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Gemma 2b It

gemma-2b-it
  • Open weights
  • Gemma license licence
  • 2.5B params
  • Released 2024-02-21(curated)
4/27axes covered
Overall · LMArena
1,094#381 of 396
Best percentile
5.1Hard prompts
Cohort standing
24.1median 31.5

What the data says

Benchmarked on 4 of 27 tracked axes. Every figure below is the model's own published result on that board.

Weakest axes

  • 4 pct — Overall (1,094)
  • 4.5 pct — Instruction following (1,071)
  • 4.9 pct — Coding (1,136)

Uneven: 1.1 percentile points between its best and worst axis — 5.1 pct on Hard prompts versus 4 pct on Overall.

  • Hard prompts5.1 pct1,112Verify →
  • Coding4.9 pct1,136Verify →
  • Instruction following4.5 pct1,071Verify →
  • Overall4 pct1,094Verify →
  • WebDev ArenaNot benchmarked
  • Vision ArenaNot benchmarked
  • Factuality ArenaNot benchmarked
  • Search ArenaNot benchmarked
  • Document ArenaNot benchmarked
  • Agent ArenaNot benchmarked
  • GPQA DiamondNot benchmarked
  • AIME (math)Not benchmarked
  • FrontierMathNot benchmarked
  • SimpleQA VerifiedNot benchmarked
  • ARC-AGI-2Not benchmarked
  • Humanity's Last ExamNot benchmarked
  • SimpleBenchNot benchmarked
  • LiveBenchNot benchmarked
  • BFCL v4 (function calling)Not benchmarked
  • SWE-bench Verified (live board)Not benchmarked
  • Terminal-Bench 4.0Not benchmarked
  • MMLU-Pro (Artificial Analysis)Not benchmarked
  • LiveCodeBench (Artificial Analysis)Not benchmarked
  • IFBench (Artificial Analysis)Not benchmarked
  • HHEM Hallucination (Vectara)Not benchmarked
  • SWE-bench VerifiedNot benchmarked
  • Terminal-BenchNot benchmarked

Percentiles are the model's standing within each board's own population. Axes marked "not benchmarked" have no published result for this model — that is an absence of data, not a low score.

How to run it

Run it yourself

Repositorygoogle/gemma-2b-it
Parameters2.5B
ArchitectureGemmaForCausalLM
LicenceGemma license
AccessGated — you must accept the repo's terms before downloading

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "google/gemma-2b-it"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

Ollama

We don't sync Ollama tags, so we won't print a command we can't verify — a wrong one would pull the wrong model. Search the Ollama library →

Unsloth fine-tuning recipes are model-specific and we don't derive them here. Unsloth docs →

Identifiers, limits and prices are synced from the upstream source shown above. Any field we haven't synced is left out entirely rather than guessed.

  • gemma-2b-it stands at 24.1 (raw mean 4.6) — measured on 4 of 27 axes.

    standingtemplateVerify →

  • gemma-2b-it stands at 24.1 versus a cohort median of 31.5 across 97 models released within 180 days — below the peer median.

    cohorttemplateVerify →

Every claim is a computed fact from the current board data, phrased by a low-temperature model call and rejected by a validation gate if any number is not in the source row. How the leaderboards work →