Erog0291/stewmind-1.7b · Hugging Face

pipe = pipeline("text-generation", model="Erog0291/stewmind-1.7b") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate

Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Erog0291/stewmind-1.7b") model = AutoModelForCausalLM.from_pretrained("Erog0291/stewmind-1.7b", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

- Notebooks

- Google Colab

- Kaggle

- Local Apps[Settings](https://huggingface.co/settings/local-apps "Set up your favorite local applications")

- llama.cpp

How to use Erog0291/stewmind-1.7b 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 Erog0291/stewmind-1.7b:Q4_K_M

Run inference directly in the terminal:

llama cli -hf Erog0291/stewmind-1.7b:Q4_K_M ##### Install from WinGet (Windows)

winget install llama.cpp

Start a local OpenAI-compatible server with a web UI:

llama serve -hf Erog0291/stewmind-1.7b:Q4_K_M

Run inference directly in the terminal:

llama cli -hf Erog0291/stewmind-1.7b: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 Erog0291/stewmind-1.7b:Q4_K_M

Run inference directly in the terminal:

./llama-cli -hf Erog0291/stewmind-1.7b: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 Erog0291/stewmind-1.7b:Q4_K_M

Run inference directly in the terminal:

./build/bin/llama-cli -hf Erog0291/stewmind-1.7b:Q4_K_M ##### Use Docker

docker model run hf.co/Erog0291/stewmind-1.7b:Q4_K_M

- Llama new

- LM Studio

- Jan

- vLLM

How to use Erog0291/stewmind-1.7b with vLLM:

##### Install from pip and serve model

Install vLLM from pip:

pip install vllm

Start the vLLM server:

vllm serve "Erog0291/stewmind-1.7b"

Call the server using curl (OpenAI-compatible API):

curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Erog0291/stewmind-1.7b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' ##### Use Docker

docker model run hf.co/Erog0291/stewmind-1.7b:Q4_K_M

- SGLang

How to use Erog0291/stewmind-1.7b with SGLang:

##### Install from pip and serve model

Install SGLang from pip:

pip install sglang

Start the SGLang server:

python3 -m sglang.launch_server \ --model-path "Erog0291/stewmind-1.7b" \ --host 0.0.0.0 \ --port 30000

Call the server using curl (OpenAI-compatible API):

curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Erog0291/stewmind-1.7b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' ##### Use Docker images

docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Erog0291/stewmind-1.7b" \ --host 0.0.0.0 \ --port 30000

Call the server using curl (OpenAI-compatible API):

- Ollama

How to use Erog0291/stewmind-1.7b with Ollama:

ollama run hf.co/Erog0291/stewmind-1.7b:Q4_K_M

- Unsloth Desktop

- Pi

How to use Erog0291/stewmind-1.7b with Pi:

##### Start the llama.cpp server

Install llama.cpp:

brew install llama.cpp

Start a local OpenAI-compatible server:

llama serve -hf Erog0291/stewmind-1.7b:Q4_K_M ##### 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": "Erog0291/stewmind-1.7b:Q4_K_M" } ] } } } ##### Run Pi

Start Pi in your project directory:

- Docker Model Runner

How to use Erog0291/stewmind-1.7b with Docker Model Runner:

docker model run hf.co/Erog0291/stewmind-1.7b:Q4_K_M

- Lemonade

How to use Erog0291/stewmind-1.7b with Lemonade:

##### Pull the model

Download Lemonade from https://lemonade-server.ai/

lemonade pull Erog0291/stewmind-1.7b:Q4_K_M ##### Run and chat with the model

lemonade run user.stewmind-1.7b-Q4_K_M ##### List all available models

lemonade list

- Hermes Agent

How to use Erog0291/stewmind-1.7b with Hermes Agent:

##### Start the llama.cpp server

Install llama.cpp:

brew install llama.cpp

Start a local OpenAI-compatible server:

llama serve -hf Erog0291/stewmind-1.7b:Q4_K_M ##### 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 Erog0291/stewmind-1.7b:Q4_K_M ##### Run Hermes

hermes

- Atomic Chat

- OpenClaw

How to use Erog0291/stewmind-1.7b with OpenClaw:

##### Start the llama.cpp server

Install llama.cpp:

brew install llama.cpp

Start a local OpenAI-compatible server:

llama serve -hf Erog0291/stewmind-1.7b:Q4_K_M ##### 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 "Erog0291/stewmind-1.7b:Q4_K_M" \ --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"

- [StewMind 1.7B 🇳🇬 — The Tiny Agentic Brain That Runs on YOUR Machine](https://huggingface.co/Erog0291/stewmind-1.7b#stewmind-17b-%F0%9F%87%B3%F0%9F%87%AC--the-tiny-agentic-brain-that-runs-on-your-machine "StewMind 1.7B 🇳🇬 — The Tiny Agentic Brain That Runs on YOUR Machine")

- [Real Evaluation (measured, not marketing)](https://huggingface.co/Erog0291/stewmind-1.7b#real-evaluation-measured-not-marketing "Real Evaluation (measured, not marketing)")

- [StewMind vs the giants (our measured numbers + publicly listed specs)](https://huggingface.co/Erog0291/stewmind-1.7b#stewmind-vs-the-giants-our-measured-numbers--publicly-listed-specs "StewMind vs the giants (our measured numbers + publicly listed specs)")

- [Quick start](https://huggingface.co/Erog0291/stewmind-1.7b#quick-start "Quick start")

- [What's next: StewMind 1.7B ULTRA](https://huggingface.co/Erog0291/stewmind-1.7b#whats-next-stewmind-17b-ultra "What's next: StewMind 1.7B ULTRA")

![Image 4: STEWMIND 1.7B](https://huggingface.co/Erog0291/stewmind-1.7b/blob/main/banner.jpg)

[](https://huggingface.co/Erog0291/stewmind-1.7b#stewmind-17b-%F0%9F%87%B3%F0%9F%87%AC--the-tiny-agentic-brain-that-runs-on-your-machine) StewMind 1.7B 🇳🇬 — The Tiny Agentic Brain That Runs on YOUR Machine

Big models cook for everyone. **This one cooks for you.**

StewMind 1.7B is a compact agentic AI built in Nigeria. It routes intents, calls tools, plans with ReAct and stores memory — **locally, offline, with zero API bills.**

[](https://huggingface.co/Erog0291/stewmind-1.7b#real-evaluation-measured-not-marketing) Real Evaluation (measured, not marketing)

We ran StewMind 1.7B live on a plain CPU (no GPU) across 5 agentic tasks. Raw results are published in `benchmark_results.json`.

**Tool-routing test** — input: _"Send 5000 naira to my brother today. Reply with only a JSON object."_ StewMind answered:

```json {"tool": "transfer", "args": {"amount": "5000", "recipient": "my brother", "date": "today"}} ```

Valid JSON. Correct tool. Correct arguments. On a CPU.

**Measured on CPU (fp32, unquantized, shared sandbox):**

| Test | Result | | --- | --- | | Intent routing → JSON tool call | ✅ valid JSON, correct tool + args | | Tool call (weather) | ✅ correct plan | | ReAct planning | ✅ sound first step | | Memory extraction | ✅ correct approach | | Pidgin understanding | ✅ understood "Wetin be your main work?" | | Speed | ~2 tok/s on plain CPU fp32 (15-30+ tok/s typical with GGUF Q4 on a laptop) | | Footprint | 1.72B params · 1.0 GB GGUF (Q4_K_M) · loads in seconds |

[](https://huggingface.co/Erog0291/stewmind-1.7b#stewmind-vs-the-giants-our-measured-numbers--publicly-listed-specs) StewMind vs the giants (our measured numbers + publicly listed specs)

| | StewMind 1.7B | GPT-4o-mini (API) | Llama-3.1-8B | Phi-3.5-mini | | --- | --- | --- | --- | --- | | Runs offline | ✅ 100% local | ❌ needs internet | ✅ | ✅ | | Data leaves your device | Never | Always | Never | Never | | Download size | ~1 GB (GGUF) | 0 (API only) | ~4.7 GB (Q4) | ~2.3 GB (Q4) | | RAM to run | ~1.5 GB | n/a | ~6 GB | ~3 GB | | Cost per 1M tokens | $0.00 forever | ~$0.15–0.60 (listed) | $0.00 | $0.00 | | First-token latency | ms (on-device) | ~500-2000 ms network | ms | ms | | Fits in a phone | ✅ | ❌ | ❌ | barely |

A 1.7B will not out-write GPT-4. **It doesn't have to.** Agents need routing, tool calls and reflexes — and there StewMind wins on speed, cost and privacy. NVIDIA's own research ("Small Language Models are the Future of Agentic AI") points exactly this way.

[](https://huggingface.co/Erog0291/stewmind-1.7b#quick-start) Quick start

```python from transformers import AutoModelForCausalLM, AutoTokenizer tok = AutoTokenizer.from_pretrained("Erog0291/stewmind-1.7b") model = AutoModelForCausalLM.from_pretrained("Erog0291/stewmind-1.7b") msgs = [{"role": "user", "content": "Call the weather tool for Lagos. JSON only."}] ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt") print(tok.decode(model.generate(ids, max_new_tokens=80, do_sample=False)[0][ids.shape[1]:], skip_special_tokens=True)) ```

**GGUF (Ollama / llama.cpp):** download `stewmind-1.7b-Q4_K_M.gguf` from this repo — ready to run on phones, laptops and edge devices.

[](https://huggingface.co/Erog0291/stewmind-1.7b#whats-next-stewmind-17b-ultra) What's next: StewMind 1.7B ULTRA

ULTRA is training right now on 100,000 agentic workflow samples (tool calls, ReAct traces, memory ops) — checkpoints land in Erog0291/stewmind-1.7b-ultra as they bake. Expect sharper, faster, stricter-JSON agents.

Read the launch story: Big Models Cook for Everyone. This One Cooks for You.

Built with 🍲 in Nigeria by MUTYINT · Apache 2.0 · commercial use allowed

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Model size

2B params

Tensor type

F16

Chat template

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