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

Use Docker

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

LM Studio
Jan

vLLM

How to use bartowski/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 "bartowski/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": "bartowski/Ling-3.0-tiny-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

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

Unsloth Studio

How to use bartowski/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 bartowski/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 bartowski/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 bartowski/Ling-3.0-tiny-GGUF to start chatting

How to use bartowski/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 bartowski/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": "bartowski/Ling-3.0-tiny-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

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

Pull the model

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

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use bartowski/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 bartowski/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 "bartowski/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"

Llamacpp imatrix Quantizations of Ling-3.0-tiny by inclusionAI

Using llama.cpp release b10472 for quantization.

Original model: https://huggingface.co/inclusionAI/Ling-3.0-tiny

Model details:

Parameter count: 8B
Input support: text
Speculative decoding: no
imatrix: yes - details
Perplexity/KLD measured: no

How to run

Prompt format

<role>SYSTEM</role>{system_prompt}
detailed thinking on<|role_end|><role>HUMAN</role>{prompt}<|role_end|><role>ASSISTANT</role>
<think>

Don't know which to choose? Grab Q4_K_M (4.92GB) - usually a good mix of size and performance. Download instructions available here

Available files:

Filename	Quant type	File Size	Split	Description
Ling-3.0-tiny-bf16.gguf	bf16	15.80GB	false	Full BF16 weights.
Ling-3.0-tiny-Q8_0.gguf	Q8_0	8.41GB	false	Extremely high quality, generally unneeded but max available quant.
Ling-3.0-tiny-Q6_K_L.gguf	Q6_K_L	6.96GB	false	Uses Q8_0 for embed and output weights. Very high quality, near perfect, recommended.
Ling-3.0-tiny-Q6_K.gguf	Q6_K	6.84GB	false	Very high quality, near perfect, recommended.
Ling-3.0-tiny-Q5_K_L.gguf	Q5_K_L	5.87GB	false	Uses Q8_0 for embed and output weights. High quality, recommended.
Ling-3.0-tiny-Q5_K_M.gguf	Q5_K_M	5.72GB	false	High quality, recommended.
Ling-3.0-tiny-Q5_K_S.gguf	Q5_K_S	5.55GB	false	High quality, recommended.
Ling-3.0-tiny-Q4_K_L.gguf	Q4_K_L	5.10GB	false	Uses Q8_0 for embed and output weights. Good quality, recommended.
Ling-3.0-tiny-Q4_1.gguf	Q4_1	5.08GB	false	Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon.
Ling-3.0-tiny-Q4_K_M.gguf	Q4_K_M	4.92GB	false	Good quality, default size for most use cases, recommended.
Ling-3.0-tiny-Q4_K_S.gguf	Q4_K_S	4.75GB	false	Slightly lower quality with more space savings, recommended.
Ling-3.0-tiny-Q4_0.gguf	Q4_0	4.62GB	false	Legacy format, kept for compatibility with older tools.
Ling-3.0-tiny-IQ4_NL.gguf	IQ4_NL	4.62GB	false	Similar to IQ4_XS, but slightly larger.
Ling-3.0-tiny-IQ4_XS.gguf	IQ4_XS	4.39GB	false	Decent quality, smaller than Q4_K_S with similar performance, recommended.
Ling-3.0-tiny-Q3_K_XL.gguf	Q3_K_XL	4.13GB	false	Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability.
Ling-3.0-tiny-IQ3_M.gguf	IQ3_M	3.93GB	false	Medium-low quality, new method with decent performance comparable to Q3_K_M.
Ling-3.0-tiny-Q3_K_L.gguf	Q3_K_L	3.91GB	false	Lower quality but usable, good for low RAM availability.
Ling-3.0-tiny-Q3_K_M.gguf	Q3_K_M	3.79GB	false	Low quality.
Ling-3.0-tiny-IQ3_XS.gguf	IQ3_XS	3.78GB	false	Lower quality, new method with decent performance, slightly better than Q3_K_S.
Ling-3.0-tiny-Q3_K_S.gguf	Q3_K_S	3.64GB	false	Low quality, not recommended.
Ling-3.0-tiny-IQ3_XXS.gguf	IQ3_XXS	3.46GB	false	Lower quality, new method with decent performance, comparable to Q3 quants.
Ling-3.0-tiny-Q2_K_L.gguf	Q2_K_L	3.24GB	false	Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable.
Ling-3.0-tiny-Q2_K.gguf	Q2_K	3.00GB	false	Very low quality but surprisingly usable.
Ling-3.0-tiny-IQ2_M.gguf	IQ2_M	2.83GB	false	Relatively low quality, uses SOTA techniques to be surprisingly usable.

Download a specific file:

hf download bartowski/Ling-3.0-tiny-GGUF --include "Ling-3.0-tiny-Q4_K_M.gguf" --local-dir ./

Downloading using the Hugging Face CLI

Click to view download instructions

First, make sure you have the Hugging Face CLI installed:

pip install -U "huggingface_hub[cli]"

Download a specific file:

hf download bartowski/Ling-3.0-tiny-GGUF --include "Ling-3.0-tiny-Q4_K_M.gguf" --local-dir ./

How to run

These quants run with llama.cpp - installable in one line via llama.app:

curl -LsSf https://llama.app/install.sh | sh
llama-server -hf bartowski/Ling-3.0-tiny-GGUF:Q4_K_M

llama-server includes a built-in chat web UI, served at http://localhost:8080 by default.

These quants were made with llama.cpp release b10472 - if this model's architecture is newly supported, you'll need that release or newer to run them.

They also work in: LM Studio · koboldcpp · ramalama · Jan AI · Text Generation Web UI · LoLLMs · Atomic Chat

imatrix

All quants made using imatrix option, with a calibration corpus rendered through this model's own chat template. The corpus pairs plain prose with tool-calling and reasoning conversations (corpus source data), encoded exactly as this model sees them at inference and processed with --parse-special, so chat-format special tokens contribute to the importance matrix. The corpus rendered for this model is included in this repo: Ling-3.0-tiny-calibration-v6.txt. The imatrix is available here: Ling-3.0-tiny-imatrix.gguf.

Calibration render details

{
  "generator": "auto_quant_v2 calibration renderer",
  "recipe": "calibration-v6",
  "model": "Ling-3.0-tiny",
  "encoder": "chat_template",
  "chunk_size": 512,
  "prose_chunks": 220,
  "tool_chunks": 345,
  "total_chunks": 565,
  "tool_chunk_fraction": 0.611,
  "n_conversations": 137,
  "extension_convs_used": 0,
  "conversation_token_lengths": [
    523,
    1594,
    1193,
    1476,
    1046,
    1300,
    3127,
    754,
    1163,
    1353,
    1019,
    2059,
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    1200,
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    1099,
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    1326,
    990,
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    1167,
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    1463,
    1601,
    844,
    1376,
    1604,
    1472,
    1161,
    1211,
    1003,
    1019,
    1650,
    1619,
    1147,
    433,
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    1392,
    1048,
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    1973,
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    955,
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    840,
    1100,
    1045,
    1166,
    1133,
    868,
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    1114,
    1530,
    873,
    1483,
    2099,
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    333,
    1071,
    3285,
    2856,
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    944,
    809,
    1266,
    793,
    668,
    1711,
    965,
    880,
    1240,
    1409
  ],
  "warnings": []
}

Embed/output weights

Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.

ARM/AVX information

llama.cpp automatically "repacks" weights into an interleaved layout at load time for faster inference on ARM and AVX machines - details in this PR. This once required downloading special Q4_0_4_4/4_8/8_8 files; those are long gone. Online repacking now covers Q4_0, IQ4_NL, and most K-quants, so no special quant choice is needed for CPU inference.

Which file should I choose?

Click here for details

An older (early 2024) but still useful write-up with charts comparing quant performances is provided by Artefact2 here

The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.

If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.

If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.

Hugging Face can also do this math for you: add your hardware in your Local Apps settings and the model page will show which files fit.

Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.

If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.

If you want to get more into the weeds, you can check out this extremely useful feature chart:

llama.cpp feature matrix

But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.

These I-quants can also be used on CPU, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.

Credits

Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset.

Thank you ZeroWw for the inspiration to experiment with embed/output.

Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski

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GGUF

Model size

8B params

Architecture

bailingmoe3

Hardware compatibility

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