WithinUsAI/IBM-Grok4-Ultra.Fast.Coder-1B-GGUF

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

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."