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Allenai · model passport

Llama 3 1 Tulu 3 70b

llama-3-1-tulu-3-70b
  • Open weights
  • Llama 3.1 licence
  • 70.6B params
  • Released 2024-11-21(curated)
4/27axes covered
Overall · LMArena
1,286#281 of 396
Best percentile
31.1Instruction following
Cohort standing
37.8median 41.0

What the data says

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

Uneven: 4.3 percentile points between its best and worst axis — 31.1 pct on Instruction following versus 26.8 pct on Hard prompts.

  • Instruction following31.1 pct1,272Verify →
  • Overall29.3 pct1,286Verify →
  • Coding27.6 pct1,308Verify →
  • Hard prompts26.8 pct1,274Verify →
  • 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

Repositoryallenai/Llama-3.1-Tulu-3-70B
Parameters70.6B
ArchitectureLlamaForCausalLM
LicenceLlama 3.1

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "allenai/Llama-3.1-Tulu-3-70B"
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.

  • llama-3.1-tulu-3-70b stands at 37.8 (raw mean 28.7) — measured on 4 of 27 axes.

    standingtemplateVerify →

  • llama-3.1-tulu-3-70b stands at 37.8 versus a cohort median of 41 across 131 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 →