Instructions to use tampakwill/AWA-Micro-Monster-3M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tampakwill/AWA-Micro-Monster-3M with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="tampakwill/AWA-Micro-Monster-3M",
	filename="AWA-Micro-F16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use tampakwill/AWA-Micro-Monster-3M 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 tampakwill/AWA-Micro-Monster-3M:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tampakwill/AWA-Micro-Monster-3M:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tampakwill/AWA-Micro-Monster-3M:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tampakwill/AWA-Micro-Monster-3M: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 tampakwill/AWA-Micro-Monster-3M:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf tampakwill/AWA-Micro-Monster-3M: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 tampakwill/AWA-Micro-Monster-3M:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tampakwill/AWA-Micro-Monster-3M:Q4_K_M

Use Docker

docker model run hf.co/tampakwill/AWA-Micro-Monster-3M:Q4_K_M

LM Studio
Jan
Ollama
How to use tampakwill/AWA-Micro-Monster-3M with Ollama:
```
ollama run hf.co/tampakwill/AWA-Micro-Monster-3M:Q4_K_M
```

Unsloth Studio

How to use tampakwill/AWA-Micro-Monster-3M 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 tampakwill/AWA-Micro-Monster-3M 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 tampakwill/AWA-Micro-Monster-3M to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tampakwill/AWA-Micro-Monster-3M to start chatting

Atomic Chat new
Docker Model Runner
How to use tampakwill/AWA-Micro-Monster-3M with Docker Model Runner:
```
docker model run hf.co/tampakwill/AWA-Micro-Monster-3M:Q4_K_M
```

Lemonade

How to use tampakwill/AWA-Micro-Monster-3M with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tampakwill/AWA-Micro-Monster-3M:Q4_K_M

Run and chat with the model

lemonade run user.AWA-Micro-Monster-3M-Q4_K_M

List all available models

lemonade list

🐲 AWA Micro-Monster (Logika 1.71M + Vocab 8k)

Eksperimen AI "Super-Duper Brutal" dari seri AWA. Model ini dirancang dengan sangat mungil namun mempertahankan tingkat presisi absolut menggunakan FP32 Murni.

📊 Spesifikasi Kancil Berotak Super:

Architecture: GPT-2 (Micro Scale)
Total Parameters: ~3.76M
Logic Parameters: ~1.71M
Context Window: 512/1024 Tokens
Precision: FP32 (Full 32-bit Float)
Dataset Eksperimen: Teks filosofis "Curcol Senja" (Hafalan 100% setelah 5000 Epoch).

📦 Varian GGUF:

Tersedia dalam F32 (Asli), F16, dan Q4_K_M (Hanya 3.6 MB!).

Downloads last month: 18

Safetensors

Model size

3.76M params

Tensor type

F32

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