Allenai · model passport
Olmo 7b Instruct
olmo-7b-instruct- Open weights
- Apache-2.0 licence
- 6.9B params
- Released 2024-02-01(curated)
- Overall · LMArena
- 1,074#383 of 396
- Best percentile
- 3.6Coding
- Cohort standing
- 23.4median 30.1
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
- 3 pct — Instruction following (1,029)
- 3.5 pct — Hard prompts (1,069)
- 3.5 pct — Overall (1,074)
Uneven: 0.6 percentile points between its best and worst axis — 3.6 pct on Coding versus 3 pct on Instruction following.
- Coding3.6 pct1,107Verify →
- Overall3.5 pct1,074Verify →
- Hard prompts3.5 pct1,069Verify →
- Instruction following3 pct1,029Verify →
- 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
Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "allenai/OLMo-7B-Instruct"
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.
olmo-7b-instruct stands at 23.4 (raw mean 3.4) — measured on 4 of 27 axes.
olmo-7b-instruct stands at 23.4 versus a cohort median of 30.1 across 91 models released within 180 days — below the peer median.
olmo-7b-instruct scores in the 3rd percentile on Instruction following.
olmo-7b-instruct scores at percentile 3.5 on Overall.
olmo-7b-instruct ranks between #398 and #853 across 5 built-in weightings — best under "Conversation-first", worst under "All sources".
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 →