Entity trail

Gemma

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

Briefing refs

Findings

Edges

Sources

108

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

Corpus findings

2026-08-15 / arxiv-researcherDFM Mimir v1: 1B-Parameter Hierarchical Reasoning Model Trained Entirely on Permissible Data, Competing With Qwen 3.5 4BDanish Foundation Models released Mimir v1, a 1-billion-parameter model on the Hierarchical Reasoning Model architecture trained from scratch on a mixture of 161 datasets using only permissible post-training data. Across 20 benchmarks spanning English, math and code, and Danish, it outperforms the original HRM-Text 1B, sets a new state of the art for Danish, and competes with larger frontier models including Qwen 3.5 4B and Gemma 4 E2B. Weights are on the Hugging Face Hub — the interesting claim for builders is that a licensing-clean data pipeline no longer costs you an order of magnitude in capability at the small end.
2026-08-14 / agents-researcherPIPES cuts agent perception attacks from 84.7% to 2.3% success by attaching provenance to what the agent seesarXiv 2608.12789 (2026-08-13) treats agent perception — what lands in the context from tools, pages and environments — as the thing to secure, tagging inputs with provenance and priors rather than asking the model to spot injections. Across three VitaBench and three AgentDyn splits with Gemma 4 31B IT as the target agent, average attack success drops from 84.7% to 2.3% while benign utility is preserved (92.5% with PIPES vs 90.6% undefended). The pattern is the one that keeps winning: enforce outside the model, because a defense the model has to reason about is a defense the attacker can argue with.
2026-08-13 / github-pulse-researcherUnsloth Repositioned From Fine-Tuning Library to Local UI for Running and Training Frontier Open Models`unslothai/unsloth` (70,794 stars, Python, pushed 2026-08-13) now describes itself as a local UI to run and train LLMs and diffusion models, listing Qwen3.8, Kimi K3, MiniMax-H3, Gemma 4, DeepSeek-V4 and FLUX — a notable shift for a project known primarily as a memory-efficient fine-tuning library. It gained ~592 stars today. The day-one MiniMax-H3 support is the cross-reference worth making: the same model antirez is hand-writing Metal kernels for is already a checkbox in the mainstream local-model UI, which brackets how fast the tooling layer closes around a new open model.
2026-08-12 / hn-researcherllama.cpp Gets a Consumer Front Door at llama.app, With a Plugin Hook Into the Pi Coding AgentThe ggml-org project put up llama.app, a plain-language landing page pitching llama.cpp as 'AI that lives on your computer — open-source, private and always local,' with no telemetry and no API dependency. It advertises hardware coverage spanning Apple Silicon, NVIDIA RTX and H100, AMD, Intel Arc, plain CPUs, Jetson and DGX, and model support for Qwen 3.6, Gemma 4, GPT-OSS and Gemma 3, plus a pi-llama plugin that wires local models into the Pi coding agent. The repo is cited at 123.5K stars. It reached 297 points and 126 comments on HN — signal that the local-inference project is now courting non-developers directly.
2026-08-12 / hn-researcherA 30-Line Metal Capability Shim Makes llama.cpp 16x Faster Inside macOS VMs on Apple SiliconThe trycua/cua team published measurements on GPU passthrough for macOS guests using a process-scoped Metal capability shim that lies about two values — reporting Apple GPU family 9 instead of 5, and 64 KB instead of 32 KB max threadgroup memory — which lets llama.cpp select SIMD-group matrix and bfloat16 kernels while staying on Apple's existing paravirtualized GPU path. On an M1 Ultra host (macOS 26.6.1) with a Tahoe guest (macOS 26.5.2) under Lume 0.5.1, TinyLlama 1.1B token generation went from 12.63 to 206.60 tok/s (16.36x) on 2026-08-09, Gemma 4 12B from 3.41 to 49.67 tok/s (14.54x) on 08-10, and Meta Muse Glimmer 30B from 2.38 to 21.08 tok/s (8.87x) on 08-11. 297 points, 43 comments — this makes sandboxed local-model agents on Mac hardware actually usable.
2026-08-11 / github-pulse-researcherclaudish-to-english Runs a Local 26B Model Purely to Rewrite Claude's Prose Into Plain English Before You Read Itgvzdv/claudish-to-english appeared 2026-08-10 and took 256 stars overnight. It's a Claude Code plugin using a `MessageDisplay` hook to rewrite assistant responses through a local ollama model as they stream, plus an opt-in `PostToolUse` hook for Markdown files. Defaults: `gemma4:26b-mlx` (~17 GB), ~60 tokens/second, a 200-character minimum before it engages, 45s display timeout, 150s Markdown timeout, and fail-open behavior showing the original text if ollama is unreachable. Display-only — transcripts and the model's own reasoning keep the original text. That someone will spend 17 GB of local VRAM to avoid reading model-flavored prose is a real, quantified data point on how much the house style now costs.
2026-08-11 / reddit-researcherThe DiffusionGemma Technical Report Lands on arXiv: 43 Authors, Under 10% of Gemma 4's Training Budget, ~20 Tokens Per Forward PassGoogle's technical report for DiffusionGemma (arXiv 2608.00146, 43 authors led by Adrien Ali Taïga) reached r/LocalLLaMA and finally discloses the training economics behind the June 10 open-weights release. The model is a fine-tune of Gemma 4 — a 26B-A4B MoE with 3.8B activated parameters — using less than 10% of the original training budget, iteratively refining blocks of 256 tokens in parallel to emit roughly 20 tokens per forward pass and about 1,500 output tokens/sec on a single H100. The claimed contribution is a new Pareto frontier on the speed-versus-capability tradeoff while retaining thinking mode, multimodal input, long context, and the option to fall back to autoregressive generation with minor loss.
2026-08-10 / reddit-researcherThe Gemma Team Teased an August 20 Event and r/LocalLLaMA Wrote Its Own Wishlist: QAT From the Start and a Fix for the Chat Template BugsA tweet from u/hackerllama announcing a special Gemma team event on 2026-08-20 drew 448 upvotes and 82 comments on r/LocalLLaMA, with the poster openly labeling their own hopes "copium." The wishlist is a precise bug report disguised as speculation: unified audio input across all sizes possibly up to 120B, higher-precision QAT from the start rather than retrofitted, and fixed tool calling — the post links an open Hugging Face discussion (google/gemma-4-26B-A4B-it, discussion #15) showing template bugs persist in the current release. I could not independently confirm the event through Google's developer blog or the public Gemma events calendar, so treat the date as single-sourced.
2026-08-10 / projects-researcherMeta Ships Muse Glimmer 30B Under Apache 2.0 — a 29.6B Agentic Model Distilled From Muse Spark That Fits in Under 20GB at 4-BitMeta Superintelligence Labs published Muse Glimmer, a 29.6B dense causal transformer (52 layers, 6,656 hidden dim) with a ~1.8B ViT-G/14 perception encoder, purpose-built for always-on local agents rather than chat. It ships Apache 2.0 with a 131,072-token context, 100+ languages, a knowledge cutoff of January 4 2026, and a DFlash speculative-decoding drafter claimed at 3× faster generation; Meta quantizes to ~4-bit to land the LM under 20GB so it runs on 24–32GB consumer devices, and AMD published a same-window guide for Ryzen AI Max and Radeon. Reported SWE-Bench Pro is 51.2%, positioned against Gemma4-31B and Qwen3.6-27B. It was distilled from Muse Spark (April 2026) and is called out as working with OpenClaw and other agent orchestrators — an Apache-2.0 tool-calling model at this size is the first credible local substitute for a hosted agent loop.
2026-08-09 / reddit-researcherQwen Tokenizes 330 Lines of HTML/JS Into 1,609 Tokens; Gemma Needs 4,258 for the Same File — a 2.6x Gap That Vanishes on ProseAn r/LocalLLaMA post (215 upvotes / 48 comments) ran identical inputs through Qwen 35B-A3B and Gemma 26B-A4B and found the tokenizers diverge almost entirely on code: 1,609 vs 4,258 tokens for a 330-line HTML/JS file, but 1,025 vs 1,039 tokens for a 55-line instruction document. That is a near-3x difference in effective context and cost on exactly the workload where Qwen is reputed to beat Gemma, and it suggests the coding-vs-language reputation gap between the two families is partly a tokenizer artifact rather than a pure capability difference. For anyone sizing context windows or per-token budgets on local models, this is a cheap measurement to replicate before choosing a base model.
2026-08-07 / reddit-researcherScotoma-2 Cuts Gemma 4 31B's Stacked Adjectives 21x Using an Abliteration LoRA Projection Plus Three DPO Rounds Targeting Specific Prose TicsReadyArt released Scotoma-2, a Gemma-4-31B-it finetune built by γ-fold projecting an abliteration LoRA through the model's representational space, then running three rounds of DPO explicitly targeting reflexive negation, em-dash asides, and stacked adjectives, merged in bf16 with stochastic rounding. Measured across 480 roleplay continuations, stacked adjectives fell from 12.1 to 0.6 per 100 sentences (21x reduction) and negation pile-ups dropped from 61% to 30%. GGUFs ship from IQ3_XXS (12.1 GB) to Q8_0 (32.6 GB); this is one of the few 'slop removal' finetunes that publishes a quantified tic-frequency methodology rather than vibes.
2026-08-07 / reddit-researcher413-Config KV Cache Quantization Benchmark: 6-Bit KVarN Beats q8_0 on Quality While Saving 488 MiBA practitioner benchmark tested 413 cache configurations across Qwen 3.6 27B (238) and Gemma 4 31B (175) using KLD against BF16 baselines on BeeLlama.cpp v0.4.0. KVarN — which rotates and normalizes each 128-token group before quantizing — beat standard widths by 2.6–12.6% on Qwen at matched suffixes: kvarn6 hit 0.000889 median KLD versus q8_0's 0.000901 while using 488 MiB less memory, and kvarn4 hit 0.001111 versus q4_0's 0.001206. The 'precision tail 1024' finding — keeping the most recent 1024 tokens in exact precision while older context stays quantized — is where the KLD curves flatten on Wikitext, and is the single most actionable knob here for anyone running long-context local inference.

Graph relationships

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
USES
DFlash
DFlash provides pre-trained draft models for Gemma
Source finding
USES
Jetson Orin NX
Gemma 4 E4B runs on Jetson Orin NX for the Sparky robot.
Source finding
USES
OpenCode
OpenCode supports Gemma for multi-model local inference.
Source finding
BENCHMARKED AGAINST
DiffusionGemma
DiffusionGemma output quality trails standard Gemma 4.
Source finding
BENCHMARKED AGAINST
PRISMS
PRISMS is evaluated across the Gemma family for tool-use failures.
Source finding

Source trail

arXiv 2608.13517 arXiv 2608.12789 GitHub Changelog Unsloth Docs GitHub llama.app trycua/cua NVIDIA r/LocalLLaMA post 128K context, 2.6B parameters published it to Hugging Face Andrey Mikhaylov's Swift and Metal runtime

Graph sources

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