whatmodel.sh / Pythia 70M
Pythia 70M
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Pythia 70M is an open-weights language model from EleutherAI, released February 2023. Its published size is 0.0704B parameters and a 2k-token context window. Licence: apache-2.0.
Catalogue identity notes
Final (step 143000) checkpoint of the standard (non-deduplicated) Pythia model, repo main branch. Total parameter count from the Pythia model card.
$ how it compares
The LLMs ranked either side of Pythia 70M, out of 828 with a score, then more from EleutherAI.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 824 |  OPT-125M | 3±2 | local | ||
| 825 |  GPT-2 Large (774M) | 1±2 | local | ||
| 826 | GPT-2 Medium (355M) | -1±2 | local | ||
| 827 | Pythia 70M | -7±2 | local | ||
| 828 | GPT-2 Small (124M) | -9±3 | local | ||
| more from EleutherAI | |||||
| 756 | GPT-NeoX-20B | 28±1 | local | ||
| 777 | Pythia 12B | 25±1 | local | ||
| 780 | GPT-J 6B | 25±1 | local | ||
$ what it's good at
other tests7 not in a task · 98% of its score
| test | result | vs the others in its run | counts |
|---|---|---|---|
| SciQ lab | 60.1% ✎ | ▃··▂▃▂▂▆▃▃█▂ beats 0/19 | 45% |
| ARC Easy lab | 37.4% ✎ | ▃·▅▃▃▅·██▃██ beats 0/19 | 21% |
| LAMBADA (OpenAI version), last-word accuracy lab | 18.5% ✎ | ▃··▅▃▃▅▅▅███ beats 0/19 | 14% |
| PIQA lab | 59.5% ✎ | ▃·▄▄·▄▂▆▂▄█▄ beats 0/19 | 13% |
| WinoGrande lab | 52.8% ✎ | ▄····▄▆▂▄▆▆█ beats 3/19 | 3.9% |
| ARC Challenge lab | 18.1% ✎ | █··▁·▁▁·▁▂▁▂ beats 9/19 | 1.0% |
| Open PT-LLM Leaderboard average (9 Portuguese tasks) | 23.2% ✎ | ▃▁▁▁▁▁▁▁▂█▅▁ beats 5/276 | 0.4% |
$ 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 Pythia 70M · needs ~0.05 GB · from huggingface.co/mradermacherchat in the terminal
llama cli -hf mradermacher/pythia-70m-GGUF:Q4_K_M - Or start a local server and open the address it prints (http://localhost:9931)
llama serve -hf mradermacher/pythia-70m-GGUF:Q4_K_M
LM Studio
version
- Install LM Studio · Open the app once before using the lms command.
- Run Pythia 70M · needs ~0.05 GB · from huggingface.co/mradermacher
- Or download it in the terminal
lms get https://huggingface.co/mradermacher/pythia-70m-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 Pythia 70M · needs ~0.05 GB · from huggingface.co/mradermacherchat in the terminal
ollama run hf.co/mradermacher/pythia-70m-GGUF:Q4_K_M
Other ways to run it
- Run it as published · needs ~0.17 GB · from huggingface.co/EleutherAI (official)
uv run --with torch --with 'transformers<5' python -c "from transformers import pipeline; print(pipeline('text-generation', model='EleutherAI/pythia-70m')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
Official weights and documentation: huggingface.co/EleutherAI ↗
$ how the score was worked out · Pythia: A Suite for Analyzing… 98%, Open Portuguese LLM Leaderboa… 0%
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 |
|---|---|---|
 Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling — authors' own runs · lab-reported | 6 | 98% |
 Open Portuguese LLM Leaderboard (archived) | 1 | 0% |
| prior · pull toward the middle | 2% | |
| weighted average | = -7.2 |
$ improve this page
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data updated · sources and licences · json