[whatmodel.sh](../../README.md) / Pythia 6.9B
# Pythia 6.9B
EleutherAI · released 2023-02 · 6.86B params · apache-2.0
**score 22.8** ±0.7 · #695 of 732 · from 37 benchmark results across 8 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Pythia 6.9B among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁●▃▅▇█▇▅▄▃▁ #460 of 469* 1.8% 1
math ▁▁●▅█▆▄█▄▂▁▁ #406 of 431* 2.0% 1
coding · #460 of 469 · 1.8% of its score
Beats [Molmo 7B-D (0924)](molmo-7b-d.md), [DeepSeek R1 Distill Qwen 1.5B](deepseek-r1-distill-qwen-1-5b.md), [Qwen3 0.6B](qwen3-0-6b.md) here, though they rank higher overall. Trails [Phi-1 (1.3B)](phi-1.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval 7.5% ▄······●·▄▄█ beats 1/5 medium · avg 59% 1.8% sharp · too hard for it
math · #406 of 431 · 2.0% of its score
Beats [Command R (03-2024)](command-r-03-2024.md), [Mistral 7B Instruct v0.3](mistral-7b-instruct-v0-3.md), [Gemma 2 2B IT](gemma-2-2b.md) here, though they rank higher overall. Trails [GPT-Neo 125M](gpt-neo-125m.md), [OPT-125M](opt-125m.md), [GPT-2 XL (1.5B)](gpt-2-xl.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K 3.5% ●···▄▄····▄▄ beats 1/5 easy · avg 72% 2.0% sharp · too hard for it
other tests (17)
test result vs the others in its run difficulty counts why
ARC Challenge 35.3% ▁·▁··▁▁●▂█▆▃ beats 9/26 easy · avg 82% 26% sharp · several runs
WinoGrande 60.6% ▁····▁··▁●█▄ beats 4/26 easy · avg 86% 12% several runs
ARC Easy 61.1% ▁▁·▁▁▄●▄█▅▁▃ beats 7/26 easy · avg 87% 9.4% blunt · at its level · several runs
HellaSwag 63.9% ▃···▁●▅█▇▃▁▁ beats 7/26 very easy · avg 91% 8.1% at its level · several runs
SciQ 89.8% ▁·▁·▁·●▅▃█▄▁ beats 5/26 very easy · avg 97% 7.2% blunt · several runs
OpenBookQA 25.8% █·●▁▃·▁▃▅▇▃▄ beats 6/26 very hard · avg 29% 6.2% too hard for it · small run · several r…
PIQA 75.1% ▁·▁··▁▁▃●▅█▂ beats 7/26 easy · avg 87% 5.6% blunt · at its level · several runs
BoolQ 64.0% ▂▃▂●▆▂▅▆█▂▂▂ beats 5/26 easy · avg 88% 5.4% at its level · several runs
ImageNetVC visual commonsense, te… 46.8% ▂·▄·▂█▄●█▄▂▆ beats 10/21 easy · avg 74% 3.6% blunt · at its level
LAMBADA (OpenAI version), last-wo… 67.0% ▁··▂▁▁▁▂●█▅▃ beats 9/26 very easy · avg 87% 3.1% blunt · at its level · several runs
BIG-Bench Hard 28.1% ●···▄··▄··▄▄ beats 0/5 easy · avg 74% 2.2% sharp · too hard for it
TriviaQA (maker) 15.1% ▁▁▂▂▄●▃▆▅█▅▂ beats 18/80 medium · avg 61% 2.1% small run
Factual knowledge recall, Premium… 11.0% ▅····●▅·██·█ beats 2/13 medium · avg 52% 1.3% typical
Factual knowledge recall, Premium… 21.7% ▃●▃··▅▃·█▃▃▅ beats 2/13 medium · avg 61% 1.2% typical
MMLU 26.2% ▄·●····▄·▄·█ beats 1/5 easy · avg 75% 1.0% noisy
Social IQA 40.7% ▁···▁●█▁▁▁·▁ beats 3/26 hard · avg 56% 0.9% blunt
Open PT-LLM Leaderboard average (… 29.0% ●▁▁▁▁▁▁▁▂█▅▁ beats 6/276 medium · avg 72% 0.6% noisy
source results share of the score
Stability AI 10 37% ▇▇▇▇
OLMo: Accelerating the Science of Langu… 8 19% ▇▇
Pythia: A Suite for Analyzing Large Lan… 6 19% ▇▇
trlX: A Framework for Large Scale Reinf… 5 11% ▇
How Far Can Camels Go? Exploring the St… 4 7% ▇
ImageNetVC: Zero- and Few-Shot Visual C… 1 4% ▇
Towards a Holistic Evaluation of LLMs o… 2 3% ▇
Open Portuguese LLM Leaderboard 1 1% ▇
prior: pull toward the middle 0% ▇
weighted average = 22.8
Licences: Stability AI ([Apache-2.0](../../licenses/stablelm-original-eval-json-Apache-2.0.txt)) · OLMo: Accelerating the Science of Language Models ([CC BY 4.0](../../licenses/paper-2402.00838v4-CC-BY-4.0.txt)) · Pythia: A Suite for Analyzing Large Language Models Across Training and Scaling ([CC BY-SA 4.0](../../licenses/paper-2304.01373v2-CC-BY-SA-4.0.txt)) · trlX: A Framework for Large Scale Reinforcement Learning from Human Feedback ([CC BY 4.0](../../licenses/acl-2023-emnlp-main-530-CC-BY-4-0.txt)) · How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources ([CC BY 4.0](../../licenses/paper-2306.04751v2-tulu1-CC-BY-4-0.txt)) · ImageNetVC: Zero- and Few-Shot Visual Commonsense Evaluation on 1000 ImageNet Categories ([CC BY 4.0](../../licenses/acl-2023-findings-emnlp-133-CC-BY-4-0.txt)) · Towards a Holistic Evaluation of LLMs on Factual Knowledge Recall ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt))
## How to run it
### llama.cpp (recommended)
1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md)
```sh
# macOS / Linux
curl -LsSf https://llama.app/install.sh | sh
# Windows
irm https://llama.app/install.ps1 | iex
```
2. Run Pythia 6.9B (Q4_K_M, needs ~4.4 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/pythia-6.9b-GGUF/tree/d8e5050a875b7bfd5de881d30b33abc3d492cb60))
```sh
llama cli -hf mradermacher/pythia-6.9b-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf mradermacher/pythia-6.9b-GGUF:Q4_K_M
```
Other versions: Q8_0 (~7.3 GB) `llama cli -hf mradermacher/pythia-6.9b-GGUF:Q8_0`
### LM Studio
1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download)
2. Run Pythia 6.9B (Q4_K_M, needs ~4.4 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/pythia-6.9b-GGUF/tree/d8e5050a875b7bfd5de881d30b33abc3d492cb60))
```sh
lms get https://huggingface.co/mradermacher/pythia-6.9b-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q8_0 (~7.3 GB) `lms get https://huggingface.co/mradermacher/pythia-6.9b-GGUF@Q8_0 -y`
### Ollama
1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download)
```sh
# Linux
curl -fsSL https://ollama.com/install.sh | sh
```
2. Run Pythia 6.9B (Q4_K_M, needs ~4.4 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/pythia-6.9b-GGUF/tree/d8e5050a875b7bfd5de881d30b33abc3d492cb60))
```sh
ollama run hf.co/mradermacher/pythia-6.9b-GGUF:Q4_K_M
```
Other versions: Q8_0 (~7.3 GB) `ollama run hf.co/mradermacher/pythia-6.9b-GGUF:Q8_0`
### Other ways to run it
1. Run it as published (fp16, needs ~13.85 GB, from [huggingface.co/EleutherAI](https://huggingface.co/EleutherAI/pythia-6.9b/tree/c0e3eee36dc47af0c49f361c74cfe459c09f7f23))
```sh
uv run --with torch --with 'transformers<5' --with accelerate python -c "from transformers import pipeline; print(pipeline('text-generation', model='EleutherAI/pythia-6.9b', torch_dtype='auto', device_map='auto')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
```
Model repository and documentation: [huggingface.co/EleutherAI](https://huggingface.co/EleutherAI/pythia-6.9b)
[interactive version](https://whatmodel.sh/model/pythia-6-9b) · [JSON](https://whatmodel.sh/api?format=json&all=1)