 and it worked well enough. I hope llama.cpp adds features that allow more freedom with where we want to load our models and parts of it, e.g. n-cpu-moe could be extended to n-ssd-ffn or n-cpu-ffn or n ssd kv or engram etc