Models › codegemma family

google Gemma Terms of Use out 3 years ago

8.54B parameters, text in, text out, 8K tokens of context.

Verified 2026-10-25 Rank #662 in LLMs · score 72.9 method v1 Sources 3 Downloads 30d 51.2k · #380 of 619 tracked

Can I run it?5.1GB at INT4Estimatefits a 16 GB cardmethod
Can I use it?Gemma Terms of UseGoogle's terms + prohibited-use policy; must pass terms downstreamlicence file2026-10-24
Do people use it?51.2kdownloads, 30 days#380 of 619 we track · below the median+1 likes, downloads −2% this week262 likesHugging Face2026-10-24

Worth reading

Picked by rule from 2 tracked pieces: the lab's own post, the most shared, a field report, the latest.

2 best of 2 tracked

The 1 pick above, plus these 1. Top is the radar's score: discussion, source authority and the lab's own posts, fading with age.

  1. ArticleCodeGemma – an official Google release for code LLMshuggingface.co · 2024-04-09 · 4 HN pts

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: 10 quantized builds and 21 Spaces.

  • As a service0no API provider listed
  • On own hardware10quantized builds
  • Built on1610 fine-tunes · 6 adapters · 0 merges
  • In apps21Hugging Face Spaces

Top Spaces running it: ysharma/CodeGemma ♥ 90 · Justinrune/LLaMA-Factory ♥ 12 · NiansuhAI/Main ♥ 10

Will it fit?

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

Fits a 16 GB card at INT4

About 5.1 GB at INT4, 10 GB at FP8, 21 GB at BF16: weights only, so leave room for the KV cache.

16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 5.1 GB of weights (estimate; KV cache and activations extra)INT45.1 GBFP8: about 10 GB of weights (estimate; KV cache and activations extra)FP810 GBBF16: about 21 GB of weights (estimate; KV cache and activations extra)BF1621 GB
16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 5.1 GB of weights (estimate; KV cache and activations extra)INT45.1 GBFP8: about 10 GB of weights (estimate; KV cache and activations extra)FP810 GBBF16: about 21 GB of weights (estimate; KV cache and activations extra)BF1621 GB

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

The story so far

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

2 dated articles and 7 repo commits so far

Teal is the weights (2024-03-21), amber triangles are 2 dated articles (each links to it), grey ticks are repo commits.

AprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOct2024-08-07: 1 repo commit2024-06-27: 1 repo commit2024-04-16: 1 repo commit2024-04-11: 1 repo commit2024-04-09: 3 repo commitsGoogle CodeGemma: Open Code Models Based on Gemma [pdf] · 2024-04-09CodeGemma – an official Google release for code LLMs · 2024-04-09Weights 2024-03-21 · 2024-03-21Weights 2024-03-21now
AprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOctNovDecJanFebMarAprMayJunJulAugSepOct2024-08-07: 1 repo commit2024-06-27: 1 repo commit2024-04-16: 1 repo commit2024-04-11: 1 repo commit2024-04-09: 3 repo commitsGoogle CodeGemma: Open Code Models Based on Gemma [pdf] · 2024-04-09CodeGemma – an official Google release for code LLMs · 2024-04-09Weights 2024-03-21 · 2024-03-21Weights 2024-03-21now

What people built on it

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

26 derivatives on the Hub, led by 10 quantized

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

10 quantized (Hugging Face model tree)10 fine-tunes (Hugging Face model tree)6 adapters (Hugging Face model tree)10 quantized10 fine-tunes6 adapters
10 quantized (Hugging Face model tree)10 fine-tunes (Hugging Face model tree)6 adapters (Hugging Face model tree)10 quantized10 fine-tunes6 adapters

All variants →

Reference

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

Benchmarks

20 rows

Benchmarks 20 rows

measured third-party leaderboard rowauthor-reported number from the model card or eval files (20)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.
BenchmarkValueSetting / notesReported byDate
author-reported BC HE C#26.77B-ITmodel author2026-10-07
author-reported BC HE C++42.27B-ITmodel author2026-10-07
author-reported BC HE Go28.67B-ITmodel author2026-10-07
author-reported BC HE Java48.47B-ITmodel author2026-10-07
author-reported BC HE JavaScript467B-ITmodel author2026-10-07
author-reported BC HE Kotlin51.67B-ITmodel author2026-10-07
author-reported BC HE Python48.47B-ITmodel author2026-10-07
author-reported BC HE Rust367B-ITmodel author2026-10-07
author-reported HumanEval · Write code with agents56.17B-ITmodel author2026-10-07
author-reported HumanEval · Write code with agents68.257B-ITmodel author2026-10-07
author-reported HumanEval · Write code with agents20.057B-ITmodel author2026-10-07
author-reported MBPP · Write code with agents54.27B-ITmodel author2026-10-07
author-reported MBPP · Write code with agents56.77B-ITmodel author2026-10-07
author-reported MBPP · Write code with agents41.27B-ITmodel author2026-10-07
author-reported MBPP · Write code with agents46.27B-ITmodel author2026-10-07
author-reported MBPP · Write code with agents57.37B-ITmodel author2026-10-07
author-reported MBPP · Write code with agents61.47B-ITmodel author2026-10-07
author-reported MBPP · Write code with agents59.97B-ITmodel author2026-10-07
author-reported MBPP · Write code with agents627B-ITmodel author2026-10-07
author-reported MBPP · Write code with agents53.57B-ITmodel author2026-10-07

Use-case evidence

3 use cases

What it is good at 3 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

20 fields

Listed in LLMs

Facts

Pipeline tagtext-generation
Librarytransformers
ArchitecturesGemmaForCausalLM
Model typegemma
Dtypebfloat16
Params by dtypeBF16: 8,537,680,896
Size bandM
MoEno
Context length8,192 (config.json max_position_embeddings)
Gatedmanual
LicenseGemma Terms of Use (gemma)
Hub snapshot2026-10-25

Servability

vLLM · SGLang

Servability

vLLM: supported · GemmaForCausalLM · 7050ef881ff0835f6890a47db1859a24c65481d6SGLang: supported · GemmaForCausalLM · 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 codegemma-7b-it: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)21 GB · Single GPU (<24 GB)
FP8 (1 byte/param)10 GB · Single GPU (<24 GB)
INT4 (0.5 bytes/param)5.1 GB · Single GPU (<24 GB)

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

All variants

26 variants

Lineage

Original model Family: google/codegemma 26 derivatives on the Hub

quantized: 10finetune: 10adapter: 6

Top variants (26 shown, ranked by downloads)

Relation
Quant
Size
26 rows
Variant Relation Quant Params Band License Author Likes Downloads
MaziyarPanahi/codegemma-7b-it-GGUF quantized GGUF — — — MaziyarPanahi 0 10.2k
mradermacher/codegemma-7b-it-i1-GGUF quantized GGUF — — gemma mradermacher 0 8.3k
second-state/CodeGemma-7b-it-GGUF quantized GGUF — — gemma second-state 2 6.3k
mradermacher/codegemma-7b-it-GGUF quantized GGUF — — gemma mradermacher 1 3.1k
alphaduriendur/codegemma-7b-it-Q4_K_M-GGUF quantized GGUF — — gemma alphaduriendur 0 551
anthonym21/pinescript-v5-instructions-merged finetune — 8.54B M apache-2.0 anthonym21 2 307
sheldonrobinson/codegemma-7b-it-Q8_0-GGUF quantized Q8_0 — — gemma sheldonrobinson 0 374
bms22/codegemma-7b-it-Q4_K_M-GGUF quantized GGUF — — gemma bms22 0 372
ernie-research/APPS-Gemma-7B-MA-PPO-Fixed10 finetune — 8.54B M mit ernie-research 1 254
sheldonrobinson/codegemma-7b-it-Q4_K_M-GGUF quantized GGUF — — gemma sheldonrobinson 0 299
lapfed255/codegemma-7b-it-Q4_K_M-GGUF quantized GGUF — — gemma lapfed255 0 273
scthornton/codegemma-7b-securecode adapter — — — gemma scthornton 0 235
zeetroid/code-bench-CodeGemma-7BIT-cg-nv9n_it_zs adapter — — — gemma zeetroid 0 182
llmware/codegemma-7b-it-ov quantized — — — gemma llmware 1 99
zeetroid/code-bench-CodeGemma-7BIT-cg-nv9n_it_fs adapter — — — gemma zeetroid 0 119
lurf21/codegemma-7b-it-NEP finetune — 9.32B L apache-2.0 lurf21 0 112
kawasumi/Tema_Q-R2.0-Code finetune — 8.54B M gemma kawasumi 0 96
ShethArihant/PSC-2_codegemma-7b-it_sft_2-epochs adapter — — — — ShethArihant 0 70
anthonym21/pinescript-v5-instructions finetune — — — — anthonym21 1 0
litert-community/codegemma-7b-it-int4-litertlm finetune int4 — — gemma litert-community 1 0
KakashiH/BashExplainer_Gemma adapter — — — apache-2.0 KakashiH 0 40
LDES777/tap_ft_gemma adapter — — — — LDES777 0 21
gaianet/CodeGemma-7b-it-GGUF finetune GGUF — — gemma gaianet 0 0
athenadai/codegemma-finetuned-qlora-rank4 finetune — — — — athenadai 0 0
ShethArihant/codegemma-7b-it_sft_2-epochs finetune — — — — ShethArihant 0 0
cpxavier2/codegemma-7b-it-int4-litertlm finetune int4 — — gemma cpxavier2 0 0

Repository changelog

7 commits

Repository changelog 7 commits

DateCommitSHA
2024-08-07 Upload fixed tokenizer files (#11) 078cdc5
2024-06-27 Update link to terms (#10) eeb9fb1
2024-04-16 Fix tokenizer (#7) 9b1d923
2024-04-11 Update README.md (#8) 14a1c06
2024-04-09 Update README.md 6b02e58
2024-04-09 Tokenizer Fixes (#5) 2a467a0
2024-04-09 Squashing commit dd73e23

Articles and coverage

2 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.

Official1

huggingface.co2024-04-09authorityabout the codegemma familyHN 4 pts · 2 comments1,655 words
Google released CodeGemma, a family of open-access code-specialist LLMs based on the Gemma architecture. The 2B and 7B base models were trained on an additional 500 billion tokens of primarily English language data, mathematics, and code to improve logical reasoning. CodeGemma-7B outperforms similarly-sized 7B models on HumanEval and performs best on GSM8K among 7B models. The original checkpoints are released in bfloat16 precision, though 4-bit loading of CodeGemma 7B takes about 9 GB of memory to run.written, reviewed, cited
  • CodeGemma 2B is trained exclusively on code infilling for fast completion where latency and privacy are crucial.
  • The 7B base model mixes 80% code infilling data with natural language for broader code and language understanding.
  • Generation must stop early using terminators because the model tends to output extra code or comments after the FIM tokens.

Analysis1

storage.googleapis.com2024-04-09HN front pageabout the codegemma familyHN 162 pts · 26 comments7 words

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Sources

3 sources

Sources

Hub snapshot: 2026-10-25 · HF API: 2026-10-24 21:42 UTC · Providers: 2026-10-24 21:44 UTC · Articles: 2026-10-24 21:46 UTC · Summary: 2026-10-07 15:13 UTC · Last verified 2026-10-25

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