Instructions to use WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B-GGUF 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 WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B-GGUF: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 WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B-GGUF: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 WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B-GGUF:Q4_K_M

Ollama
How to use WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B-GGUF with Ollama:
```
ollama run hf.co/WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B-GGUF with Docker Model Runner:
```
docker model run hf.co/WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B-GGUF:Q4_K_M
```

Lemonade

How to use WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Llama3.2-Octo.Thinker.iNano-1B-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Llama3.2-Octo.Thinker.iNano-1B-GGUF / README.md

Guy DuGan II

Create README.md

0ba188a verified 6 months ago

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metadata

license: other
library_name: gguf
tags:
  - gguf
  - llama
  - llama-3.2
  - text-generation
  - local-llm
  - llama-cpp
  - lm-studio
  - ollama
  - 1b
pipeline_tag: text-generation
language:
  - en
base_model_relation: quantized
base_model:
  - gss1147/Llama-3.2-OctoThinker-iNano-1B

Llama-3.2-OctoThinker-iNano-1B-GGUF

Model Summary

Llama-3.2-OctoThinker-iNano-1B-GGUF is the GGUF quantized release of the main model:

Main model repo:
https://huggingface.co/gss1147/Llama-3.2-OctoThinker-iNano-1B

This repository packages the model for efficient local inference in GGUF-compatible runtimes such as llama.cpp, LM Studio, and similar local tools.

GGUF to Main Model Link

This GGUF repository corresponds to the main model repo:

gss1147/Llama-3.2-OctoThinker-iNano-1B

If you want the original non-GGUF model, training/merge details, tokenizer files, and main repository metadata, use the repo above.

Available Files

This GGUF repository currently includes:

Q4_K_M — 955 MB
Q5_K_M — 1.09 GB
F16 — 3 GB

Architecture

Architecture: llama
Model size: 1B params

Intended Use

This model is intended for:

local text generation
assistant-style prompting
lightweight reasoning tasks
summarization
simple coding help
offline/local inference workflows

Quantization Notes

Choose the file that best matches your hardware:

Q4_K_M for smaller size and lighter RAM usage
Q5_K_M for a stronger quality-to-size balance
F16 for the highest-fidelity file in this repo, with much higher memory requirements

Example llama.cpp Usage

llama-cli -m /path/to/Llama-3.2-OctoThinker-iNano-1B.Q4_K_M.gguf -p "Explain recursion in Python with a simple example."