Instructions to use ZEROLABS1/zero-loop with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ZEROLABS1/zero-loop with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ZEROLABS1/zero-loop:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ZEROLABS1/zero-loop:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ZEROLABS1/zero-loop:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ZEROLABS1/zero-loop:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf ZEROLABS1/zero-loop:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ZEROLABS1/zero-loop:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf ZEROLABS1/zero-loop:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ZEROLABS1/zero-loop:Q4_K_M

Use Docker

docker model run hf.co/ZEROLABS1/zero-loop:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use ZEROLABS1/zero-loop with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ZEROLABS1/zero-loop"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ZEROLABS1/zero-loop",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ZEROLABS1/zero-loop:Q4_K_M

Ollama
How to use ZEROLABS1/zero-loop with Ollama:
```
ollama run hf.co/ZEROLABS1/zero-loop:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use ZEROLABS1/zero-loop with Docker Model Runner:
```
docker model run hf.co/ZEROLABS1/zero-loop:Q4_K_M
```

Lemonade

How to use ZEROLABS1/zero-loop with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ZEROLABS1/zero-loop:Q4_K_M

Run and chat with the model

lemonade run user.zero-loop-Q4_K_M

List all available models

lemonade list

Atomic Chat

zero-loop / benchmarks.md

ZEROLABS1

ZERO LOOP benchmarks table

e6b76e4 verified 25 days ago

preview code

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1.96 kB

	ZERO LOOP	ZERO LOOP Q4_K_M	SmolLM2-135M-Instruct	SmolLM-135M-Instruct
Size And Speed
Parameters	19.9M	19.9M	134.5M	134.5M
Download size	40 MB	14 MB	269 MB	269 MB
CPU speed (tokens/s, 4 threads)	40	464	18	18
Tool Use
ZL-Calc: calculator tool	0	0	6	0
ZL-Calc-Noisy: typos, lower-case, ×	0	0	0	0
ZL-Word: GSM8K problems + calculator	0	3	1	0
ZL-Search: TriviaQA + Wikipedia tool	3	5	16	20
Valid tool call on the first try	0	0	16	4
Assistant Behaviour
ZL-Identity: name and creator	92	85	46	31

ZERO LOOP is ahead of every 134M-class baseline on 1 of 6 benchmarks and tied on 1.

_{Same machine, greedy decoding and the same tool protocol for every model. The baselines were not trained on the protocol, so they get it in the system prompt plus two worked examples; ZERO LOOP uses its built-in prompt. Identity: baselines are told their name and creator in the system prompt. ZL-Calc / ZL-Calc-Noisy: 100 arithmetic questions with unseen numbers (the noisy set is lower-cased, with a typo and a multiplication sign). ZL-Word: 100 GSM8K test word problems, the model may call the calculator. ZL-Search: 100 TriviaQA validation questions, live Wikipedia search, correct if the final answer contains a gold alias. Valid tool call: the first reply to an arithmetic question is a parseable call. These are our own small tests of the skills ZERO LOOP is built for (100 questions each: differences under ~10 points are noise). They say nothing about general ability: on MMLU, HellaSwag or ARC the larger models trained on far more data score higher. The Q4_K_M column runs the shipped GGUF in llama.cpp; its speed comes from llama.cpp, the other columns from Transformers fp32, so speed is shown for it but not compared.}