Instructions to use Rohanify/Indenta-13M-Python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Rohanify/Indenta-13M-Python with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Rohanify/Indenta-13M-Python",
	filename="indenta-9m.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 Rohanify/Indenta-13M-Python with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Rohanify/Indenta-13M-Python
# Run inference directly in the terminal:
llama-cli -hf Rohanify/Indenta-13M-Python

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Rohanify/Indenta-13M-Python
# Run inference directly in the terminal:
llama-cli -hf Rohanify/Indenta-13M-Python

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 Rohanify/Indenta-13M-Python
# Run inference directly in the terminal:
./llama-cli -hf Rohanify/Indenta-13M-Python

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 Rohanify/Indenta-13M-Python
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Rohanify/Indenta-13M-Python

Use Docker

docker model run hf.co/Rohanify/Indenta-13M-Python

LM Studio
Jan

vLLM

How to use Rohanify/Indenta-13M-Python with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Rohanify/Indenta-13M-Python"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Rohanify/Indenta-13M-Python",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Rohanify/Indenta-13M-Python

Ollama
How to use Rohanify/Indenta-13M-Python with Ollama:
```
ollama run hf.co/Rohanify/Indenta-13M-Python
```

Unsloth Studio new

How to use Rohanify/Indenta-13M-Python 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 Rohanify/Indenta-13M-Python 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 Rohanify/Indenta-13M-Python to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Rohanify/Indenta-13M-Python to start chatting

Docker Model Runner
How to use Rohanify/Indenta-13M-Python with Docker Model Runner:
```
docker model run hf.co/Rohanify/Indenta-13M-Python
```

Lemonade

How to use Rohanify/Indenta-13M-Python with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Rohanify/Indenta-13M-Python

Run and chat with the model

lemonade run user.Indenta-13M-Python-{{QUANT_TAG}}

List all available models

lemonade list

🚀 Indenta-13M-Python (GGUF)

An optimized from-scratch model made with a custom tokenizer and GPT-2 architecture. This model is built specifically for lightning-fast Python code completions and basic code generation. At ~13M parameters, it runs with near-zero latency on absolutely any hardware!

🛠️ Web UI & Multi-Turn Stability Note

This model features a hyper-lightweight 13M parameter footprint optimized for single-task completions based directly on structural templates.

Because it lacks the large capacity required for conversational context processing, it can drop formatting structure if a graphical Web UI forces a massive, multi-message chat stream into its context layers.

For Best Performance in Web UIs:

Use New Chat Threads: Click the "New Chat" or "Clear" button in your user interface between coding tasks. This keeps the model's focus squarely on your active prompt.
Synchronized Template: This model card includes an aligned layout format (## Instruction: and ### Input:) matching the training data to stop token bleeding across chat iterations.

Downloads last month: 64

GGUF

Model size

13.3M params

Architecture

gpt2

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Rohanify
/

Indenta-13M-Python

🚀 Indenta-13M-Python (GGUF)

🛠️ Web UI & Multi-Turn Stability Note

For Best Performance in Web UIs:

Dataset used to train Rohanify/Indenta-13M-Python