Entity trail

Gemma

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

Briefing refs

Findings

Edges

Sources

107

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

Corpus findings

2026-08-23 / reddit-researcherA 16GB VRAM Fine-Tune of Gemma 4 12B Claims 2.7x Better Tool Calling and 15.7% More Tool Calls AttemptedA builder fine-tuned Gemma 4 12B specifically for tool use and CLI work because nothing larger fits comfortably in 16 GB of VRAM, reporting a 2.7x improvement on tool calling plus a 15.7% rise in the number of tool calls the model tries to emit, which they read as less time lost in reasoning. Weights are published fp16 through Q4_K_M for llama.cpp and Ollama at huggingface.co/TheOneWhoWill/Coding-Monkey-Gemma-GGUF. Tested attached to GitHub Copilot, it correctly sequenced npm installs after create-next-app; the author notes Q6 and above are noticeably better if you have the VRAM.
2026-08-22 / arxiv-researcherGoogle's Gemma Open Models Pass One Billion Downloads With Over 100,000 Community VariantsGoogle DeepMind announced on 2026-08-20 that the Gemma family has passed one billion cumulative downloads, with outside developers publishing more than 100,000 variants on the open weights over roughly two years, an ecosystem Google calls the Gemmaverse. The figures were disclosed by DeepMind VP Clement Farabet and product director Olivier Lacombe, alongside a new curated Awesome Gemma GitHub repository serving as the official directory of community fine-tunes and tooling. Cited deployments include NASA, Satlyt and Starcloud running Gemma in orbit for onboard image analysis and intersatellite routing, and India's National Health Authority integrating Gemma 4 into Aarogya Setu 2.0.
2026-08-22 / reddit-researcherMeasured Across 9 Models: Telling an LLM to Be Concise Saves ~1.5x, Shortening Your Prompt Costs Up to 96% MoreResearchers posting to r/MachineLearning tested output compression against input compression at five reduction levels on five short-answer datasets, across GPT-4o, GPT-5.4, Claude Haiku 4.5, Claude Sonnet 4.6, Qwen2.5-VL-7B, Qwen3.5-9B, DeepSeek-R1-Distill, Gemma-4-E4B and Kimi-K2.6. Instructing shorter output averaged 1.5x cheaper and up to 3x in the best case with accuracy roughly flat, and the effect held across an eleven-language run including Swahili, Bengali, Thai and Telugu. Trimming the input prompt did the reverse, costing up to 96% more on the worst benchmark because the model compensates with longer answers. The authors surfaced the paper specifically because Claude Code had shipped a concise output style the day before.
2026-08-20 / hn-researcherOrnith-1.5 Closes the Loop: the Model Writes Its Own Tasks and Scaffolds, and a 397B MoE Hits 86.1 on Terminal-Bench 2.1Ornith released 1.5 in August 2026, extending self-scaffolding into full end-to-end self-improvement by jointly optimizing task generation, scaffold construction and solution rollouts with GRPO instead of training on human-curated task sets. The 397B MoE scores 86.1 on Terminal-Bench 2.1 and 56.0 on DeepSWE, which the writeup puts level with Claude Opus 4.8; a 35B MoE activating 3B params per token beats dense Gemma 4-31B, and a 9B dense variant still reaches 47.0 on Terminal-Bench 2.1 on-device. Built on Qwen3.5 and Gemma 4 bases, weights on Hugging Face; 199 points and 69 comments on HN.
2026-08-18 / github-pulse-researcherUnsloth Pivots From Fine-Tuning Library to Local Desktop App for Running and Training Modelsunslothai/unsloth (73,462 stars, Apache-2.0, pushed 2026-08-18, +739 stars on the day) has repositioned its repo description from a fine-tuning library to 'Local UI to run and train LLMs and diffusion models.' The shift is visible in the release tags: v0.1.701-beta on 2026-08-11 introduced Unsloth Desktop, followed by v0.1.702-beta on 08-13 and v0.1.800-beta on 08-14 adding Qwen3.8-27B. Advertised coverage now spans Qwen3.8, Kimi K3, MiniMax-H3, Gemma 4, DeepSeek-V4, and FLUX in one app. For builders, a training-focused project shipping a consumer desktop surface is the clearest signal yet that local fine-tuning is being packaged for people who won't open a notebook.
2026-08-18 / sources-researcherMatthew Berman's Aug 17 Trending Roundup Is Five-Sixths Agent-Skill Infrastructure — Including Block's 28k-Star 'Hive Mind' Rust PlatformThe 2026-08-17 video (retitled from the feed's original) covers Unsloth (73,462 stars, now a local UI for running and training Qwen3.8, Kimi K3, MiniMax-H3, Gemma 4, DeepSeek-V4 and FLUX), kepano/obsidian-skills (46,629 stars, MIT — agent skills for Obsidian CLI and open formats), block/buzz (28,246 stars, Rust, Apache-2.0, described only as "a hive mind communication platform"), diagram-design, ego-lite, and lightningpixel/modly (6,563 stars, TypeScript — desktop image/prompt-to-3D running entirely on local GPU). All six star counts and push dates were verified against the GitHub API; five of the six are agent-adjacent, and Unsloth's repositioning from a fine-tuning library to a local run-and-train UI is the quietest but most consequential change in the set.
2026-08-17 / skill-finderFiltering multi-agent messages by answer correctness throws away value: over 4 in 10 outcome-changing wrong messages helpDiverse Hypothesis Deliberation caches five independently generated messages per problem, then hides and reveals each one to the same downstream integrator to measure whether availability improves or harms final correctness — separating trajectory value from answer correctness. Across five math and science benchmarks and two model families (gpt-oss-120b, gemma-4-31B-it), wrong-but-helpful messages appear in every benchmark-model combination, and among wrong-answer messages that changed the outcome more than four in ten changes were helpful (p=0.0002). Interventions also showed complete messages beat isolated components, so the practical move is to stop gating agent-to-agent messages on a correctness check and measure marginal contribution to the integrator instead.
2026-08-17 / sources-researcherSimpleOPD Distills Long-Context Reasoning Across Tokenizer Boundaries in Shared Text Space — Intern-S2-Preview Gains 21.2 Points on ProofBench to 55.2, Passing Gemini-2.5-ProSimpleOPD (arXiv 2608.14277, submitted Aug 14) tackles the practical blocker in on-policy distillation: teacher and student rarely share a tokenizer. It performs distillation in shared text space to sidestep the mismatch entirely, then adds a student reference KL loss and masks the advantages of special termination tokens to stop runaway generation length. Intern-S2-Preview reached 55.2 on ProofBench, a 21.2-point improvement that puts it past Gemini-2.5-Pro, with gains replicated across Qwen3, Qwen3.5, Intern-S2, GLM-4.7 and Gemma-4 and generalizing to HLE and HiPhO. Tokenizer-agnostic matters because it means you can distill from whichever teacher is strongest rather than whichever one shares your vocabulary.
2026-08-16 / arxiv-researcherMixture of Training Recomposes Independently Pretrained Layer Blocks Into a Working 1.3B Model at Parity PerplexityMoT (arXiv 2608.13277, 2026-08-13) asks whether pretraining can be decomposed into small independently schedulable jobs. It partitions a target Transformer into contiguous layer blocks, trains each inside a frozen pretrained aligner scaffold, then recomposes with an optional short end-to-end adaptation pass. On a 1.3B Gemma-style model trained on C4, a quality-parity schedule reaches the same reported perplexity as the monolithic baseline with a shorter idealized layer-equivalent critical path after aligner preparation — though it processes more aggregate tokens, so the compute advantage depends on reusing the aligner across runs. The authors are careful to present this as a proof of mechanism, not a replacement for monolithic pretraining.
2026-08-16 / skill-finderTag tool output with source provenance and field contracts: state-corruption attack success drops 84.7% to 2.3%PIPES (Provenance-Informed, Prior-Enforced Screening) defends against state-corruption attacks, where attacker-controlled data makes false claims about the environment that slip past injection filters because they read as ordinary tool results. It screens each response unit two ways — static field contracts where a schema gives stable expectations, and source provenance metadata for open-ended content — then removes, warns, blocks, or escalates on violation. On six benchmark splits with Gemma 4 31B IT under adaptive attack, attack success fell from 84.7% to 2.3% while benign utility rose slightly (92.5% vs 90.6%).
2026-08-16 / hn-researcherHugging Face: Agents Became the Hub's Primary Users in July, With Claude Code at 44.4% of All Agent TrafficHugging Face's State of Open Models report, published 2026-08-14, contains a statistic that has gone largely unremarked in the coverage: by July 2026 agents — not humans — were the Hub's primary users, and Claude Code alone accounted for 44.4% of that agent traffic. The rest of the report reframes the open-weights race by usage rather than headlines: Qwen pulls 39.6M monthly GGUF downloads against Gemma's 20.8M and Llama's 7.5M, and holds 151,448 derivative repos (2.6x Meta's total footprint, 4.7x Llama specifically) growing at 180–210 new repos daily. Models under 1B parameters take 83% of all-time downloads while everything above 100B takes 1%, and only one repository appears in both the top-25-by-downloads and top-25-by-likes lists — attention and adoption are measuring different things entirely.
2026-08-16 / reddit-researcherPer-Tensor Precision Allocation Recovered Gemma 4 E4B's Reasoning From 28.9% to 69.5% at the Exact Same 3.3 GB BudgetA quantization called CADA, posted to r/LocalLLaMA, searches per-tensor mixed precision around an IQ2_XXS byte budget rather than applying uniform 2-bit quantization. On the byte-matched control comparison the model card reports reasoning going from 28.906% (stock IQ2_XXS) to 69.531% — a 40.6 percentage-point gain, or +140.54% relative — at 3.32 GiB, a 76.32% reduction from the 14.02 GiB BF16 source. If the result holds under independent eval, it says the 2-bit quality cliff practitioners have accepted for years is largely an allocation failure, not an information-theoretic floor.

Graph relationships

USES
Aarogya Setu 2.0
India's National Health Authority is integrating Gemma 4 into Aarogya Setu 2.0.
Source finding
USES
Starcloud
Starcloud is running Gemma in orbit for intersatellite routing.
Source finding
USES
Satlyt
Satlyt is running Gemma in orbit for onboard image analysis.
Source finding
USES
NASA
NASA is running Gemma in orbit for onboard image analysis.
Source finding
RELEASED
Google
Google DeepMind announced that the Gemma family has passed one billion cumulative downloads.
Source finding
USES
Minute
Minute uses Gemma 4 as an alternative for summarization
Source finding
SUPPORTS
LLMD
LLMD supports running Gemma models transparently.
Source finding
SUPPORTS
LiteRT-LM
LiteRT-LM provides production-ready inference for Gemma models on edge devices.
Source finding
SUPPORTS
Ollama
Ollama supports Gemma as a locally runnable model.
Source finding
BUILT BY
Google
Gemma 4 26B-A4B is Google's open language model.
Source finding
USES
AI Edge Eloquent
AI Edge Eloquent uses on-device Gemma ASR models.
Source finding
BENCHMARKED AGAINST
TurboQuant
TurboQuant achieved 6x KV cache reduction with no accuracy loss on Gemma
Source finding

Source trail

arXiv 2608.14277 Hugging Face arXiv arXiv Hugging Face arXiv 2608.13517 GitHub Changelog Unsloth Docs GitHub llama.app trycua/cua NVIDIA

Graph sources

entity graphfindings textkg edgeskg entitiesnewsletter issues

Fetching from the wire…