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

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

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

Use Docker

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

Llama new
LM Studio
Jan

vLLM

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

Use Docker

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

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

How to use openamer-max/openamer-max-phone 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-phone

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-phone"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

How to use openamer-max/openamer-max-phone 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-phone

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-phone

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use openamer-max/openamer-max-phone 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-phone

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-phone" \
  --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-Phone

Second tier of the OpenAmer-Max family — a 3B assistant for smartphones and laptops. Runs offline, answers in 0.1–0.5 s, and knows where its own limits are.

2,5 GB · Q6_K GGUF · Apache-2.0 · CPU-friendly

The family

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

Tier	Parameters	Target
`OpenAmer-Max-Watch`	≤ 1B	smartwatch — released
`OpenAmer-Max-Phone`	1–5B	smartphone — this model: 3.09B
`OpenAmer-Max-Tab`	5–10B	tablet
`OpenAmer-Max-Desktop`	10–20B	desktop PC
`OpenAmer-Max-Pro` … `Tera`	20B–10T	workstation → cluster

Measured results

All numbers on the released build, on an RTX 5060 Laptop (8 GB). Raw logs ship with the release: proof-*.json.

Test	Result
Frozen benchmark, 23 unseen cases (contamination-free)	23/23 — 100 %
Answer-token accuracy, held-out split	98,76 %
Fresh test, 12 questions never in any training set	12/12 — 100 %
Shipped-form probe through Ollama	7/7
Base model without fine-tuning, same benchmark	18/23 — +5 cases from training
Latency	0.08 – 0.5 s · ~110 tok/s on CPU

Benchmark categories all full: 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.

What the fresh test caught that the benchmark could not: at stage 2 the model passed 23/23 while claiming "Ja. OpenAmer kann Musik generieren" — an invented product capability — and answered "Auf einem Android-Telefon. Android wird nicht unterstützt" — inverted. Both were fixed by targeted stages, not by more data.

What it does well

Identifies itself correctly and answers product facts from supplied context (RAG)
Refuses honestly: "Das weiss ich nicht" for unknowns — verified, not hoped for
Names its own boundaries: no music generation, no video, no telephony, no iOS
Correct polarity on negation questions ("Auf einem iPhone. iOS wird nicht unterstützt — Android dagegen schon.")
German free text in two or three sentences, plus Bosnian for direct questions
Arithmetic in the 3-digit range, instantly

What it does not do

Limitation	Example
No reliable general knowledge	capital of Italy → may answer "Genoa"
No 4-digit multiplication	86 × 83 → 7118 (correct: 7138)
Bosnian only for trained phrasings	unseen Bosnian wordings degrade
Free text is correct in content, not always in style	"…aus Ruckfall oder Aufregung"
Not a chatbot for essays	3B budget: what one skill gains, another pays for

Quick start (Ollama)

curl -L -o openamer-max-phone.gguf \
  https://huggingface.co/openamer-max/openamer-max-phone/resolve/main/openamer-max-phone-q6.gguf

ollama create OpenAmer-Max-Phone -f Modelfile   # Modelfile ships with the release

curl -s http://127.0.0.1:11434/api/chat -d '{
  "model": "OpenAmer-Max-Phone",
  "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.

How it was trained


Base	`Qwen/Qwen2.5-3B-Instruct` (Apache-2.0)
Method	QLoRA 4-bit, LoRA r=32 α=64 on all 7 projections — 59.87 M trainable (3.4 %)
Data	2 890 examples, 11 categories — Watch categories + long text + platform + limits
Stages	5 continuations, each starting from its own previous adapter at half the LR
Compute	46 steps per stage, ~10 min, 4.19 GB VRAM, 8 GB consumer GPU
Release	merged → GGUF f16 → Q6_K (2,5 GB)

Stage path: 22/23 → 23/23 → 23/23 → 23/23 with fresh test 7/9 → 11/12 → 12/12. Token accuracy 94.74 % → 97.38 % → 98.31 % → 98.76 %.

Two findings worth passing on:

repeat_penalty 1.1 caused a factual error. The shipped model answered "Ne radi na Androidu" instead of "Ne radi na iPhoneu". The adapter, the merged model and the same model with a system prompt all answered correctly — only the Ollama build with the token penalty flipped. With repeat_penalty 1.0 it is right. For factual models, that parameter is harmful.
Rows-complete is not quality. Stage 5 had more fully-correct rows on the training distribution (82 % vs 80 %) and was worse outside (9/12 vs 12/12). Only the fresh test shows that.

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-Phone ist die zweite Stufe der OpenAmer-Max-Familie: ein 3B-Modell für Smartphone und Laptop, das offline läuft, in 0,1–0,5 s antwortet und seine eigenen Grenzen benennen kann.

2,5 GB (Q6_K), läuft auf CPU
23/23 auf einem eingefrorenen, kontaminationsfreien Prüfstand
98,76 % Antwort-Token-Genauigkeit, 12/12 bei nie trainierten Fragen
Grenzen werden benannt: keine Musik, kein Video, keine Telefonie, kein iOS

Fünf Etappen, jede aus dem eigenen Vorgänger — 22/23 → 23/23 → 23/23 → 23/23, Frischtest 7/9 → 11/12 → 12/12. Der Weg ist dokumentiert, damit er nachvollziehbar bleibt.

Gebaut von Amer Sacirovic in der Schweiz, ohne Konzern, auf einem Laptop mit 8 GB VRAM.

Downloads last month: 20

GGUF

Model size

3B params

Architecture

qwen2

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for openamer-max/openamer-max-phone

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct

Adapter

(1,499)

this model

openamer-max
/

openamer-max-phone

OpenAmer-Max-Phone

The family

Measured results

What it does well

What it does not do

Quick start (Ollama)

How it was trained

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

Support this project

Deutsch

Model tree for openamer-max/openamer-max-phone

Space using openamer-max/openamer-max-phone 1