Instructions to use Erog0291/stewmind-1.7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

# Use a pipeline as a high-level helper
from transformers import pipeline

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

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

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 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):
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?"
			}
		]
	}'

Ollama
How to use Erog0291/stewmind-1.7b with Ollama:
```
ollama run hf.co/Erog0291/stewmind-1.7b:Q4_K_M
```
Unsloth Desktop

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:
pi

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"

Now on Ollama — one line, no signup:

ollama run emmanuelerogian723/stewmind-1.7b

Model page: https://ollama.com/emmanuelerogian723/stewmind-1.7b

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.

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:

{"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

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.

Quick start

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.

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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Safetensors

Model size

2B params

Tensor type

F16