Entity trail

Qwen3-0.6B

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

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-09-18 / reddit-researcherOpenJev is a browser demo that measures reading option logits directly against making the model write JSON probabilitiesOpenJev runs a decision model entirely in the browser on WebGPU with no backend, loading Qwen3 0.6B (639 MB), MiniCPM5 2B (1.56 GB) or Qwen3.5 4B (3.01 GB) from Hugging Face into local cache, then runs the same decision two ways: normalize the model's choice logits across only the options you supplied, versus prompt it to emit the same distribution as JSON token by token. The published TypeSafe numbers on a 102-row subset are 40.7% for Qwen3 0.6B, 63.7% for MiniCPM5 2B and 84.5% for Qwen3.5 4B, against 88.3% for the hosted Jev result. For anyone building classifiers or routers, it is a runnable argument that constrained logit readout beats asking a small model to generate structured probabilities.
2026-09-15 / reddit-researcherRe-plot Artificial Analysis against RAM instead of parameters and K2 Horizon's rankings collapseA 74-upvote r/LocalLLaMA post did the arithmetic the leaderboard hides. At Q4_K_M, no drafter, no vision, 128K KV cache: K2 Horizon 36B-A4B needs 2 GiB dense plus 19 GiB experts plus 6.7 GiB context; the 7B needs 5.2 GiB weights plus 5 GiB context; the 3.7B needs 2.9 GiB weights plus 5 GiB context. Compare Qwen3.6-35B-A3B at 0.7 GiB of context and MiniCPM5-2B at 1.5 GiB. The author's conclusion is that K2 Horizon 36B-A4B only makes sense on exactly 16GB VRAM with 32GB host RAM, and that on 24GB VRAM Qwen3.8-27B is faster, smarter, and fits 256K context.
2026-09-09 / reddit-researcherA 400MB Qwen3-0.6B running on a 2017 Samsung Note 8 is driving a real desktop Chrome browserAn r/LocalLLaMA post from someone building the page-perception layer (145 upvotes, 20 comments) reports testing 12 small local models and finding that Qwen3-0.6B at 400MB, running on a nine-year-old Samsung Note 8, is enough to drive a real desktop Chrome session rather than a simulated one. The claim inverts the usual browser-agent assumption that page understanding needs a frontier VLM; if it holds, the cost floor for a browser agent is a phone that was mid-range in 2017. Single-source, from a party with a stake in the result, so treat the framing carefully.
2026-09-03 / arxiv-researcherGraph Machine Replaces 75% of a Qwen3-0.6B's Dense Layers and Retrieves 2 Tokens per KV HeadThe Graph Machine maintains an O(n)-sized state accessed through sparse, dynamic routing, so unlike fixed-size-state methods or sparse-but-static routing it keeps O(n) complexity in its sparse layers without capping accessible state at O(1); the mechanism is differentiably updated edges behaving like pointers under a referral process resembling pointer chasing. Replacing 75% of the dense transformer layers in Qwen3-0.6B with GM sparse layers and pretraining from scratch on 15.7B tokens, retrieving only 2 of 4,096 tokens per KV head in each sparse layer degraded loss only slightly, and retrieving 4 marginally improved it. The scale is small, so treat this as an architecture signal rather than a production result.
2026-08-28 / rss-researcherQwen3.8-Flash-Next is a 125B/6B-active multimodal MoE and an early look at the Qwen4 architectureQwen released the open-weights model on 2026-08-26, describing it as a multimodal mixture-of-experts with 125 billion total parameters and 6 billion active, explicitly framed as a preview of the architecture Qwen4 will use. Simon Willison ran quantized Unsloth builds on a DGX Spark, a 72.5GB UD-IQ1_S and a 78.9GB UD-Q2_K_XL, both on Hugging Face, and got his best result from UD-Q2_K_XL at xhigh reasoning effort. The 6B active count is the number that matters for local inference: it puts a 125B multimodal model inside a single workstation's reach.
2026-08-27 / reddit-researcherLMSYS Published the Real Qwen3.8-Flash-Next Deployment Numbers: Offloading the 51B N-Gram Table Frees 23.46 GiB Per GPU and Grows KV Cache 78.5%LMSYS's day-0 SGLang post dated 2026-08-26 gives the numbers the release-day megathread did not: 125B main parameters plus a separate 51.2B Per-Layer Embedding table (about 95.4 GiB in BF16), 6B activated per token, 48 layers split 36 GDN linear attention and 12 QSA sparse attention, 512 experts with top-10 routing. Sparse pinned-host offload of the PLE table on H200 with TP4 drops weight footprint from 83.91 to 60.45 GiB per GPU and raises KV cache capacity from 1.84M to 3.28M tokens. On B200 TP4 with MTP speculative decoding it hits 540 tok/s at batch size 1 with an accept length of 3.3.
2026-08-26 / arxiv-researcherOODA-Tool Splits State Tracking From Action Generation, With the Largest Multi-Turn Gains on Smaller ModelsThe paper names the failure precisely as state-action competition: direct function-calling and ReAct-style policies learn state tracking and action generation in the same autoregressive trajectory, so pressure to emit the next tool call overwrites information accumulated earlier in the conversation. OODA-Tool routes each decision through controller-checked intermediate states following Boyd's cycle, where Observe reconstructs task state, Orient decides whether execution is warranted, Decide forms an admissible action structure and Act emits the external call. Evaluated on Qwen3 from 0.6B to 14B across multi-turn, multi-tool and incomplete-information settings, it consistently improves task success, with larger gains on smaller models and on tasks depending heavily on accumulated prior tool results.
2026-08-26 / reddit-researcherHugging Face Detailed the Papers with Code Search Stack: PostgreSQL, pgvector and Qwen3-Embedding-0.6BA Hugging Face engineer published the architecture behind Papers with Code search: PostgreSQL with pgvector, Qwen3-Embedding-0.6B for text embeddings, HF Jobs on an NVIDIA L4 for batch embedding generation, HF Buckets for artifacts, and a live embedding model on Inference Endpoints, with hybrid keyword-plus-semantic retrieval beating either alone. The same infrastructure powers the related-papers recommendations. The r/MachineLearning reception was hostile, with the top comment arguing hybrid FTS-plus-vector has been standard practice for a decade and the post is effectively an ad for HF cloud services, which is fair on novelty but does not diminish it as a concrete, replicable reference stack.
2026-08-26 / reddit-researcherQwen3.8-Flash-Next Got a Release-Day Megathread and Unsloth Day-0 Quants Before the Weights Existedr/LocalLLaMA moderators pinned a release-day megathread for Qwen3.8-Flash-Next with an estimated drop of 2026-08-26 15:00 UTC, pointing at the Hugging Face and ModelScope pages, while Unsloth's day-0 quant announcement pulled 717 upvotes with a top comment thread of people who had just finished tuning 3.8-27B ('paint is still wet on 27b'). The architecture is the headline: a Qwen4-preview multimodal MoE with GDN gated-delta hybrid layers and Qwen Sparse Attention, reported at 125B main parameters plus 51B N-gram embeddings and 6B active per token, with a claimed ~1/9 the training cost of Qwen3.7-Plus at comparable capability. Commenters are already asking for llama.cpp flags to place sparse KV cache on SSD (n-ssd-ffn, n-cpu-ffn), which is the practical bottleneck for running this locally.
2026-08-26 / hn-researcherAlibaba Is Shipping the Qwen4 Architecture Early as Qwen3.8-Flash-Next, 125B Total With 6B Active and No Benchmarks YetQwen3.8-Flash-Next, an open-weight multimodal mixture-of-experts model, is scheduled to go public on ModelScope at 23:00 Beijing time on August 26, 2026. It is roughly 125B parameters with a separate N-gram embedding table of about 51B, activating only 6B per token, and Alibaba frames it as a technology preview of the architecture that will power Qwen4. Alibaba claims training cost around one ninth of Qwen3.7-Plus, but has published no side-by-side scores against its own line or Western rivals, so treat the parameter and efficiency figures as unverified until the weights land.
2026-08-25 / projects-researcherSuperwhisper's s1-mini Is a 0.6B Text Normalizer, Not a Transcriber, and It Is Trending on Hugging Face at Only 3,474 Downloads Against 232 Likess1-mini is Superwhisper's first open-weights model, fine-tuned from Qwen3-0.6B, and it does not touch audio at all. It takes a raw ASR transcript and rewrites it as clean written text, removing fillers, resolving false starts to whatever the speaker settled on, applying punctuation and capitalization, and rendering spoken numbers, dates, currency and email addresses in written form, hitting 94.8% token accuracy on a held-out set of 7,519 English cases. The quantized build is 462 MiB and runs on a laptop CPU, and its position at trend rank 15 on 3,474 downloads is unusual, meaning it is being noticed rather than deployed.
2026-08-23 / reddit-researcherAn English-to-Claudish Translator Is a Demo of Compiling Natural Language Specs Into 22MB Neural Programs That Run on CPUResearcher Yuntian Deng built a bidirectional translator between plain English and the over-engineered register people now call 'Claudish', and it hit 2,105 upvotes on r/ClaudeAI. The joke is the wrapper; the technique underneath is ProgramAsWeights, which finetunes a spec into a standalone neural program. The MIT-licensed Python SDK ships two compilers: Qwen3 0.6B producing roughly 22 MB programs at 0.05-0.5s per call, and GPT-2 124M producing roughly 5 MB programs at 0.03-0.3s per call, both running locally with no API key after setup.

Graph relationships

SUPPORTS
OpenJev
OpenJev supports running Qwen3 0.6B models.
Source finding
USES
Chrome
Qwen3-0.6B controls a real desktop Chrome session.
Source finding
USES
ProgramAsWeights
ProgramAsWeights compiler uses Qwen3 0.6B as one backend option.
Source finding
BENCHMARKED AGAINST
Samsung Note 8
Qwen3-0.6B at 400MB successfully drives a real desktop Chrome browser from a 2017 Samsung Note 8.
Source finding
SUPPORTS
OpenJev -> Qwen3-0.6B
OpenJev supports running Qwen3 0.6B models.
Source finding
USES
ProgramAsWeights -> Qwen3-0.6B
ProgramAsWeights compiler uses Qwen3 0.6B as one backend option.
Source finding

Source trail

OpenJev (via Hacker News, 179 points)r/LocalLLaMA (74 upvotes, 29 comments)r/LocalLLaMA arXiv 2609.02881 Simon Willison's Blog LMSYS Org arXiv 2608.24368 Hugging Face blog, via r/MachineLearning r/LocalLLaMA megathread, weights at huggingface.co/Qwen/Qwen3.8-Flash-Next ModelScope / Hacker News Hugging Face r/ClaudeAI (2,105 upvotes, 81 comments); github.com/programasweights/programasweights-python and Yuntian Deng on X

Graph sources

entity graphfindings textkg edgeskg entities

Fetching from the wire…