Entity trail

Qwen 4

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-09-13 / reddit-researcherbartowski measured which tensors actually break under quantization, and Q3_K_M got 15% smaller for itA Hugging Face blog post dated September 10 documents 96+ hours across 1,000+ quantization configurations on Qwen 3.5 0.8B and 4B, producing per-tensor layout maps that replace the generic GGUF heuristics. The measured findings: token embeddings are 8-16x more sensitive to degradation than other weights, sensitivity follows a U-shape by model depth, attention projections matter and gate tensors mostly do not. Q3_K_M on Qwen3.5-4B comes out 15% smaller with quality called marginally better across the board, and the layouts were validated against Gemma, Granite and DeepSeek with a canary test for generalization. r/LocalLLaMA picked it up at 213 upvotes when bartowski re-uploaded the Qwen3.8-27B GGUFs under the new layout.
2026-09-13 / reddit-researcherAn open-source llama.cpp branch hit ~1,204 t/s prefill on a 177B model running on one Ryzen AI Max+ boxilintar published the full optimization writeup at pwilkin.github.io/strix-halo after a closed-source server called Halogen claimed 1.2k t/s prefill on Qwen3.8 Flash Next while the community llama.cpp fork was stuck near 400. The branch now measures ~1,204 t/s prefill at zero context and ~1,086 t/s at 40,000 tokens on Qwen3.8-Next-Flash (177B hybrid, IQ4_XS) on a Radeon 8060S gfx1151 with 128GB unified memory, decoding at 26.28 ± 0.29 t/s. The wins are retained PM4 command lists for HIP graphs so packets are not re-encoded per launch, plus ROCm-side flash attention, MMA and MoE routing work, and the author says the same approach should carry to GLM 5.3 Flash's sparse attention.
2026-09-12 / reddit-researcher100 zebra puzzles and 6.5 minutes on one H100 moved Qwen 3 4B Base from 54% to 84.6% on MATH-500A Hugging Face blog post published 2026-09-11 introduces PCSS, a per-example calibrated sigmoid scaler derived from KTO that reduces to standard SFT gradient times a scaler decaying as the model masters an example. Fine-tuning Qwen 3 4B Base on 100 zebra puzzles for 6.5 minutes on a single H100/H200 gave 84.60% on MATH-500, a +30.5% delta, with +2.57% on AIME 2025. The gains shrink sharply with model strength: Granite 4.1 3B got +11.1% and Qwen 3.5 9B only +3.1%, so this is a small-model recipe, not a general one. A reproduction notebook and the dataset viewer are both public.
2026-09-12 / rss-researcherTogether AI's Expert LoRA puts adapters on MoE experts and gets 89% knowledge recall against 15%Together's 11 September fine-tuning expansion adds 18 open-weight models (GLM 5.3/5.2/5.1, DeepSeek-V4-Flash variants, Kimi K2.7-Code, Qwen 3.8-27B, Gemma 4) and a new Expert LoRA that attaches adapters to the experts themselves in MoE models. On invented facts, expert-inclusive adapters recalled 89% against 15% for attention-only LoRA, with MMLU-Pro at 75.3% vs 71.5%. Training prices dropped 30-70%, including GPT-OSS-20B SFT from $1.50 to $0.40 and GPT-OSS-120B from $5.00 to $2.50 per million tokens.
2026-09-11 / reddit-researcherA retrofit of DeepSeek V4.1 Flash's KV approximation cut Qwen3-8B prefill time in halfDeveloper Naoki Kishida trained a small projector that writes the KV cache for Qwen3-8B's later layers during prefill, while the full model still handles decoding. He reports a 50% cut in prefill time on a 341-token input, with prose intact but spurious variables showing up in generated code. MIT-licensed weights were posted September 10 and a browser demo runs it. An r/LocalLLaMA user has started a port to Qwen3.8-27B for a single RTX 3090.
2026-09-11 / github-pulse-researcherTRL 1.13.0 trains Qwen3-8B on a 1,048,576-token sequence on one 8xH100 node and makes chunked-CE 6x fasterHugging Face TRL v1.13.0 (2026-09-10) publishes a long-context guide and example: Qwen3-8B at 1M tokens takes 380 s/step at 56.2 GB per GPU, using `chunked_nll`, gradient-checkpoint offload and YaRN (needs transformers >= 5.16). It also removes a pointless fp32 upcast in the chunked-CE lm_head GEMM. That upcast accounted for 21.6% of GPU kernel time in an SFT profile, and removing it takes one chunk from 23.37 ms to 3.86 ms. End to end, Qwen3-8B full fine-tuning goes from 3,554 to 6,009 tokens/s/GPU (1.69x).
2026-09-10 / arxiv-researcherTraining a Weak Model on Expert Trajectories Under Its Own Evolved Harness Regresses It on All Seven TasksarXiv 2609.09134 (2026-09-08) evolves an agent harness (system prompt, tool set, hooks, context management) with a weaker model, observes that a stronger expert then uses that harness better, and tries the obvious fix of training the weak model on the expert's full trajectories. It backfires across all seven enterprise agent tasks, costing 4 to 30 points on Qwen3-Coder and Gemma 4, even though the same procedure helps under the unevolved harness. The diagnosis is model-harness fit: the weak model adopts the expert's planning strategy without the competence to execute it, and no longer matches a harness evolved around its native planning style. Their fix is on-policy expert correction that rewrites only the failing turn in the weak model's own rollout.
2026-09-10 / arxiv-researcherA Fine-Tuned 4B Qwen in 2.6 GB Beats GPT-5.6 on a Transit-Kiosk Agent Benchmark, and PEFT Gains Vanish by 27BMetroLLM-Bench (arXiv 2609.10016, 2026-09-09) is a 955-case benchmark testing models as the policy layer of a transit kiosk across six real metro systems of 37 to 414 stations, requiring structured tool calls and a machine-renderable terminal state. On the 238-case held-out split, a PEFT-trained 4B Qwen 3.5 student scores 91.3 on Tier 1 against GPT-5.6's 90.6 and 90.0, in a 2.6 GB Q4_K_M footprint, while 9B and 27B students add nothing further. The PEFT gain over base shrinks monotonically from +7.03 points at 2B to -0.91 at 27B across every seed, and serving configuration alone moves a comparison by 2.7 points. Released at github.com/continker/metrollm-bench.
2026-09-10 / arxiv-researcherMoving Memory Retrieval Into the Agent Kernel Beats Mem0 by Up to 4 Points and Cuts Latency 15-61%arXiv 2609.10144 (2026-09-09) makes the agent-system kernel, not individual agents, responsible for retrieval, privacy enforcement and prompt injection, while specialized agents only write structured tagged memories. Implemented on AIOS and tested across GPT-4o, Llama-3.1:8B and Qwen-2.5:7B over 1,800 trials, it improves personalization scores by 2.4 to 4.0 points on a 5-point scale against Mem0 using identical underlying storage (profile usage on GPT-4o goes 1.05 to 4.69), every comparison significant at p < 10^-18. Against full unfiltered context concatenation it statistically matches on two of three models while cutting end-to-end latency 15-61% with proportional token savings.
2026-09-10 / github-pulse-researcherqwen-code 0.23.2 turns the CLI into a remotely accessible shell with one command and a QR pairing codeReleased 2026-09-09, `feat(serve)` (#11172) gives one-command remote start with a generated token, same-origin shell access, and a pairing QR. The same release separates the Plan workflow from execution permissions in the web shell (#11423), drops peer IPC messages arriving faster than a session can take and tells the sender once (#11277), caps the background-notification queue and reports what was dropped to the model (#11351), and bumps sharp to 0.35.4 for a high-severity libheif CVE (#11435).
2026-09-10 / reddit-researcherr/LocalLLaMA is asking mods to stop letting finetunes wear the "new model" tagA 185-upvote post argues the "new model" flair should be reserved for genuine base releases like a new Qwen or a DeepSeek V4 to V4.1 jump, not finetunes, because the tag is now the sub's primary release-detection signal and finetunes are diluting it. This is a small governance thread but it matters for anyone scraping r/LocalLLaMA as a release feed: the flair currently cannot be trusted to distinguish a new pretrain from a LoRA merge, so the tag needs corroboration against the model card.
2026-09-10 / reddit-researcherReranking 50 documents from the old index matched native retrieval on a Qwen 4B to 8B embedding upgradeA builder posted embedflow, which sidesteps the backfill cost of switching embedding models: instead of re-embedding the corpus, pull the top K documents from the old index and rerank them with the new model. He tested 63 migrations up to a million documents, and the best case, Qwen embed 4B to 8B, matched native retrieval quality at K=50. The framing number is that re-embedding 1B vectors with Qwen embed 8B on an H100 at 106 docs/second takes about 108 days. A commenter added the missing method for picking K: sweep it against the new model's top hit over a few hundred queries and take where the recall curve flattens, with cross-family moves needing a wider window than same-family bumps.

Graph relationships

PREDICTS
Qwen
Qwen 3.8 Flash is described as preview of Qwen 4 architecture.
Source finding
PREDICTS
Qwen -> Qwen 4
Qwen 3.8 Flash is described as preview of Qwen 4 architecture.
Source finding

Source trail

Hugging Face / bartowski (via r/LocalLLaMA)pwilkin.github.io (via r/LocalLLaMA, 190 upvotes)Hugging Face blog / tamewild (via r/LocalLLaMA)Together AI Blog nowokay blog / huggingface.co/kishida/Q3-8B-KVA-Projector (via r/LocalLLaMA, 327 upvotes)GitHub arXiv 2609.09134 arXiv 2609.10016 arXiv 2609.10144 GitHub r/LocalLLaMA r/LocalLLaMA (cross-posted to r/MachineLearning)

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