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

NANI-Nithin commited on 1 day ago

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@@ -1,18 +1,110 @@
 ---
-base_model: inclusionAI/Ling-3.0-tiny
 language:
 - en
 tags:
 - gguf
 - quantized
 - moe
 - bailingmoev3
-- ling
 ---
-# Ling-3.0-tiny GGUF
-Community GGUF conversion of [inclusionAI/Ling-3.0-tiny](https://huggingface.co/inclusionAI/Ling-3.0-tiny).
 ## Reproducibility

 ---
 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