Instructions to use NANI-Nithin/Ling-3.0-tiny-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 NANI-Nithin/Ling-3.0-tiny-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 NANI-Nithin/Ling-3.0-tiny-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NANI-Nithin/Ling-3.0-tiny-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 NANI-Nithin/Ling-3.0-tiny-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NANI-Nithin/Ling-3.0-tiny-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 NANI-Nithin/Ling-3.0-tiny-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf NANI-Nithin/Ling-3.0-tiny-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 NANI-Nithin/Ling-3.0-tiny-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NANI-Nithin/Ling-3.0-tiny-GGUF:Q4_K_M

Use Docker

docker model run hf.co/NANI-Nithin/Ling-3.0-tiny-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use NANI-Nithin/Ling-3.0-tiny-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NANI-Nithin/Ling-3.0-tiny-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "NANI-Nithin/Ling-3.0-tiny-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NANI-Nithin/Ling-3.0-tiny-GGUF:Q4_K_M

Ollama
How to use NANI-Nithin/Ling-3.0-tiny-GGUF with Ollama:
```
ollama run hf.co/NANI-Nithin/Ling-3.0-tiny-GGUF:Q4_K_M
```

Unsloth Studio

How to use NANI-Nithin/Ling-3.0-tiny-GGUF 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 NANI-Nithin/Ling-3.0-tiny-GGUF 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 NANI-Nithin/Ling-3.0-tiny-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for NANI-Nithin/Ling-3.0-tiny-GGUF to start chatting

How to use NANI-Nithin/Ling-3.0-tiny-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NANI-Nithin/Ling-3.0-tiny-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "NANI-Nithin/Ling-3.0-tiny-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use NANI-Nithin/Ling-3.0-tiny-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NANI-Nithin/Ling-3.0-tiny-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "NANI-Nithin/Ling-3.0-tiny-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use NANI-Nithin/Ling-3.0-tiny-GGUF with Docker Model Runner:
```
docker model run hf.co/NANI-Nithin/Ling-3.0-tiny-GGUF:Q4_K_M
```

Lemonade

How to use NANI-Nithin/Ling-3.0-tiny-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NANI-Nithin/Ling-3.0-tiny-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Ling-3.0-tiny-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use NANI-Nithin/Ling-3.0-tiny-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NANI-Nithin/Ling-3.0-tiny-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default NANI-Nithin/Ling-3.0-tiny-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

Ling-3.0-tiny-GGUF

GGUF quantizations of inclusionAI/Ling-3.0-tiny, converted for use with compatible llama.cpp-based runtimes.

This repository includes a complete selection of standard K-quants and importance-matrix (IQ) quantizations, so you can choose the best balance of model size, speed, and output quality for your hardware.

Runtime compatibility: Ling-3.0-tiny uses the BailingMoeV3 / hybrid architecture. Use a runtime with explicit support for this architecture. Generic or older llama.cpp builds may not load these files correctly.

Available files

Quantization	Best for
`F16`	Highest-fidelity baseline; re-quantization and high-memory systems
`Q8_0`	Near-F16 quality with substantially lower memory use
`Q6_K`	High-quality local inference
`Q5_K_M`	Strong quality-to-size balance
`Q5_K_S`	Slightly smaller alternative to Q5_K_M
`Q5_0`	Legacy-style 5-bit option
`Q4_K_M`	Recommended default for most users
`Q4_K_S`	Smaller Q4 K-quant alternative
`Q4_0`	Compact legacy-style 4-bit option
`IQ4_NL`	High-quality importance-matrix 4-bit option
`IQ4_XS`	Compact importance-matrix 4-bit option
`Q3_K_L`	Higher-quality 3-bit K-quant
`Q3_K_M`	Balanced 3-bit K-quant
`Q3_K_S`	Smaller 3-bit K-quant
`IQ3_M`	Strong quality-per-GB option for constrained systems
`IQ3_S`	Smaller 3-bit IQ option
`IQ3_XS`	Very compact IQ 3-bit option
`IQ3_XXS`	Extremely compact IQ 3-bit option
`Q2_K`	Low-memory K-quant option
`IQ2_M`	Compact IQ quant with better quality potential than very-low-bit options
`IQ2_S`	Low-memory IQ option
`IQ2_XS`	Very small IQ option
`IQ2_XXS`	Extremely small IQ option
`IQ1_M`	Experimental ultra-low-memory option
`IQ1_S`	Smallest experimental option

Recommended downloads

Your priority	Recommended file
Best quality	`Ling-3.0-tiny-F16.gguf`
Near-original quality	`Ling-3.0-tiny-Q8_0.gguf`
High quality with lower memory use	`Ling-3.0-tiny-Q6_K.gguf`
Best general-purpose choice	`Ling-3.0-tiny-Q4_K_M.gguf`
Small but capable	`Ling-3.0-tiny-IQ3_M.gguf`
Tight VRAM / RAM budget	`Ling-3.0-tiny-IQ2_M.gguf`
Experimental minimum size	`Ling-3.0-tiny-IQ1_S.gguf`

For most users, start with Q4_K_M. If you have more RAM or VRAM, try Q5_K_M, Q6_K, or Q8_0. IQ quants can offer attractive quality-to-size trade-offs, but results and compatibility may vary by runtime and hardware.

Usage

Download one .gguf file, then run it with a compatible build of llama.cpp.

llama-cli \
  -m Ling-3.0-tiny-Q4_K_M.gguf \
  -ngl 99 \
  -c 4096 \
  -p "Write a concise explanation of retrieval-augmented generation."

-ngl 99 attempts to offload all supported layers to the GPU. Remove it or set -ngl 0 for CPU-only inference.

Important notes

These files are quantized derivatives of the original model; output quality changes depending on the chosen quantization.
Very low-bit quants, especially IQ1 and IQ2 variants, are intended for memory-constrained or experimental use and may noticeably reduce output quality.
Use the original model’s license, terms, and usage requirements.
Validate the selected quantization on your own workload before production use.

Conversion details

Base model: inclusionAI/Ling-3.0-tiny
Format: GGUF
Conversion/runtime branch: BailingMoeV3-compatible llama.cpp fork
Standard K-quants: generated from the F16 GGUF
IQ quants: generated using an importance matrix calibrated on a text corpus

Credits

Original model by inclusionAI
GGUF conversion and quantization by NANI-Nithin
GGUF tooling by the llama.cpp community

Disclaimer

This is a community GGUF conversion and is not an official release by inclusionAI. Please report conversion, loading, or compatibility issues in this repository’s Discussions section.

Reproducibility

This repository was generated with a BailingMoeV3-enabled llama.cpp fork. The exact source checkout checkpoint is recorded below:

{
  "stage": "01_checkout_bailing_llama",
  "status": "complete",
  "timestamp_utc": "2026-08-11T10:36:13.114546+00:00",
  "model": "inclusionAI/Ling-3.0-tiny",
  "llama_repo": "https://github.com/aetherbird/llama.cpp.git",
  "llama_branch": "bailingmoe3-support",
  "repo_dir": "/mnt/ling/src/llama.cpp",
  "commit": "3a0124fa8c20356ed5e6bf0c0ebae1566d6f49c1"
}

Files

F16: Conversion baseline.
Q4_K_M: General local-inference default.
Q5_K_M, Q6_K, Q8_0: Higher-fidelity variants.
IQ*: Importance-matrix variants, generated only when supported by the pinned quantizer.

Use a Ling/BailingMoeV3-compatible runtime to load these files.

Downloads last month: -

GGUF

Model size

8B params

Architecture

bailingmoe3

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for NANI-Nithin/Ling-3.0-tiny-GGUF

Base model

inclusionAI/Ling-3.0-tiny

Quantized

(5)

this model