whatmodel.sh / OLMo 1B
OLMo 1B
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OLMo 1B is an open-weights language model from Allen Institute for AI, released February 2024. Its published size is 1.18B parameters and a 2k-token context window. Licence: apache-2.0.
Catalogue identity notes
OLMo 1B (Feb 2024, 3T tokens), main checkpoint of allenai/OLMo-1B; the -hf repo is AI2's Transformers-format conversion of the same weights.
$ how it compares
The LLMs ranked either side of OLMo 1B, out of 828 with a score, then more from Allen Institute for AI.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 789 |  Pythia 2.8B | 21±1 | local | ||
| 790 |  BLOOM 7.1B | 21±1 | local | ||
| 791 | OLMo 1B | 21±2 | local | ||
| 792 |  Phi-1 (1.3B) | 21±4 | local | ||
| 793 |  Qwen1.5 0.5B Chat | 21±3 | local | ||
| more from Allen Institute for AI | |||||
| 493 | OLMo 2 13B Instruct (1124) | 46±1 | local | ||
| 516 | Tulu 2 70B | 46±2 | local | ||
| 557 | Code Tulu 2 34B | 44±2 | local | ||
$ what it's good at
other tests8 not in a task · 99% of its score
| test | result | vs the others in its run | counts |
|---|---|---|---|
| ARC Challenge lab | 34.5% ✎ | ▃▃······▃▅▃█ beats 1/8 | 18% |
| WinoGrande lab | 58.9% ✎ | ▂····▃·▂·▂▂█ beats 1/8 | 15% |
| SciQ lab | 88.1% ✎ | ▂·▃···▂·▂·█▂ beats 1/8 | 14% |
| HellaSwag lab | 62.5% ✎ | ▂·····▃▂·▂·█ beats 1/8 | 13% |
| OpenBookQA lab | 46.4% ✎ | ▄·····▄▄▄██▄ beats 2/8 | 12% |
| ARC Easy lab | 58.1% ✎ | ▃···▃·▃··▃▅█ beats 1/8 | 11% |
| BoolQ lab | 60.7% ✎ | █····▃··█▃·▃ beats 0/8 | 10% |
| PIQA lab | 73.7% ✎ | ▄····▄·▄▄▄██ beats 1/8 | 6.0% |
$ where to run it
llama.cpp · recommended
version
- Install llama.cpp · Homebrew and winget installs name the commands llama-cli and llama-server instead.macOS / Linux
curl -LsSf https://llama.app/install.sh | shWindowsirm https://llama.app/install.ps1 | iex - Run OLMo 1B · needs ~0.7 GB · from huggingface.co/mradermacherchat in the terminal
llama cli -hf mradermacher/OLMo-1B-hf-GGUF:Q4_K_M - Or start a local server and open the address it prints (http://localhost:9931)
llama serve -hf mradermacher/OLMo-1B-hf-GGUF:Q4_K_M
LM Studio
version
- Install LM Studio · Open the app once before using the lms command.
- Run OLMo 1B · needs ~0.7 GB · from huggingface.co/mradermacher
- Or download it in the terminal
lms get https://huggingface.co/mradermacher/OLMo-1B-hf-GGUF@Q4_K_M -y - Then run lms ls to see its name, and lms chat followed by that name
Ollama
version
- Install Ollama
download it (macOS / Windows) ↗
Linuxcurl -fsSL https://ollama.com/install.sh | sh - Run OLMo 1B · needs ~0.7 GB · from huggingface.co/mradermacherchat in the terminal
ollama run hf.co/mradermacher/OLMo-1B-hf-GGUF:Q4_K_M
Other ways to run it
- Run it as published · needs ~4.71 GB · from huggingface.co/allenai
uv run --with torch --with 'transformers<5' python -c "from transformers import pipeline; print(pipeline('text-generation', model='allenai/OLMo-1B-hf')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
Weights and documentation: huggingface.co/allenai ↗
$ how the score was worked out · OLMo: Accelerating the Scienc… 99%
Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works
| source | results | share of the score |
|---|---|---|
 OLMo: Accelerating the Science of Language Models — authors' own runs · lab-reported | 8 | 99% |
| prior · pull toward the middle | 1% | |
| weighted average | = 21.0 |
$ improve this page
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data updated · sources and licences · json