Instructions to use openamer-max/openamer-max-watch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use openamer-max/openamer-max-watch 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 openamer-max/openamer-max-watch
# Run inference directly in the terminal:
llama cli -hf openamer-max/openamer-max-watch

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf openamer-max/openamer-max-watch
# Run inference directly in the terminal:
llama cli -hf openamer-max/openamer-max-watch

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 openamer-max/openamer-max-watch
# Run inference directly in the terminal:
./build/bin/llama cli -hf openamer-max/openamer-max-watch

Use Docker

docker model run hf.co/openamer-max/openamer-max-watch

Llama new
LM Studio
Jan

vLLM

How to use openamer-max/openamer-max-watch with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/openamer-max/openamer-max-watch

Ollama
How to use openamer-max/openamer-max-watch with Ollama:
```
ollama run hf.co/openamer-max/openamer-max-watch
```
Unsloth Desktop

How to use openamer-max/openamer-max-watch with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf openamer-max/openamer-max-watch

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "openamer-max/openamer-max-watch"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use openamer-max/openamer-max-watch with Docker Model Runner:
```
docker model run hf.co/openamer-max/openamer-max-watch
```

Lemonade

How to use openamer-max/openamer-max-watch with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull openamer-max/openamer-max-watch

Run and chat with the model

lemonade run user.openamer-max-watch-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use openamer-max/openamer-max-watch with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf openamer-max/openamer-max-watch

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default openamer-max/openamer-max-watch

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use openamer-max/openamer-max-watch with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf openamer-max/openamer-max-watch

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "openamer-max/openamer-max-watch" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

OpenAmer-Max-Watch

The smallest tier of the OpenAmer-Max family — a 0.6B assistant that runs offline on a device, answers in 0.04–0.38 s, and admits when it does not know something.

639 MB · Q8_0 GGUF · Apache-2.0 · runs on CPU, no GPU needed

Why this model exists

Most assistants grow upwards. This one goes downwards: a model small enough for a smartwatch, a phone with 2 GB free RAM, or a Raspberry Pi — that still stays honest about the limits of what it knows.

It is the anchor of a size ladder. Every tier is named after its hardware target:

Tier	Parameters	Target
`OpenAmer-Max-Watch`	≤ 1B	smartwatch · this model: 0.6B
`OpenAmer-Max-Phone`	1–5B	smartphone
`OpenAmer-Max-Tab`	5–10B	tablet
`OpenAmer-Max-Desktop`	10–20B	desktop PC
`OpenAmer-Max-Pro`	20–30B	pro workstation
`OpenAmer-Max-Ultra`	30–100B	large server
`OpenAmer-Max-Mega`	100–500B	cluster
`OpenAmer-Max-Giga`	500B–1T	giga
`OpenAmer-Max-Tera`	1T–10T	tera

Measured results (not estimates)

Every number below was produced on the released build, on an RTX 5060 Laptop (8 GB). Raw logs are in the repository: proof-frozen-23.json, proof-token-accuracy.json.

Test	Result
Frozen benchmark, 23 unseen cases (facts, paraphrases, refusals, Bosnian, long, short, edge)	23/23 — 100 %
Answer-token accuracy (held-out split, 4 536 answer tokens)	99.63 %
Fresh test, 9 questions never in any training set	8/9
Shipped-form probe (16 questions through Ollama, `think:false`)	15/16
Latency per answer	0.04 – 0.38 s

Category detail on the frozen set: facts 7/7 · paraphrases 3/3 · refusals 5/5 · Bosnian 3/3 · Bosnian refusals 1/1 · long 1/1 · short 1/1 · edge 2/2.

No test-set contamination. 68 training rows that contained a benchmark question verbatim were detected and removed automatically (purge_testset). Without that step the score would have looked the same but would have measured memorisation instead of transfer. The number above is the cleaned one.

Arithmetic	Answer
17 × 23	391 ✅
45 × 12	540 ✅
21 × 19	399 ✅

What it does well

Identifies itself correctly and consistently
Answers product facts from a supplied context (RAG) without inventing
Refuses honestly: "Das weiss ich nicht" for unknowns — verified, not hoped for
Names its own boundaries (no music generation, no video from nothing, no telephony)
One-word answers and short lists, German and Bosnian
Small arithmetic, instantly

What it does not do — read this before you use it

Limitation	Example
No free-form German prose	asked to write a sentence about dogs: "Ein Hund atmet, wobei die Luft kalt und schwindy."
No general knowledge	capital of Italy → "Genoa."
No 4-digit multiplication	86 × 83 → 7118 (correct: 7138)
Bosnian only for direct phrasings	unseen Bosnian wordings degrade, especially after quantisation
Needs thinking mode off	without `think:false` Qwen3 spends every token inside `thinking` and never answers

This is a 0.6B model. It is a reliable product-identity and short-answer layer, not a chatbot for essays. Treat it as a component, not as an oracle.

Quick start (Ollama)

# 1. download
curl -L -o openamer-max-watch.gguf \
  https://huggingface.co/openamer-max/openamer-max-watch/resolve/main/openamer-max-watch-q8.gguf

# 2. register
cat > Modelfile <<'EOF'
FROM ./openamer-max-watch.gguf
PARAMETER temperature 0.6
PARAMETER num_ctx 4096
PARAMETER num_predict 96
PARAMETER stop "<|im_end|>"
PARAMETER stop "<|im_end|>"
SYSTEM """Du bist OpenAmer, ein persoenlicher KI-Assistent von Amer Sacirovic aus der Schweiz.
Du laeufst lokal auf dem Geraet des Nutzers, ohne Cloud und ohne Telemetrie.
Antworte kurz: ein Satz, hoechstens zwei."""
EOF
ollama create OpenAmer-Max-Watch -f Modelfile

# 3. run — think:false is REQUIRED
curl -s http://127.0.0.1:11434/api/chat -d '{
  "model": "OpenAmer-Max-Watch",
  "messages": [{"role":"user","content":"Was ist OpenAmer?"}],
  "stream": false,
  "think": false,
  "options": {"temperature": 0}
}' | jq -r .message.content

Output:

Ein persoenlicher KI-Assistent, der lokal auf dem Geraet laeuft und Aufgaben ausfuehrt. Entwickelt von Amer Sacirovic in der Schweiz.

llama.cpp users: llama-cli -m openamer-max-watch-q8.gguf -p "Was ist OpenAmer?" -n 96 --temp 0

How it was trained — full transparency


Base	`Qwen/Qwen3-0.6B` (Apache-2.0)
Method	LoRA r=32, α=64, all 7 projections — 20.19 M trainable (3.28 %)
Data	1 873 self-generated examples, 10 categories, 0 contradictory prompts
Stages	4 — fresh start, then 3 continuations from its own previous adapter
Compute	124 steps per stage, ~5 min, 3.4 GB VRAM, bf16, no quantisation
Release	merged → GGUF f16 → Q8_0 (639 MB)

Three findings that shaped the result, documented so others can reuse them:

Contradictory labels were the main defect. A helper that shortened answers turned "Ja. OpenAmer kann Webseiten abrufen…" into a bare "Ja." — same prompt, two different answers. 540 of 1 696 prompts were affected. Fixing this alone moved token accuracy from 94.9 % to 99.2 %.
Contamination is invisible unless you look for it. 68 training rows overlapped the benchmark verbatim. A purge step now runs on every generation and reports the count.
Quantisation quality is not monotone in bits. For one intermediate adapter Q8_0 answered 401 instead of 391, while Q6_K was correct; for the final adapter Q8_0 is the better one. Each release is therefore checked against its own bf16 reference.

The stage-by-stage path: 20/23 → 22/23 → 23/23 → 23/23, token accuracy 99.08 % → 99.45 % → 99.48 % → 99.63 %. Each stage targeted one measured failure class, not "more data".

Files

File	Size	Notes
`openamer-max-watch-q8.gguf`	639 MB	recommended, released build
`openamer-max-watch-q6.gguf`	495 MB	smaller, slightly degraded Bosnian
`Modelfile`	2 KB	system prompt + parameters
`paypal-qr.png`	68 KB	support this project

Support this project

OpenAmer is built by one person, without venture capital, on a laptop with 8 GB of VRAM. Every tier of the ladder above is trained on that machine or on borrowed cloud GPUs.

If this model is useful to you — as a product component, as a research baseline, or as an example of small-model engineering — you can fund the next tier:

PayPal: https://www.paypal.com/qrcodes/managed/bfb5b6b5-c04c-4836-af60-987dff8cff61

A ⭐ on GitHub helps as much as a donation: it is what makes sponsors and accelerators look at a project in the first place.

Citation

@misc{openamer_max_watch_2026,
  title  = {OpenAmer-Max-Watch: a 0.6B on-device assistant tier},
  author = {Sacirovic, Amer},
  year   = {2026},
  note   = {OpenAmer-Max model family, Watch tier (<=1B)},
  url    = {https://huggingface.co/openamer-max/openamer-max-watch}
}

Deutsch

OpenAmer-Max-Watch ist die kleinste Stufe der OpenAmer-Max-Familie: ein 0,6B-Modell, das offline auf einem Gerät läuft, in 0,04–0,38 s antwortet und zugibt, wenn es etwas nicht weiß.

639 MB (Q8_0), läuft auf CPU, keine GPU nötig
23/23 auf einem eingefrorenen, kontaminationsfreien Prüfstand
99,63 % Antwort-Token-Genauigkeit, 8/9 bei nie trainierten Fragen
Grenzen werden benannt: keine Musik, kein Video, keine Telefonie, kein Allgemeinwissen

Wichtig im Betrieb: think: false setzen. Ohne das verbraucht Qwen3 alle Tokens im Denkblock und antwortet nie.

Gebaut von Amer Sacirovic in der Schweiz, ohne Konzern, auf einem Laptop mit 8 GB VRAM. Unterstützung für die nächste Stufe (OpenAmer-Max-Phone) über den PayPal-QR-Code oben.

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GGUF

Model size

0.6B params

Architecture

qwen3

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for openamer-max/openamer-max-watch

Base model

Qwen/Qwen3-0.6B-Base

Finetuned

Qwen/Qwen3-0.6B

Quantized

(547)

this model