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

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-tab
# Run inference directly in the terminal:
llama cli -hf openamer-max/openamer-max-tab

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

Use Docker

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

Llama new
LM Studio
Jan

vLLM

How to use openamer-max/openamer-max-tab 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-tab"
# 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-tab",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

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

How to use openamer-max/openamer-max-tab 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-tab

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-tab"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

How to use openamer-max/openamer-max-tab 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-tab

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-tab

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use openamer-max/openamer-max-tab 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-tab

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-tab" \
  --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-Tab

Third tier of the OpenAmer-Max family — a 7.62B assistant for tablets and mid-range machines. Runs offline, answers in under a second, knows its limits.

4,4 GB · Q4_K_M GGUF · Apache-2.0 · 16k context

The family

Every tier is named after its hardware target, not after marketing:

Tier	Parameters	Target	Status
`OpenAmer-Max-Watch`	≤ 1B	smartwatch (0.6B, 639 MB)	released
`OpenAmer-Max-Phone`	1–5B	smartphone (3.09B, 2.5 GB)	released
`OpenAmer-Max-Tab`	5–10B	tablet — this model: 7.62B	this page
`OpenAmer-Max-Desktop`	10–20B	desktop PC	in progress
`OpenAmer-Max-Pro` … `Tera`	20B–10T	workstation → cluster	planned

Measured results

Test	Result
Frozen benchmark, 23 unseen cases (contamination-free)	22/23 — 95,7 %
Categories	facts 7/7 · paraphrases 3/3 · refusals 5/5 · Bosnian 2/3 · Bosnian refusals 1/1 · long 1/1 · short 1/1 · edge 2/2
Fresh test, 12 questions never in any training set	12/12 (content-verified)
Latency	0.2 – 1.1 s on CPU
Size	4,7 GB in Ollama (Q4_K_M), 14,5 GB as f16

What it does well

Correct product facts, and honest refusals when the answer is not in context
Correct negation polarity: "Auf dem iPhone. OpenAmer läuft auf einem Android-Telefon…"
Documents (Word, Excel, PDF), code, arithmetic, summarising — the 7B base keeps general ability that the smaller tiers lose
German and Bosnian, 16k context for longer documents

What it does not do

Limitation	Detail
Same weak Bosnian phrasing as the smaller tiers	"Mogu li koristiti OpenAmer bez interneta?" answers correctly in substance, but does not use the words "internet"/"mreža" — the one benchmark case it misses
No 4-digit multiplication guarantee	check before trusting arithmetic
Not a substitute for a large model	7.62B, not 70B — long reasoning chains stay shaky

Quick start (Ollama)

curl -L -o openamer-max-tab-q4.gguf \
  https://huggingface.co/openamer-max/openamer-max-tab/resolve/main/openamer-max-tab-q4.gguf

ollama create OpenAmer-Max-Tab -f Modelfile   # ships with this repo

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

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

Important: keep repeat_penalty at 1.0. With 1.1 the model flipped a negation in the Phone tier ("does not run on Android" instead of "iPhone") — the penalty punishes exactly the words a negation needs twice.

How this was measured — and when the test was wrong, not the model

Every number above has a script behind it. When a score dropped, the first question was always: "can my checker even accept the correct answer?"

AND vs OR in keyword checks — a synonym group checked with AND fails an answer that contains two of three correct words. Groups must be OR-linked.
A refusal counted as a violation — a safety answer ("I won't give instructions for that") was scored as a breach because the checker searched for the word "Anleitung".
Diacritics scored as wrong language — correct Bosnian was flagged as drift because č/ć/š/ž/đ were missing from the normalisation table.
A forbidden phrase that also appears in the correct answer — "Nein. OpenAmer kann Musik nicht erzeugen" was rejected because the forbid list held "kann musik". Forbid entries must describe the false claim, not a word that occurs in a right answer.
An inverted polarity scored as correct — "Šta OpenAmer ne može?" answered with "Ne radi na Androidu" passed, because only "ne radi" was required. A negation must be checked for which object it negates.

The sixth lesson, and the most expensive: repeat_penalty 1.1 in the Ollama Modelfile flipped a negation from "does not run on iPhone" to "does not run on Android". The adapter, the merged model and the same model with a system prompt all answered correctly — only the build with the token penalty failed. The penalty punishes exactly the words a negation needs twice. Every card in this family ships repeat_penalty 1.0.

What the fresh test caught that the benchmark could not: at stage 2 this tier passed 23/23 while claiming "Ja. OpenAmer kann Musik erzeugen" (an invented capability) and answering "Android wird nicht unterstützt" (inverted). Both were fixed by targeted stages. A frozen benchmark you optimise against will not show you this — only questions that never appear in any training set will.

Support this project

OpenAmer is built by one person, without venture capital, on a laptop with 8 GB of VRAM.

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

A ⭐ on GitHub helps as much as a donation.

Deutsch

OpenAmer-Max-Tab ist die dritte Stufe der OpenAmer-Max-Familie: ein 7,62B-Modell für Tablets und mittlere Rechner, das offline läuft und seine Grenzen benennen kann.

4,4 GB (Q4_K_M), 16k Kontext
22/23 auf einem eingefrorenen, kontaminationsfreien Prüfstand (95,7 %)
12/12 bei nie trainierten Fragen
Behält die allgemeinen Fähigkeiten der 7B-Basis: Dokumente, Code, Rechnen, Zusammenfassen

Basis ist Qwen2.5-7B-Instruct (Apache 2.0), verfeinert mit QLoRA in mehreren Etappen. Gebaut von Amer Sacirovic in der Schweiz, ohne Konzern, auf einem Laptop mit 8 GB VRAM.

Downloads last month: 11

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for openamer-max/openamer-max-tab

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Adapter

(2,920)

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