Instructions to use bloomer010/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 bloomer010/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 bloomer010/Ling-3.0-tiny-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bloomer010/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 bloomer010/Ling-3.0-tiny-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bloomer010/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 bloomer010/Ling-3.0-tiny-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf bloomer010/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 bloomer010/Ling-3.0-tiny-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bloomer010/Ling-3.0-tiny-GGUF:Q4_K_M

Use Docker

docker model run hf.co/bloomer010/Ling-3.0-tiny-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use bloomer010/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 "bloomer010/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": "bloomer010/Ling-3.0-tiny-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bloomer010/Ling-3.0-tiny-GGUF:Q4_K_M

Ollama
How to use bloomer010/Ling-3.0-tiny-GGUF with Ollama:
```
ollama run hf.co/bloomer010/Ling-3.0-tiny-GGUF:Q4_K_M
```

Unsloth Studio

How to use bloomer010/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 bloomer010/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 bloomer010/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 bloomer010/Ling-3.0-tiny-GGUF to start chatting

How to use bloomer010/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 bloomer010/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": "bloomer010/Ling-3.0-tiny-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use bloomer010/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 bloomer010/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 "bloomer010/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 bloomer010/Ling-3.0-tiny-GGUF with Docker Model Runner:
```
docker model run hf.co/bloomer010/Ling-3.0-tiny-GGUF:Q4_K_M
```

Lemonade

How to use bloomer010/Ling-3.0-tiny-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bloomer010/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 bloomer010/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 bloomer010/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 bloomer010/Ling-3.0-tiny-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

Ling-3.0-tiny-GGUF / README.md

bloomer010

Add UD-Q6_K_XL and UD-Q8_K_XL quants

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metadata

license: mit
base_model:
  - inclusionAI/Ling-3.0-tiny
pipeline_tag: text-generation
tags:
  - gguf
  - bailingmoe3
  - mixture-of-experts
  - conversational

Ling-3.0-tiny GGUF

GGUF conversions of inclusionAI/Ling-3.0-tiny, converted directly from the released BF16 safetensors.

Compatibility

Ling-3.0-tiny uses the new bailingmoe3 GGUF architecture and its Q-LoRA attention path. Until support is merged upstream, use the following llama.cpp branch:

https://github.com/aetherbird/llama.cpp/tree/bailingmoe3-support

Stock llama.cpp builds without BailingMoE3 support will not load these files.

Upstream PR: https://github.com/ggml-org/llama.cpp/pull/26608

Files

Quant	Size
BF16	15.8 GB
UD-Q8_K_XL	11.19 GB
Q8_0	8.41 GB
UD-Q6_K_XL	7.27 GB
Q6_K	6.50 GB
Q5_K_M	5.64 GB
Q5_K_S	5.48 GB
Q4_K_M	4.82 GB
MXFP4_MOE	4.72 GB
Q3_K_M	3.84 GB
IQ2_M	2.70 GB
Q1_0	1.30 GB

Importance Matrix

IQ2_M was generated with a model-specific importance matrix:

Wikitext-2 raw training text
100 chunks
512 tokens per chunk
51,200 calibration tokens total
332 matrix entries

XL Quantization Recipes

UD-Q8_K_XL uses Q8_0 for the main expert gate and up tensors. Token embeddings, expert down projections, attention and Q-LoRA projections, and KDA projections remain BF16.

UD-Q6_K_XL uses Q6_K for the main expert gate and up tensors. Token embeddings, output weights, expert down projections, attention and Q-LoRA projections, and KDA projections use Q8_0. It was generated with the importance matrix described above.

Architecture

7.9B total parameters and 1.3B active parameters per token
24 layers: 18 KDA layers and 6 MLA layers
128 routed experts, 8 active per token, plus 1 shared expert
Q-LoRA rank 256 and KV-LoRA rank 512
131,072-token context in the released configuration
No bundled MTP block (num_nextn_predict_layers: 0)

Validation

BF16 conversion completed with 526 tensors, including all 18 Q-LoRA tensors
CPU and CUDA architecture tests passed
BF16, Q8_0, Q6_K, Q4_K_M, and MXFP4_MOE loaded and generated tokens with CUDA
Q1_0, IQ2_M, Q3_K_M, Q5_K_S, and Q5_K_M passed CPU-only prompt processing and token generation tests
UD-Q6_K_XL and UD-Q8_K_XL passed CPU-only prompt processing and token generation tests
CUDA testing used an RTX 4070 and RTX 3060

Build

git clone --branch bailingmoe3-support https://github.com/aetherbird/llama.cpp.git
cd llama.cpp
cmake -B build -DGGML_CUDA=ON
cmake --build build --config Release -j --target llama-cli llama-server

Usage

./build/bin/llama-server \
  -m Ling-3.0-tiny-Q4_K_M.gguf \
  -c 131072 \
  -ngl auto \
  --flash-attn auto

Thinking is enabled by default. Recommended sampling parameters from the source model card are temperature=1.0, top_p=0.95, and top_k=20.