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

Libraries

How to use Inserloft/NaNo with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Inserloft/NaNo",
	filename="NaNo-V3.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 Inserloft/NaNo with llama.cpp:

Install from brew

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

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Inserloft/NaNo
# Run inference directly in the terminal:
llama-cli -hf Inserloft/NaNo

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 Inserloft/NaNo
# Run inference directly in the terminal:
./llama-cli -hf Inserloft/NaNo

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 Inserloft/NaNo
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Inserloft/NaNo

Use Docker

docker model run hf.co/Inserloft/NaNo

LM Studio
Jan
Ollama
How to use Inserloft/NaNo with Ollama:
```
ollama run hf.co/Inserloft/NaNo
```

Unsloth Studio new

How to use Inserloft/NaNo 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 Inserloft/NaNo 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 Inserloft/NaNo to start chatting

Using HuggingFace Spaces for Unsloth

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

Docker Model Runner
How to use Inserloft/NaNo with Docker Model Runner:
```
docker model run hf.co/Inserloft/NaNo
```

Lemonade

How to use Inserloft/NaNo with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Inserloft/NaNo

Run and chat with the model

lemonade run user.NaNo-{{QUANT_TAG}}

List all available models

lemonade list

Cleo Nano v3.1 (Bilingual Optimization)

Cleo Nano is a decoder-only Transformer model developed by Inserloft under the vision of Jesus Heriberto Corona. This version (v3.1) features surgical fine-tuning for bilingual stability (English/Spanish) and hallucination control.

Model Details

Architecture: Decoder-Only GPT (Custom)
Layers: 8
Embedding Dim: 384
Attention Heads: 12
Context Window: 256 tokens
Parameters: ~15M
Training Data: Mix of Wikipedia, Python Code (CodeFeedback), and Identity Anchoring.

Usage

To use this model, you need the custom CleoNanoV3 architecture defined in PyTorch. The weights can be loaded using torch.load() or via the Hugging Face from_pretrained if using the provided mapping logic.

Capabilities

Bilingual Chat: Responds to general queries in both Spanish and English.
Code Generation: Specialized in Python snippets (Sum, Loops, Classes).
Identity Preservation: Strong grounding on its origin and creator.

Developed by Inserloft

Downloads last month: -

GGUF

Model size

52.9M params

Architecture

gpt2

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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