gemma-4-E2B

5.12B parameters, several kinds of input and output, 128K tokens of context.

Verified 2026-10-25 Rank #255 in LLMs · score 85.7 method v1 Sources 3 Downloads 30d 181.6k · #260 of 619 tracked

Changed this week: 2 new quantized builds on the Hub (46 in all).2026-10-24

Can I run it?3.1GB at INT4Estimatefits a 16 GB cardmethodCommunity: Show HN: Prompt-to-Excalidraw demo with Gemma 4 E2B in the browser (3.1GB) →

Can I use it?Apache 2.0Permissive; patent grantlicence file2026-10-24

Do people use it?181.6kdownloads, 30 days#260 of 619 we track · top 42%+8 likes, downloads −17% this week536 likesHugging Face2026-10-24

Worth reading

Picked by rule from 37 tracked pieces: the lab's own post, the most shared, a field report, the latest.

Most discussed · 868 HN pts

Gemma 4 on iPhone

apps.apple.com 2026-04-05 868 HN pts · 234 comments about the gemma 4 family family

Latest · 59 days ago

Gemma 4 E2B inference in 700 lines of C

github.com 2026-08-27 18 HN pts 722 words

25 best of 37 tracked

The 2 picks above, plus these 23. Top is the radar's score: discussion, source authority and the lab's own posts, fading with age.

CodeShow HN: Open-source engine running Gemma 4 26B in 2 GB RAM on any M-series Macgithub.com · 2026-07-29
ArticleGemma 4 12B: A unified, encoder-free multimodal modelblog.google · 2026-06-03 · official
ArticleRunning Gemma 4 26B at 5 tokens/sec on a 13-year-old Xeon with no GPUneomindlabs.com · 2026-07-15
CodeShow HN: Cactus Hybrid: We taught Gemma 4 to know when it's wronggithub.com · 2026-07-22 · 191 HN pts
ArticleGemma 4 QAT models: Optimizing compression for mobile and laptop efficiencyblog.google · 2026-06-05 · 406 HN pts · official
ArticleAccelerating Gemma 4: faster inference with multi-token prediction draftersblog.google · 2026-05-05 · 687 HN pts · official
ArticleGoogle releases Gemma 4 open modelsdeepmind.google · 2026-04-02 · 1,812 HN pts · official
ArticleFable 5 pushed Gemma 4 to 255 tok/s on WebGPUxcancel.com · 2026-06-18 · 48 HN pts
ArticleGoogle Gemma 4 Runs Natively on iPhone with Full Offline AI Inferencegizmoweek.com · 2026-04-15 · 303 HN pts
CodeApril 2026 TLDR Setup for Ollama and Gemma 4 26B on a Mac minigist.github.com · 2026-04-03
ArticleShow HN: Determinstic LLM inference for lowest price Gemma 4, with Windows XPtokendelivery.ai · 2026-09-12 · 9 HN pts
ArticleI ran Gemma 4 as a local model in Codex CLIblog.danielvaughan.com · 2026-04-12 · 285 HN pts
ArticleRunning Gemma 4 locally with LM Studio's new headless CLI and Claude Codeai.georgeliu.com · 2026-04-05
ArticleGemma 4 on Cerebras - The Fastest Inference Is Now Multimodalcerebras.ai · 2026-06-30
CodeShow HN: Gemma 4 Multimodal Fine-Tuner for Apple Silicongithub.com · 2026-04-07
ArticleShow HN: Prompt-to-Excalidraw demo with Gemma 4 E2B in the browser (3.1GB)teamchong.github.io · 2026-04-19
CodeShow HN: Local privacy-first Microsoft Recall alternative with Gemma 4github.com · 2026-07-04
ArticleGemma 4: Byte for byte, the most capable open modelsblog.google · 2026-04-02 · official
ArticleA Visual Guide to Gemma 4 12Bnewsletter.maartengrootendorst.com · 2026-06-03
CodeShow HN: Local LLM code-generation with Gemma 4 e2B via JSON AST to Clojuregithub.com · 2026-05-19
ArticleShow HN: Running Gemma-4 26B at 124 tokens/SEC on a CPU, no GPUapeg.dev · 2026-06-30
ArticleShow HN: I made a Gemma 4 Mac app that names screenshots with local AIsnapname.app · 2026-05-31
ArticleGoogle's new Gemma 4 12B model is designed to run on any laptop with 16GB of RAMarstechnica.com · 2026-06-03

Show all 23

Where it's used

Hugging Face does not say who downloads a model or with what. These are the proxies: API providers listing it, quantized builds, fine-tunes/adapters/merges, and Spaces that load it.

No API provider serves it, so its downloads are people running it themselves: 46 quantized builds and 12 Spaces.

As a service0no API provider listed
On own hardware46quantized builds
Built on197149 fine-tunes · 48 adapters · 0 merges
In apps12Hugging Face Spaces

Top Spaces running it: Carnot-EBM/hallucination-detector ♥ 1

Will it fit?

Weights only, an estimate: leave room for the KV cache. Method

Fits a 16 GB card at INT4

About 3.1 GB at INT4, 6.1 GB at FP8, 12 GB at BF16: weights only, so leave room for the KV cache.

Estimate params × bytes per param × 1.2 · how we estimate

The story so far

Weights, every dated article, and repo commits on one rail.

10 dated articles and 7 repo commits so far

Teal is the weights (2026-03-02), amber triangles are 10 dated articles (each links to it), grey ticks are repo commits.

What people built on it

243 derivatives on the Hub. Which build to run, by runtime: the most downloaded in each class.

243 derivatives on the Hub, most of them fine-tunes

From the Hugging Face model tree; quantized builds in teal, fine-tunes, merges and adapters in greys.

GGUF mradermacher/gemma-4-E2B-GGUF for llama.cpp, Ollama, LM Studio 23.9k downloads MLX majentik/gemma-4-E2B-TurboQuant-MLX-2bit for Apple silicon 2k downloads NVFP4 bg-digitalservices/Gemma-4-E2B-NVFP4A16 for vLLM, SGLang 10.6k downloads

All variants →

Reference

Everything else, folded: the full tables, every source.

Benchmarks

15 rows

Benchmarks 15 rows

measured third-party leaderboard rowauthor-reported number from the model card or eval files (15)No confidence intervals unless the source gave them; settings differ between rows, so compare within a benchmark, not across. Date = the benchmark's own date when it gives one, else when we read it.

Benchmark	Value	Setting / notes	Reported by	Date
author-reported AIME 2026 · Reason through hard problems	37.5% percent	Gemma 4 E2B	model author	2026-10-07
author-reported BBEH · Reason through hard problems	21.9% percent	Gemma 4 E2B	model author	2026-10-07
author-reported CoVoST	33.47	Gemma 4 E2B	model author	2026-10-07
author-reported Codeforces · Write code with agents	633 elo	Gemma 4 E2B	model author	2026-10-07
author-reported FLEURS (lower is better)	0.09 other	Gemma 4 E2B	model author	2026-10-07
author-reported GPQA Diamond · Reason through hard problems	43.4% percent	Gemma 4 E2B	model author	2026-10-07
author-reported LiveCodeBench · Write code with agents	44.0% percent	Gemma 4 E2B	model author	2026-10-07
author-reported MMLU-Pro · Reason through hard problems	60.0% percent	Gemma 4 E2B	model author	2026-10-07
author-reported MMMLU · Work across languages	67.4% percent	Gemma 4 E2B	model author	2026-10-07
author-reported MMMU-Pro · Understand images and video	44.2% percent	Gemma 4 E2B	model author	2026-10-07
author-reported MRCR · Read long documents	19.1% percent	Gemma 4 E2B	model author	2026-10-07
author-reported MathVision · Understand images and video	52.4% percent	Gemma 4 E2B	model author	2026-10-07
author-reported MedXPertQA MM	23.5% percent	Gemma 4 E2B	model author	2026-10-07
author-reported OmniDocBench · Extract text from documents	0.29 other	Gemma 4 E2B	model author	2026-10-07
author-reported tau-bench · Call tools and APIs	24.5% percent	Gemma 4 E2B	model author	2026-10-07

What the card says

2 cited sentences

Summary generated, cited

Gemma 4 E2B is a 5.12B parameter multimodal model designed for text, image, and audio processing.^[1]^[2] This dense model features a 128K context window and is released under the Apache 2.0 license.^[3]^[4]

parameters human
“Processes Text, Image with variable aspect ratio and reso…”
“The small models feature a 128K context window”
license

Generated 2026-10-07 15:04 UTC by parasail-gemma-4-26b-a4b-it; each numeral points at the fact it rests on. Not a verdict.

Use-case evidence

9 use cases

What it is good at 9 use cases

Labels are evidence, not verdicts: measured a third-party leaderboard row for this model; author-reported a number the model author published in the card or eval files; structural a deterministic signal from the name, config or chat template, or a decision-model estimate. Each chip links to its source. All use cases.

Facts

21 fields

Listed in LLMs

Facts

gemma-4-E2B has 5.12B parameters. (safetensors metadata, observed 2026-10-24)
Context length is 131,072 tokens (128K). (config.json max_position_embeddings, observed 2026-10-24)
License: Apache 2.0 (friction: none). Permissive; patent grant. (license file, observed 2026-10-24)
Created 2026-03-02, last modified 2026-07-15. (HF API, observed 2026-10-24)

Pipeline tag	any-to-any
Library	transformers
Architectures	Gemma4ForConditionalGeneration
Model type	gemma4
Dtype	bfloat16
Params by dtype	BF16: 5,123,178,051
Size band	M
MoE	no
Context length	131,072 (config.json max_position_embeddings)
Papers	arXiv:2607.02770
License	Apache 2.0 (apache-2.0)
Hub snapshot	2026-10-25

Servability

vLLM · SGLang

Servability

vLLM: supported · Gemma4ForConditionalGeneration · 7050ef881ff0835f6890a47db1859a24c65481d6SGLang: supported · Gemma4ForConditionalGeneration · 773cb938fe9484a8f7e7e46fb1b1fe96412c59ed

vLLM registry (7050ef881ff0835f6890a47db1859a24c65481d6) · SGLang registry (773cb938fe9484a8f7e7e46fb1b1fe96412c59ed) · Matched by architecture class name against each project's registry on main; 'supported' means the architecture is registered, not that this checkpoint was tested.

Hardware fit

3 estimates

Hardware fit ESTIMATE

Weights-only memory for gemma-4-E2B: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)	12 GB · Single GPU (<24 GB)
FP8 (1 byte/param)	6.1 GB · Single GPU (<24 GB)
INT4 (0.5 bytes/param)	3.1 GB · Edge (<4 GB)

Weights only (params × bytes/param × 1.2 overhead); KV cache and activations extra.

All variants

40 variants

Lineage

Original model Family: google/gemma-4 243 derivatives on the Hub

finetune: 149quantized: 46adapter: 48

Top variants (40 shown, ranked by downloads)

Variant	Relation	Quant	Params	Band	License	Author	Likes	Downloads
unsloth/gemma-4-E2B	finetune	—	5.12B	M	apache-2.0	unsloth	12	23.9k
mradermacher/gemma-4-E2B-GGUF	quantized	GGUF	—	—	apache-2.0	mradermacher	2	23.9k
software-mansion/react-native-executorch-gemma-4	quantized	—	—	—	gemma	software-mansion	3	21.3k
mradermacher/gemma-4-E2B-i1-GGUF	quantized	GGUF	—	—	apache-2.0	mradermacher	1	17.1k
Kasper-Bankler/gemma-4-E2B-Alignment-Study	quantized	GGUF	5.10B	M	apache-2.0	Kasper-Bankler	5	14.8k
software-mansion/react-native-executorch-gemma-4-multimodal	quantized	—	—	—	gemma	software-mansion	0	12.3k
bg-digitalservices/Gemma-4-E2B-NVFP4A16	quantized	—	4.29B	M	apache-2.0	bg-digitalservices	0	10.6k
bg-digitalservices/Gemma-4-E2B-NVFP4	quantized	NVFP4	4.29B	M	apache-2.0	bg-digitalservices	0	9.1k
Mungert/gemma-4-E2B-it-GGUF	quantized	GGUF	—	—	apache-2.0	Mungert	1	7.6k
RedHatAI/gemma-4-E2B-it	finetune	—	5.12B	M	apache-2.0	RedHatAI	3	7.1k
majentik/gemma-4-E2B-RotorQuant-GGUF-IQ4_XS	quantized	GGUF	—	—	apache-2.0	majentik	0	4.8k
Lythri/Lythri-4B-A2B	finetune	—	4.63B	M	apache-2.0	Lythri	17	3.5k
Open4bits/gemma-4-E2B-GGUF	quantized	GGUF	—	—	apache-2.0	Open4bits	5	4.1k
LLM-OS-Models/gemma-4-E2B-Terminal-SFT-Native-Liquid-1Epoch	finetune	—	5.05B	M	—	LLM-OS-Models	1	4.2k
LLM-OS-Models/gemma-4-E2B-Terminal-SFT-Native-Liquid-2Epoch	finetune	—	5.05B	M	—	LLM-OS-Models	1	4.1k
Jessylg27/gemma-4-E2B-GGUF	quantized	GGUF	—	—	apache-2.0	Jessylg27	0	3.8k
ggml-org/gemma-4-E2B-GGUF	quantized	GGUF	—	—	apache-2.0	ggml-org	4	3.6k
ImanolSuarez/gemma-4-E2B-it-heretic-seguridadDeLaInformacion	quantized	GGUF	5.10B	M	apache-2.0	ImanolSuarez	4	2.7k
livadies/gemma-4-E2B-Ghetto-NF4	quantized	-NF4	5.20B	M	gemma	livadies	0	2.6k
majentik/gemma-4-E2B-RotorQuant-GGUF-Q4_K_M	quantized	GGUF	—	—	apache-2.0	majentik	1	2.2k
majentik/gemma-4-E2B-TurboQuant-MLX-2bit	quantized	MLX	5.12B	M	apache-2.0	majentik	2	2k
YoSaNoo/Forge-1-Gemma-4-E2B	quantized	GGUF	—	—	apache-2.0	YoSaNoo	9	1.3k
FastFlowLM/Gemma4-12B-IT-NPU2	finetune	—	—	—	apache-2.0	FastFlowLM	2	1.6k
majentik/gemma-4-E2B-RotorQuant-GGUF-Q2_K	quantized	GGUF	—	—	apache-2.0	majentik	0	1.5k
majentik/gemma-4-E2B-RotorQuant-GGUF-Q5_K_M	quantized	GGUF	—	—	apache-2.0	majentik	0	1.5k
majentik/gemma-4-E2B-RotorQuant-GGUF-Q3_K_M	quantized	GGUF	—	—	apache-2.0	majentik	0	1.4k
zanish-labs/gemma-4-E2B-it-Q4_K_M-gguf	quantized	gguf	—	—	apache-2.0	zanish-labs	0	1.2k
majentik/gemma-4-E2B-RotorQuant-GGUF-Q8_0	quantized	GGUF	—	—	apache-2.0	majentik	1	950
SandLogicTechnologies/gemma-4-E2B-GGUF	quantized	GGUF	—	—	apache-2.0	SandLogicTechnologies	0	942
JMingo/gemma-4-E2B-it-GGUF	quantized	GGUF	—	—	apache-2.0	JMingo	0	848
arisridwan/islamgpt-id-gguf	quantized	gguf	—	—	gemma	arisridwan	0	837
wyan/usenet-gemma-4-E2B-lora-it	quantized	GGUF	5.10B	M	—	wyan	0	837
economyofdreams/Yuli-E2B-Reddit	quantized	GGUF	—	—	other	economyofdreams	1	757
eulogik/Prajna-CRNv2	finetune	—	—	—	gemma	eulogik	4	605
Animateus/gemma-4-E2B-GGUF	quantized	GGUF	—	—	apache-2.0	Animateus	0	766
eulogik/Prajna-V2	finetune	—	—	—	gemma	eulogik	1	664
nau-ai/Nau-1.0	quantized	GGUF	5.44B	M	cc-by-nc-sa-4.0	nau-ai	1	592
Animateus/gemma-4-E2B-6bit	quantized	—	4.63B	M	apache-2.0	Animateus	0	639
ananddey/gemma-4-E2B-asm-init	finetune	—	2.69B	M	gemma	ananddey	1	585
YoSaNoo/Forge-1-Little-Gemma-4-E2B	quantized	GGUF	—	—	mit	YoSaNoo	1	585

Repository changelog

7 commits

changed in the last 7 daysOther: 2026-10-24all changes

Repository changelog 7 commits

Date	Commit	SHA
2026-07-15	Update README (#10)	d29ff6b
2026-07-10	Add Technical Report	19f17d3
2026-06-03	Update README.md	63db66a
2026-06-02	Update README.md	84434eb
2026-04-02	Upload tokenizer.json	9d53598
2026-04-02	Update tokenizer_config.json	94babfa
2026-04-02	Preparing for release!	926a599

Articles and coverage

37 pieces

Ranked by source signals (HN, Lobsters, authority domains, official posts) and recency. Summaries are generated from a stored copy of the article text; every sentence cites a span of that text, and nothing is shown that failed the citation check.

Official3

Gemma 4 QAT models: Optimizing compression for mobile and laptop efficiency

blog.google2026-06-05officialauthorityHN front pageabout the gemma 4 familyHN 406 pts · 130 comments714 words

Google released new Gemma 4 checkpoints optimized with Quantization-Aware Training to improve efficiency on edge devices and consumer GPUs. The E2B model achieves a 1GB memory footprint using a mobile-specialized schema that applies targeted 2-bit quantization to token generation layers. Developers can access GGUF formats ready for llama.cpp and MTP QAT checkpoints to preserve inference speedups while running locally.written, reviewed, cited

The mobile-specialized schema pre-calculates activation scaling settings during training to reduce workload on mobile chips.
Targeted 2-bit quantization compresses token generation parts while keeping core reasoning layers at higher precision.
Deploying only needed modalities allows the Gemma 4 E2B text-only model to require less than 1 GB of memory.

Accelerating Gemma 4: faster inference with multi-token prediction drafters

blog.google2026-05-05officialauthorityHN front pageabout the gemma 4 familyHN 687 pts · 330 comments796 words

Google released Multi-Token Prediction (MTP) drafters for the Gemma 4 family under the same open-source Apache 2.0 license as Gemma 4. These drafters deliver up to a 3x speedup for models like the 26B MoE and 31B Dense versions without quality degradation. Benchmarks on hardware such as the NVIDIA RTX PRO 6000 show that increasing batch sizes to 4 to 8 unlocks up to a ~2.2x speedup locally.written, reviewed, cited

The MTP drafter shares the target model's KV cache to avoid recalculating context the larger model has already processed.
For E2B and E4B edge models, an efficient clustering technique in the embedder accelerates generation where logit calculation is a bottleneck.
The 26B mixture-of-experts model presents unique routing challenges at a batch size of 1 on Apple Silicon.

Google releases Gemma 4 open models

deepmind.google2026-04-02officialauthorityHN front pageabout the gemma 4 familyHN 1,812 pts · 474 comments253 words

Analysis7

Show HN: Cactus Hybrid: We taught Gemma 4 to know when it's wrong

github.com2026-07-22authorityHN front pageabout the gemma 4 familyHN 191 pts · 44 comments714 words

Cactus Hybrid introduces on-device models that score every answer with a confidence between 0 and 1 to decide when to hand off to a bigger model. The Gemma 4 E2B Hybrid model matches Gemini 3.1 Flash-Lite performance by routing only 15–55% of queries to the cloud while running the rest locally. It is MIT-licensed and achieves a 0.770 AUROC on MMLU, with a probe trained on zero audio data yet reaching 0.79–0.88 AUROC on four audio benchmarks.written, reviewed, cited

Weights on the meta device crash the confidence read because probes score generations outside the module forward path.
The probe is a patch you compile into the llama.cpp engine rather than a standalone library component.
Transformers version 5.14 and above cause a segfault on this specific checkpoint.

Gemma 4 E2B inference in 700 lines of C

github.com2026-08-27authorityHN 18 pts · 0 comments722 words

Fable 5 pushed Gemma 4 to 255 tok/s on WebGPU

xcancel.com2026-06-18about the gemma 4 familyHN 48 pts · 22 comments

Gemma 4 on iPhone

apps.apple.com2026-04-05HN front pageabout the gemma 4 familyHN 868 pts · 234 comments

Google Gemma 4 Runs Natively on iPhone with Full Offline AI Inference

gizmoweek.com2026-04-15HN front pageabout the gemma 4 familyHN 303 pts · 187 comments364 words

Show HN: Determinstic LLM inference for lowest price Gemma 4, with Windows XP

tokendelivery.ai2026-09-12about the gemma 4 familyHN 9 pts · 1 comments

I ran Gemma 4 as a local model in Codex CLI

blog.danielvaughan.com2026-04-12HN front pageabout the gemma 4 familyHN 285 pts · 116 comments

All coverage tracked for this model (37) — append-only, nothing is ever removed

Title	Source	Published	First seen	Signals
Gemma 4 Architecture -- Complete Technical Comparison	g4.si5.pl	—	2026-10-07	exa
Show HN: Determinstic LLM inference for lowest price Gemma 4, with Windows XP	tokendelivery.ai	2026-09-12	2026-10-07	hn
Gemma 4 E2B inference in 700 lines of C	github.com	2026-08-27	2026-10-07	hn
Show HN: Open-source engine running Gemma 4 26B in 2 GB RAM on any M-series Mac	github.com	2026-07-29	2026-10-07	hn
Show HN: Cactus Hybrid: We taught Gemma 4 to know when it's wrong	github.com	2026-07-22	2026-10-07	hn
Running Gemma 4 26B at 5 tokens/sec on a 13-year-old Xeon with no GPU	neomindlabs.com	2026-07-15	2026-10-07	hn
Show HN: Local privacy-first Microsoft Recall alternative with Gemma 4	github.com	2026-07-04	2026-10-07	hn
Show HN: Running Gemma-4 26B at 124 tokens/SEC on a CPU, no GPU	apeg.dev	2026-06-30	2026-10-07	hn
Gemma 4 on Cerebras - The Fastest Inference Is Now Multimodal	cerebras.ai	2026-06-30	2026-10-07	hn
Fable 5 pushed Gemma 4 to 255 tok/s on WebGPU	xcancel.com	2026-06-18	2026-10-07	hn
Gemma 4 QAT models: Optimizing compression for mobile and laptop efficiency	blog.google official	2026-06-05	2026-10-07	hn
Google's new Gemma 4 12B model is designed to run on any laptop with 16GB of RAM	arstechnica.com	2026-06-03	2026-10-07	hn
A Visual Guide to Gemma 4 12B	newsletter.maartengrootendorst.com	2026-06-03	2026-10-07	hn
Gemma 4 12B: A unified, encoder-free multimodal model	blog.google official	2026-06-03	2026-10-07	hn
Show HN: I made a Gemma 4 Mac app that names screenshots with local AI	snapname.app	2026-05-31	2026-10-07	hn
Show HN: Local LLM code-generation with Gemma 4 e2B via JSON AST to Clojure	github.com	2026-05-19	2026-10-07	hn
Accelerating Gemma 4: faster inference with multi-token prediction drafters	blog.google official	2026-05-05	2026-10-07	hn
A weekend with LoRA on Gemma 4 E2B: instrumenting what fine-tuning changes	aiexplr.com	2026-04-26	2026-10-07	hn
Show HN: Prompt-to-Excalidraw demo with Gemma 4 E2B in the browser (3.1GB)	teamchong.github.io	2026-04-19	2026-10-07	hn
Understanding Gemma 4: A Deep Dive into Google's New Open-Weight Model Lineup - Dev Repository	dev-repository.com	2026-04-16	2026-10-07	exa
Google Gemma 4 Runs Natively on iPhone with Full Offline AI Inference	gizmoweek.com	2026-04-15	2026-10-07	hn
Show HN: I benchmarked Gemma 4 E2B – the 2B model beat the 12B on multi-turn	aiexplr.com	2026-04-13	2026-10-07	hn, exa
I ran Gemma 4 as a local model in Codex CLI	blog.danielvaughan.com	2026-04-12	2026-10-07	hn
Gemma 4 E2B vs E4B Benchmark: The Hidden Thinking Mode That Makes the Smaller Model 20× Slower — the KodeLab	thekodelab.com	2026-04-11	2026-10-07	exa
A Visual Guide to Gemma 4 \| Artem Daniliants	daniliants.com	2026-04-10	2026-10-07	exa
A Visual Guide to Gemma 4	newsletter.maartengrootendorst.com	2026-04-09	2026-10-07	hn
Gemma 4 Benchmarks: Ultimate Local AI Performance Guide 2026 - Gemma 4 Wiki	gemma4.wiki	2026-04-09	2026-10-07	exa
Show HN: Gemma 4 Multimodal Fine-Tuner for Apple Silicon	github.com	2026-04-07	2026-10-07	hn
Gemma 4 2B Model Guide: Complete AI Implementation Manual 2026 - Gemma 4 Wiki	gemma4.wiki	2026-04-07	2026-10-07	exa
Gemma 4 on iPhone	apps.apple.com	2026-04-05	2026-10-07	hn
Running Gemma 4 locally with LM Studio's new headless CLI and Claude Code	ai.georgeliu.com	2026-04-05	2026-10-07	hn
April 2026 TLDR Setup for Ollama and Gemma 4 26B on a Mac mini	gist.github.com	2026-04-03	2026-10-07	hn
Gemma 4 Deep Dive — How Apache 2.0 Opens the Door to Sovereign AI \| Pebblous	blog.pebblous.ai	2026-04-03	2026-10-07	exa
Gemma 4 Deep Dive: Architecture, Benchmarks, and Impact — Swfte AI	swfte.com	2026-04-03	2026-10-07	exa
Google releases Gemma 4 open models	deepmind.google official	2026-04-02	2026-10-07	hn
Gemma 4: Byte for byte, the most capable open models	blog.google official	2026-04-02	2026-10-07	hn
Google Gemma 4 Deep Dive: Architecture, MoE & Benchmarks	qubridai.hashnode.dev	2026-04-02	2026-10-07	exa

Papers

1 paper

Papers 1

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Gemma 4 Technical Report this model

Gemma Team, Sherif El Abd, Vaibhav Aggarwal +320 · 2026-07-02 · PDF · HF paper page (85 upvotes)

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Sources

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Sources

Hub snapshot: 2026-10-25 · HF API: 2026-10-24 21:36 UTC · Providers: 2026-10-24 21:44 UTC · Articles: 2026-10-24 21:45 UTC · Summary: 2026-10-07 15:04 UTC · Last verified 2026-10-25

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