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
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 chat weights (safetensors, tokenizer, config)
Browse files- chat_template.jinja +1 -0
- config.json +51 -0
- generation_config.json +10 -0
- model.safetensors +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +8 -0
chat_template.jinja
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{% if messages[0]['role'] != 'system' %}<|system|>You are Zero Loop, an AI assistant created by Zero Labs. Your name is Zero Loop and your creator is Zero Labs. Think inside <think></think> before you answer. Tools: search (argument query) looks up Wikipedia, calculator (argument expression) evaluates arithmetic. To call a tool output <tool_call> then a JSON object with keys name and arguments then </tool_call>, and wait for the tool result.<|end|>{% endif %}{% for m in messages %}<|{{ m['role'] }}|>{{ m['content'] }}<|end|>{% endfor %}{% if add_generation_prompt %}<|assistant|>{{ '<think>\n' }}{% endif %}
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config.json
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{
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"architectures": [
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"Qwen3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": null,
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"dtype": "float32",
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"eos_token_id": 16379,
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"head_dim": 64,
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"hidden_act": "silu",
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"hidden_size": 256,
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"initializer_range": 0.02,
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"intermediate_size": 1024,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 4096,
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"max_window_layers": 28,
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"model_type": "qwen3",
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"num_attention_heads": 4,
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"num_hidden_layers": 16,
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"num_key_value_heads": 2,
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"pad_token_id": 16374,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 10000.0,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": true,
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"transformers_version": "5.18.0",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 16384
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}
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generation_config.json
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{
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"do_sample": true,
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"eos_token_id": 16379,
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"max_new_tokens": 512,
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"pad_token_id": 16374,
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"repetition_penalty": 1.1,
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"temperature": 0.4,
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"top_p": 0.9,
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"transformers_version": "5.18.0"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:bef531c0036c80f8efb5f24d6cbf4f3f0b6a343a8f10db36095c61c98070d2d2
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size 79753104
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tokenizer.json
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tokenizer_config.json
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{
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"backend": "tokenizers",
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|end|>",
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"model_max_length": 4096,
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"pad_token": "<|pad|>",
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"tokenizer_class": "TokenizersBackend"
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}
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