---
license: other
library_name: gguf
tags:
- gguf
- llama
- 1b
- text-generation
- local-llm
- llama-cpp
- ollama
- lm-studio
- gpt4all
pipeline_tag: text-generation
language:
- en
base_model_relation: quantized
base_model:
- gss1147/IBM-Grok4-UltraFast-Coder-1B
---

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

## Model Summary

**Llama-3.2-OctoThinker-iNano-1B-GGUF** is a compact GGUF release published by **gss1147** for local text generation and on-device inference workflows. The repository is currently listed on Hugging Face as a **GGUF** model with **1B parameters** and **llama** architecture, and includes three downloadable variants:

- **Q4_K_M** — **955 MB**
- **Q5_K_M** — **1.09 GB**
- **F16** — **3 GB**  
:contentReference[oaicite:1]{index=1}

This packaging is intended for users who want a lightweight local model that can be run with GGUF-compatible runtimes such as **llama.cpp**, **LM Studio**, and related tooling. GGUF is the format used by llama.cpp for efficient local inference, and llama.cpp documentation recommends **Q4_K_M** as a good balance for most users, **Q5_K_M** for somewhat higher quality, and **F16** when you want full-precision weights. :contentReference[oaicite:2]{index=2}

## Available Files

- `Llama-3.2-OctoThinker-iNano-1B.Q4_K_M.gguf`
- `Llama-3.2-OctoThinker-iNano-1B.Q5_K_M.gguf`
- `Llama-3.2-OctoThinker-iNano-1B.f16.gguf`  
:contentReference[oaicite:3]{index=3}

## Intended Use

This model is suited for:

- local text generation
- lightweight assistant/chat experiments
- offline inference
- CPU-friendly or lower-memory setups compared with larger models
- GGUF-based desktop applications and local inference stacks

Because this repo is distributed in GGUF format, it is aimed at **inference**, not at further full-precision training from these files directly. GGUF is primarily used for efficient deployment and local execution. :contentReference[oaicite:4]{index=4}

## Quantization Options

### Q4_K_M
A compact option intended to give a strong size-to-quality balance for everyday local inference. llama.cpp documentation describes `Q4_K_M` as a good balance and recommends it for most users. :contentReference[oaicite:5]{index=5}

### Q5_K_M
A larger quantization that typically preserves more quality than 4-bit options while still remaining much smaller than full precision. llama.cpp documentation describes `Q5_K_M` as a higher-quality choice. :contentReference[oaicite:6]{index=6}

### F16
A full-precision GGUF export for users who want the least quantization loss and have enough memory/storage to run it. llama.cpp documentation lists `f16.gguf` as full precision. :contentReference[oaicite:7]{index=7}

## Suggested Runtime Compatibility

This model should be appropriate for GGUF-compatible runtimes, including:

- **llama.cpp**
- **LM Studio**
- **GPT4All**
- other GGUF-capable local inference tools

The GGUF format is the standard format used by llama.cpp for local inference workflows. :contentReference[oaicite:8]{index=8}

## Example Usage with llama.cpp

```bash
llama-cli -m /path/to/Llama-3.2-OctoThinker-iNano-1B.Q4_K_M.gguf -p "Write a short Python function that reverses a string."