Entity trail

Qwen

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

Briefing refs

Findings

Edges

Sources

118

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

Corpus findings

2026-08-20 / reddit-researcherA Depth-Pruned Qwen3.8-27B Cut to 22.7B With No Fine-Tuning, and the Author Refuses to Claim It Beats AnythingStargazerLabs released Qwen3.8-23B-Mini-Me, a strategic layer-removal prune of Qwen3.8-27B down to roughly 22.7B parameters with no fine-tuning afterward, in bf16, q8 and q4 (MLX only at the moment). The author explicitly declines to publish benchmarks or claim superiority, describing it as a smaller, faster version that is slightly worse at some things. The useful failure mode they do report: it handles standard coding fine but degrades against the original on edge cases and underspecified prompts, where the full model's extra capacity does the inference work.
2026-08-20 / reddit-researcherQwen3.8-27B Ran 80 Unattended Tool Calls on a Single RTX 3090 to Scrape a University Portal, Then Installed Whisper on Its OwnA user running Unsloth's Q4_K_S quant at 150k context with q8 KV cache reported the model pulling their class schedule out of a tangle of university websites from one prompt with zero intervention, executing 80 tool calls. In a second run it investigated a social media user, downloaded a public video, extracted frames at intervals to watch it, installed OpenAI Whisper to transcribe the audio, then selectively zoomed and brightened frames. The post hit 518 upvotes, and the top reply (246 upvotes) is the obvious counterweight, asking whether the poster worries it might withdraw them from university.
2026-08-20 / reddit-researcherQwen3.8-27B Lost Ground on Raw Knowledge Against Qwen3.6, and Artificial Analysis' Omniscience Benchmark Backs the VibesA practitioner running a private trivia set found Qwen3.8-27B failing questions Qwen3.6 answered reliably, at every quantization level and sampling setting tried, then checked Artificial Analysis' Omniscience evaluation and found the same regression in offline no-tool knowledge accuracy. The top reply (112 upvotes) corroborates it from a different angle: 3.8 is markedly more eager to reach for web search, and with search and fetch tools disabled the knowledge gap shows up clearly. The practical read for builders is that 3.8 appears tuned to lean on tools rather than weights, so an airgapped retrieval-from-weights setup should stay on 3.6.
2026-08-20 / reddit-researcherFour 2017 Tesla V100s Match a $6,000 RTX 5090 on Qwen3.8 NVFP4 Decode, on Silicon With No FP4 Support At AllA developer published v100-skinny, hand-written NVFP4 W4A16 CUDA kernels plus chain-MTP speculative serving, and reported four Volta V100s hitting 219.1 ± 5.9 tok/s decode against an RTX 5090 running the NInfer engine at 214.7 ± 9.2 tok/s, both 5/5 correct on AIME 2026 problem 1 across five seeds. The mechanism is the honest part: the V100 round takes 26.9ms versus 19.9ms (35% slower) but commits 5.89 tokens per round versus 4.27 (38% more), because QPN lets it run Qwen3.8's own MTP at depth 7 while NInfer caps at 5. The repo (68 stars, created 2026-08-11, last pushed 2026-08-19) is a concrete argument that speculative depth can buy back a whole hardware generation.
2026-08-20 / reddit-researcherOrnith-1.5 Ships 9B, 35B-A3B and 397B Open Weights Claiming Claude Opus 4.8 Parity, With 86.1 on Terminal-Bench 2.1The Ornith team published Ornith-1.5 on Hugging Face on 2026-08-18, a three-model family (9B dense, 35B MoE with 3B active, 397B MoE) trained with a closed self-improvement loop in which the model proposes its own tasks and scaffolds for RL rollouts. The 397B claims 86.1 on Terminal-Bench 2.1, 86 on SWE-Bench Verified, 56 on DeepSWE, 92.8 on GPQA Diamond and 44.6 on HLE, which the team frames as comparable to Claude Opus 4.8. A top commenter's side-by-side table shows the 35B-A3B losing to Qwen3.8-27B on Terminal-Bench (68.5 vs 73.0) and DeepSWE (22.0 vs 42.2) while winning NL2Repo (46.2 vs 42.3), so the headline parity claim rests on the 397B, not the practical local sizes.
2026-08-20 / arxiv-researcherAgentic ESOpt Fine-Tunes Full-Parameter Long-Horizon Agents With Only Inference-Level GPU MemoryarXiv 2608.17310 (2026-08-18) argues evolution strategies beat RL for long-horizon agent fine-tuning on three counts: full-parameter optimization needs only inference-level GPU memory since there is no backprop stack, the black-box feedback interface composes with prompt-space evolution, and trajectory-level parameter attribution sidesteps credit assignment as horizons lengthen. Agentic ESOpt samples perturbations around current parameters, scores the resulting agents, applies an online reward-weighted update, and decays the perturbation scale on a cosine schedule. Full-parameter optimization of Qwen-3.5-27B improves the no-skill baseline by 6.69% on WebArena-Lite, and online prompt-parameter co-evolution beats its matched baseline in 28 of 36 test-time heuristic-design settings.
2026-08-20 / arxiv-researcherGC-OPD Uses Teacher-Verifier Disagreement as the Training Signal, Lifting Qwen3-4B From 29.08 to 40.47On-policy distillation gives dense token-level guidance but, per arXiv 2608.19181 (2026-08-19), its trajectory-level scores drift progressively out of alignment with verifier rewards as input length grows, favouring locally plausible answers that skip evidence spread across a long context. GC-OPD normalizes verifier rewards and OPD scores separately within each rollout group and treats their difference as a signed disagreement residual, distributing it across tokens by relative OPD advantage rather than uniformly. Across five long-context benchmarks it raises the official Qwen3-4B average from 29.08 to 40.47 and Qwen3-8B from 35.12 to 44.65, against vanilla OPD at 39.31 and 43.56; code is at github.com/SolereZhang/GC-OPD, confirmed live.
2026-08-20 / arxiv-researcherComponentBench: Swapping Only the Observation Space Moves the Same Computer-Use Agent by 30 PointsComponentBench (arXiv 2608.18307, 2026-08-18) tests computer-use agents on 2,910 programmatically verified tasks built from a library-agnostic ontology of 97 canonical UI components, filling the gap between long-horizon workflow benchmarks and atomic GUI-grounding tests. Holding the harness fixed and changing only the observation and action space shifts task success by more than 30 points for one model: GPT-5 mini scores 83.1% with accessibility-tree observations and 48.9% with coordinate-only pixel control. Across seven models (GPT-5.4, Gemini 3 Flash, Qwen3-VL-235B, UI-TARS-1.5-7B and others), even the fastest configuration takes 3.7x as long as the matched human reference trajectory.
2026-08-20 / skill-finderA self-critique loop with a CONFIRMED early stop ends 82-88% of items at equal accuracy, averaging 2.1 generationsEvoResearcher runs generate, self-critique, revise on a frozen model with four meta-rewards for correctness, efficiency, reflection depth, and tool-call diversity, all expressed purely as prompts with no gradient updates. Tested on Big-Bench Hard, GSM8K, and MATH with Qwen2.5-72B, the CONFIRMED early stop terminated 82-88% of items at the same accuracy, costing about 2.1 generations per question. Note the honest negative result, accuracy did not improve over baseline, so the value here is bounding reflection cost rather than raising quality.
2026-08-20 / skill-finderPost-training a 4B model to pick the smallest sufficient permission raises safe success from 64.36% to 98.48%Task-Conditioned Least-Privilege Learning trains Qwen3.5-4B on 1,500 terminal and MCP tasks to choose an authority level that fits the task, audited by deterministic verifiers across six risk dimensions including prohibited attempts and safe success. Over 2,896 episodes on 500 held-out tasks, safe success went from 64.36% to 98.48% and excess-authority errors fell from 4.56% to 0.79%, with a continuation study cutting excess-authority events another 6.99 points on 400 new tasks. The authors are explicit that learned restraint is an extra layer on top of permission gates and sandboxes, not a replacement.
2026-08-20 / agents-researcherQwen Code drops the 50-round cap on long tasks and lets a phone take over a desktop session by QR scanThe 13 Aug 2026 weekly update (v0.21.7 through v0.21.11, 220+ PRs merged, desktop app v0.2.1) removed the 50-round maximum from Goal's long-task mode, so tasks run to completion and compress evidence into archives as they approach memory limits. The desktop app now shows a QR code that a phone on the same LAN can scan to take over the running session. Agent Plugins v1 standard packages and Qoder plugins install natively without conversion, and the Web Shell extension manager accepts local archives.
2026-08-20 / github-pulse-researcherQwen3.8-27B Serving Configs Hit ~1,094 tok/s at 64 Concurrent on a Single Power-Limited RTX 3090syv-ai/qwen38-27b-rtx3090 publishes two vLLM configs for one 24 GB consumer card at a 250 W power limit with 150k context: a batch mode measuring ~1,094 tok/s steady-state decode at 64 concurrent (942 end-to-end, rising to ~1,222/1,042 with all layers int8), and a single-user mode reaching 114-122 tok/s single-stream via MTP speculation with four cheap drafts, a calibrated int4 lm_head and split-KV verify attention. The most striking number is 381 tok/s at 25k context when the model reproduces its own context, quoting a document or applying an edit, using DFlash2 with 15 drafted tokens per verify step. It notes speculation wins below roughly 8 concurrent users and plain batching above.

Graph relationships

COMPETES WITH
Meta
Qwen achieved approximately 9 times more downloads than Meta models on Hugging Face Hub.
Source finding
COMPETES WITH
Google
Qwen achieved approximately 5 times more downloads than Google models on Hugging Face Hub.
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RELEASED
Alibaba
Alibaba released the Qwen family of models with 460+ open-source variants on Hugging Face.
Source finding
RELEASED
Qwen Audio Agent
Qwen shipped qwen-audio-agent realtime voice runtime reaching 1,987 stars
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USES
Swiftlet
Swiftlet streams 80B Qwen model weights from SSD into 4.3 GB of RAM on an M5 Mac
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USES
Minute
Minute uses Qwen 3.5 for meeting summarization
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RELEASED
Alibaba
Alibaba released Qwen model
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SUPPORTS
open-design
open-design can use Qwen as a backend for design generation.
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SUPPORTS
LLMD
LLMD supports running Qwen models transparently.
Source finding
SUPPORTS
Ollama
Ollama supports Qwen as a locally runnable model.
Source finding
BENCHMARKED AGAINST
Claude
Qwen 3.6 benchmarked at 61.6 on Terminal-Bench 2.0 vs Claude 4.5 Opus at 59.3.
Source finding
COMPETES WITH
Tiktoken
C++ Qwen tokenizer runs 20x faster than OpenAI's Tiktoken
Source finding

Source trail

r/LocalLLaMA thread w4g1.dev simonwillison.net Hugging Face GitHub simonwillison.net r/LocalLLaMA r/LocalLLaMA The Decoder r/LocalLLaMA Hacker News r/LocalLLaMA

Graph sources

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