Text Generation
GGUF
Safetensors
English
qwen3
zero-loop
zero-labs
foundation-model
small-language-model
on-device
edge
tool-use
reasoning
llama.cpp
conversational
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
- llama.cpp
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
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -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
File size: 1,961 Bytes
e6b76e4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | | | 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.
<sub>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.</sub>
|