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 / README.md

NANI-Nithin

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	---
	language:
	- en
	license: apache-2.0
	base_model: inclusionAI/Ling-3.0-tiny
	tags:
	- gguf
	- llama.cpp
	- quantized
	- moe
	- bailingmoev3
	- hybrid-model
	- local-llm
	- text-generation
	pipeline_tag: text-generation
	---

	# Ling-3.0-tiny-GGUF

	GGUF quantizations of [inclusionAI/Ling-3.0-tiny](https://huggingface.co/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`.

	```bash
	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`](https://huggingface.co/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](https://huggingface.co/inclusionAI)
	- GGUF conversion and quantization by [NANI-Nithin](https://huggingface.co/NANI-Nithin)
	- GGUF tooling by the [llama.cpp](https://github.com/ggml-org/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:

	```json
	{
	"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.