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

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

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"

StewMind 1.7B 🍲

StewMind is the self-hosted agentic specialist model powering Stew, the autonomous AI agent by MUTYINT — Multipurpose Talented Young Inventors and Transformers, an AI lab for Intelligent Quotients founded by Emmanuel Ene Rejoice Gideon in Nigeria.

Purpose

Built to eliminate API rate limits for Stew's internal tasks: skill routing, tool-call planning, ReAct reasoning, memory operations, and lightweight chat — with zero per-token cost.

Training

Base model: Qwen3-1.7B
Method: LoRA fine-tune (merged), 2 epochs, bf16/fp16
Data: 728 train / 30 val synthetic agentic examples (deterministic, seed 42) across 6 task families: router, tool-calling, ReAct planning, memory ops, chat, identity
Hardware: NVIDIA Tesla T4 (Kaggle)
Final eval loss: 0.596
Trained: 2026-10-04

Identity

StewMind knows it is built by MUTYINT, is strong in agentic reasoning (ReAct, planning, reflection, verification, MCP, multi-agent, guardrails), and denies being ChatGPT/OpenAI.

Use

from transformers import AutoModelForCausalLM, AutoTokenizer
tok = AutoTokenizer.from_pretrained("Erog0291/stewmind-1.7b")
model = AutoModelForCausalLM.from_pretrained("Erog0291/stewmind-1.7b", torch_dtype="auto")

GGUF quantized versions for llama.cpp are published in this repo as stewmind-1.7b-Q4_K_M.gguf.

Downloads last month: 353

Safetensors

Model size

2B params

Tensor type

F16

Model tree for Erog0291/stewmind-1.7b

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Quantized

(412)

this model