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 / agent.py

ZEROLABS1

Upload agent.py with huggingface_hub

db50de9 verified 25 days ago

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import sys
from llama_cpp import Llama          # pip install llama-cpp-python
from tools import parse_call, run_tool
llm = Llama(model_path=sys.argv[1], n_ctx=2048, verbose=False)
msgs = []
while True:
    q = input("you> ").strip()
    if not q: break
    msgs.append({"role": "user", "content": q})
    for _ in range(3):
        out = llm.create_chat_completion(messages=msgs, max_tokens=700, temperature=0.6, top_p=0.95)["choices"][0]["message"]["content"]
        if "<think>" not in out: out = "<think>\n" + out      # the chat template already opened the think block
        msgs.append({"role": "assistant", "content": out})
        call = parse_call(out)
        if not call: break
        try: res = run_tool(call["name"], call["arguments"])
        except Exception as e: res = "error: " + str(e)
        msgs.append({"role": "tool", "content": res})
    print(out.split("</think>")[-1].strip())

	import sys
	from llama_cpp import Llama # pip install llama-cpp-python
	from tools import parse_call, run_tool
	llm = Llama(model_path=sys.argv[1], n_ctx=2048, verbose=False)
	msgs = []
	while True:
	q = input("you> ").strip()
	if not q: break
	msgs.append({"role": "user", "content": q})
	for _ in range(3):
	out = llm.create_chat_completion(messages=msgs, max_tokens=700, temperature=0.6, top_p=0.95)["choices"][0]["message"]["content"]
	if "<think>" not in out: out = "<think>\n" + out # the chat template already opened the think block
	msgs.append({"role": "assistant", "content": out})
	call = parse_call(out)
	if not call: break
	try: res = run_tool(call["name"], call["arguments"])
	except Exception as e: res = "error: " + str(e)
	msgs.append({"role": "tool", "content": res})
	print(out.split("</think>")[-1].strip())