Instructions to use macwhisperer/smartchild with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use macwhisperer/smartchild with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="macwhisperer/smartchild",
	filename="tinyllama-1.1b-Q4_K_M.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use macwhisperer/smartchild with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf macwhisperer/smartchild:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf macwhisperer/smartchild:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf macwhisperer/smartchild:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf macwhisperer/smartchild: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 macwhisperer/smartchild:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf macwhisperer/smartchild: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 macwhisperer/smartchild:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf macwhisperer/smartchild:Q4_K_M

Use Docker

docker model run hf.co/macwhisperer/smartchild:Q4_K_M

LM Studio
Jan
Ollama
How to use macwhisperer/smartchild with Ollama:
```
ollama run hf.co/macwhisperer/smartchild:Q4_K_M
```

Unsloth Studio new

How to use macwhisperer/smartchild with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for macwhisperer/smartchild to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for macwhisperer/smartchild to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for macwhisperer/smartchild to start chatting

Docker Model Runner
How to use macwhisperer/smartchild with Docker Model Runner:
```
docker model run hf.co/macwhisperer/smartchild:Q4_K_M
```

Lemonade

How to use macwhisperer/smartchild with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull macwhisperer/smartchild:Q4_K_M

Run and chat with the model

lemonade run user.smartchild-Q4_K_M

List all available models

lemonade list

📟 SmartChild-1.1B--Q4_K_M (2026 Edition)

"He's back... and he's local. lol."

This is a custom-quantized version of TinyLlama-1.1B, specifically optimized to attempt to revive the spirit and utility of the legendary SmarterChild AIM bot. While not quite as snarky as the OG, this model maintains a fun, snappy, and positive coherence that is random/uplifting in its nature. While the model is not 100% factually correct with dates or names, it is good for things like: introspection, simple advice, fake inspirational quotes, idea generation, simple coding examples, recipes, lyric generation, storytelling, brainstorming, and all other manner of silly musings (It may hallucinate, talk to itself, or "slop out" on prompts with low context like "hey!").

🧠 Why this model is different

Unlike a standard 1.1B quant, this model was processed using a custom Importance Matrix (imatrix). The training data for the imatrix was hand-curated to preserve:

Classic AIM Dialect: High retention of 2000s-era slang (rofl, lmao, brb, s/l/r).
Logical Flow: Inclusion of wllama.js source code and logic puzzles in the imatrix training to ensure the model stays coherent at low bitrates.
Modern Awareness: Contextual data for 2026, including local-first AI and edge computing concepts.

🛠 Quantization Details

Base Model: TinyLlama-1.1B-Chat-v1.0
Quantization: Q4_K_M
Format: GGUF
Size: ~668 MB
Context Length: 2048 tokens

📈 Perplexity Benchmarks

The following results were generated using llama-perplexity on the wikitext-2-raw/wiki.test.raw dataset.

Model	Precision	Perplexity (PPL)	Δ PPL
TinyLlama-1.1B (Baseline)	F16	19.5532	-
TinyLlama-1.1B (Quant)	Q4_K_M	19.9509	+0.3977

⚖️ Evaluation Verdict

For a model as small as TinyLlama (1.1B), this is a highly successful quantization. Smaller models are inherently "fragile"—they have fewer parameters to represent complex information, so reducing bit-depth usually results in a significant accuracy drop. A Delta of +0.3977 indicates that the Q4_K_M method has preserved the vast majority of the model's reasoning capabilities while reducing the memory footprint by approximately 85%.

🚀 Hardware Performance (Apple M2)

Throughput: 100 tokens/sec (Prompt Eval)
Memory Usage: ~636 MiB RAM for model weights.

🚀 Hardware Performance (Apple Intel i7)

Throughput: 3 tokens/sec (Prompt Eval)
Usage: I have included a custom built, non-AVX llama-server C++ executable in the files! Use this server in combination with my model to turn old consumer computers into cpu-only inference machines!! Runs AI on basicaly any computer from the last 16 years!