[whatmodel.sh](../../README.md) / BLOOM 1.1B
# BLOOM 1.1B
BigScience · released 2022-07 · 1.07B params · bigscience-bloom-rail-1.0
**score 6.9** ±1.1 · #721 of 732 · from 36 benchmark results across 6 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks BLOOM 1.1B among every model on that task or test, worst to best.
task vs the others rank counts tests
coding █··●·█·██··█ #5 of 6* 3.0% 1
math ▁●▃▅█▆▄█▄▂▁▁ #425 of 431* 1.4% 1
coding · #5 of 6 · 3.0% of its score
test result vs the others in its run difficulty counts why
HumanEval (maker) 2.5% ▄···●·▄·█··▄ beats 1/5 easy · avg 79% 3.0% sharp · too hard for it
math · #425 of 431 · 1.4% of its score
Beats [Flan-T5 Base](flan-t5-base.md), [Flan-T5 Small](flan-t5-small.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 0.8% ▄●█▄···▄·▄·▄ beats 1/7 easy · avg 65% 1.4% sharp · too hard for it
other tests (21)
test result vs the others in its run difficulty counts why
ARC Easy 51.5% ▃·▅▃▃●·██▃██ beats 5/19 easy · avg 87% 19% blunt · at its level · several runs
PIQA 67.2% ▂·▄▄·●▂▆▂▄█▄ beats 6/19 easy · avg 87% 12% blunt · at its level · several runs
WinoGrande 54.7% ▄····▄▆●▄▆▆█ beats 7/19 easy · avg 88% 8.8% several runs
LAMBADA (OpenAI version), last-wo… 42.6% ▃··▅▃●▅▅▅███ beats 4/19 very easy · avg 87% 7.9% blunt · at its level · several runs
ARC Challenge 23.6% ●··▁·▁▁·▁▂▁▂ beats 4/19 easy · avg 81% 7.6% sharp · too hard for it · several runs
SciQ 83.3% ▂··▂▃▂▂●▃▃█▂ beats 6/19 very easy · avg 98% 7.0% blunt · at its level
Story Cloze Test 63.3% ▂▂●▂·▂·▆·▂█▂ beats 3/14 easy · avg 89% 5.0% blunt · at its level · several runs
COPA (maker) 57.0% ▃··█●▃▃▅·▃▃▃ beats 4/12 very easy · avg 95% 5.0% at its level · several runs
TriviaQA 8.6% ▁··▁●▃▃▁▄▅▄█ beats 2/28 medium · avg 45% 4.5% too hard for it · several runs
HellaSwag 34.6% ▄·●▄·▄·▄··▄█ beats 1/7 easy · avg 81% 4.3% typical
CoQA 45.6% ▄····●▄·▄▄·█ beats 1/6 very easy · avg 92% 3.8% at its level
Winograd Schema Challenge (WSC273) 68.5% ▄●·▄·▄··█··█ beats 1/7 very easy · avg 93% 3.1% blunt · at its level
Natural Questions (closed book) 2.9% ▁·▁·●▂▁▂·▅▂█ beats 2/28 medium · avg 48% 1.9% too hard for it
BoolQ 59.1% ▄··●·█▄··█·▄ beats 1/7 easy · avg 87% 1.6% typical
RACE-high 32.6% ▄···●··▄▄··█ beats 1/6 medium · avg 51% 1.4% blunt
DROP 8.6% ▄▄···●·▄·▄·▄ beats 2/6 hard · avg 34% 1.2% blunt
OpenBookQA 19.6% ●········▂▂▂ beats 3/7 hard · avg 40% 0.7% too hard for it
TruthfulQA MC2 41.8% ▅▃●▆▁▂▅···▁▁ beats 13/31 medium · avg 57% 0.6% blunt · at its level · several runs
WebQuestions (closed book) 1.4% █···▄··▄●▄·▄ beats 4/6 very hard · avg 3.5% 0.1% blunt · too hard for it
HaluEval QA hallucination detecti… 44.9% ●▁▁·▁▁·▁▁▁▁▁ beats 11/27 hard · avg 66% 0.0% too hard for it · noisy
LogiQA (harness) 18.9% ●··········▁ beats 12/14 very hard · avg 25% 0.0% too hard for it · noisy
source results share of the score
EleutherAI lm-evaluation-harness result… 17 42% ▇▇▇▇▇
Pythia: A Suite for Analyzing Large Lan… 6 29% ▇▇▇▇
GPTQ: Accurate Post-Training Quantizati… 5 21% ▇▇▇
BLOOM: A 176B-Parameter Open-Access Mul… 1 3% ▇
Hallucinations Leaderboard results 4 3% ▇
Crosslingual Generalization through Mul… 3 1% ▇
prior: pull toward the middle 1% ▇
weighted average = 6.9
Licences: EleutherAI lm-evaluation-harness results folder ([MIT](../../licenses/lm-eval-harness-results-2023-05-MIT.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)) · GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers ([CC BY 4.0](../../licenses/paper-2210.17323v2-gptq-CC-BY-4-0.txt)) · BLOOM: A 176B-Parameter Open-Access Multilingual Language Model ([CC BY 4.0](../../licenses/paper-2211.05100v4-CC-BY-4.0.txt)) · Hallucinations Leaderboard results ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) · Crosslingual Generalization through Multitask Finetuning ([CC BY 4.0](../../licenses/acl-2023-acl-long-891-CC-BY-4-0.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 BLOOM 1.1B (Q4_K_M, needs ~1 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/bloom-1b1-GGUF/tree/104390d014122ffb562167df2f0c18bbf8f09f73))
```sh
llama cli -hf mradermacher/bloom-1b1-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf mradermacher/bloom-1b1-GGUF:Q4_K_M
```
Other versions: Q8_0 (~1.6 GB) `llama cli -hf mradermacher/bloom-1b1-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 BLOOM 1.1B (Q4_K_M, needs ~1 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/bloom-1b1-GGUF/tree/104390d014122ffb562167df2f0c18bbf8f09f73))
```sh
lms get https://huggingface.co/mradermacher/bloom-1b1-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 (~1.6 GB) `lms get https://huggingface.co/mradermacher/bloom-1b1-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 BLOOM 1.1B (Q4_K_M, needs ~1 GB, from [huggingface.co/mradermacher](https://huggingface.co/mradermacher/bloom-1b1-GGUF/tree/104390d014122ffb562167df2f0c18bbf8f09f73))
```sh
ollama run hf.co/mradermacher/bloom-1b1-GGUF:Q4_K_M
```
Other versions: Q8_0 (~1.6 GB) `ollama run hf.co/mradermacher/bloom-1b1-GGUF:Q8_0`
### Other ways to run it
1. Run it as published (fp16, needs ~2.13 GB, from [huggingface.co/bigscience](https://huggingface.co/bigscience/bloom-1b1/tree/eb3dd7399312f5f94fd13f41d2f318117d3eb1e4))
```sh
uv run --with torch --with 'transformers<5' python -c "from transformers import pipeline; print(pipeline('text-generation', model='bigscience/bloom-1b1')('Once upon a time', max_new_tokens=40)[0]['generated_text'])"
```
Model repository and documentation: [huggingface.co/bigscience](https://huggingface.co/bigscience/bloom-1b1)
[interactive version](https://whatmodel.sh/model/bloom-1b1) · [JSON](https://whatmodel.sh/api?format=json&all=1)