Instructions to use ljupco/Ling-3.0-flash-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 ljupco/Ling-3.0-flash-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 ljupco/Ling-3.0-flash-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf ljupco/Ling-3.0-flash-GGUF:IQ4_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ljupco/Ling-3.0-flash-GGUF:IQ4_XS
# Run inference directly in the terminal:
llama cli -hf ljupco/Ling-3.0-flash-GGUF:IQ4_XS

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 ljupco/Ling-3.0-flash-GGUF:IQ4_XS
# Run inference directly in the terminal:
./llama-cli -hf ljupco/Ling-3.0-flash-GGUF:IQ4_XS

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 ljupco/Ling-3.0-flash-GGUF:IQ4_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ljupco/Ling-3.0-flash-GGUF:IQ4_XS

Use Docker

docker model run hf.co/ljupco/Ling-3.0-flash-GGUF:IQ4_XS

LM Studio
Jan

vLLM

How to use ljupco/Ling-3.0-flash-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ljupco/Ling-3.0-flash-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": "ljupco/Ling-3.0-flash-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ljupco/Ling-3.0-flash-GGUF:IQ4_XS

Ollama
How to use ljupco/Ling-3.0-flash-GGUF with Ollama:
```
ollama run hf.co/ljupco/Ling-3.0-flash-GGUF:IQ4_XS
```

Unsloth Studio

How to use ljupco/Ling-3.0-flash-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 ljupco/Ling-3.0-flash-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 ljupco/Ling-3.0-flash-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

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

How to use ljupco/Ling-3.0-flash-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ljupco/Ling-3.0-flash-GGUF:IQ4_XS

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": "ljupco/Ling-3.0-flash-GGUF:IQ4_XS"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use ljupco/Ling-3.0-flash-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ljupco/Ling-3.0-flash-GGUF:IQ4_XS

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 "ljupco/Ling-3.0-flash-GGUF:IQ4_XS" \
  --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 ljupco/Ling-3.0-flash-GGUF with Docker Model Runner:
```
docker model run hf.co/ljupco/Ling-3.0-flash-GGUF:IQ4_XS
```

Lemonade

How to use ljupco/Ling-3.0-flash-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ljupco/Ling-3.0-flash-GGUF:IQ4_XS

Run and chat with the model

lemonade run user.Ling-3.0-flash-GGUF-IQ4_XS

List all available models

lemonade list

Hermes Agent

How to use ljupco/Ling-3.0-flash-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 ljupco/Ling-3.0-flash-GGUF:IQ4_XS

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 ljupco/Ling-3.0-flash-GGUF:IQ4_XS

Run Hermes

hermes

Atomic Chat

Ling-3.0-flash-GGUF

Canonical bailingmoe3-arch GGUF of inclusionAI/Ling-3.0-flash — a 124B-parameter hybrid-linear MoE (~5B active; 512 experts/8 used; 42 layers = 35 KDA recurrent + 7 gated-MLA attention; one NextN/MTP head) — converted and quantized on Apple Silicon, with full provenance for independent reproduction.

File	Quant	Size	Arch
`Ling-3.0-flash-bailingmoe3-IQ4_XS.gguf`	IQ4_XS (~4.3 bpw)	64 GB	`bailingmoe3` (canonical)

This file loads without shims or overrides in the runtime linked below (ssm_a is exp'd at conversion, kda.gate_lower_bound is in the file).

Why this file exists

The HuggingFace checkpoint is bf16 (~240 GB) and uses model_type: bailing_hybrid, which upstream llama.cpp does not support. This repo publishes a canonical bailingmoe3-arch GGUF so anyone can run the model on Apple Silicon without re-deriving the (non-trivial) conversion.

Repositories — everything needed to reproduce

What	Where
Runtime (this GGUF's home)	ljubomirj/Ling-3.0-flash-llama.cpp — standalone llama.cpp fork with BailingMoE3 support (provenance + research summary in README)
Living branch the repo was split from	ljubomirj/llama.cpp branch `atomic-bailingmoe3` — same code, keeps advancing. Built on top of the original AtomicBot-ai/atomic-llama-cpp-turboquant (Atomic llama.cpp, `feat/bailingmoe3`) — huge thanks to the Atomic team for the bailingmoe3 architecture support this is derived from
Upstream llama.cpp	ggml-org/llama.cpp
Upstream BailingMoE3 support (PR #26608)	ggml-org/llama.cpp#26608 — aetherbird's converter (`conversion/bailingmoe3.py`) used for this file
Original checkpoint	inclusionAI/Ling-3.0-flash (bf16, ~240 GB, 24 shards)
Early non-canonical GGUFs (legacy `bailing-hybrid` arch)	aj9o9/Ling-3.0-flash-GGUF — same model, older arch string; loads via the legacy shim in the runtime

Reproduce this file (from scratch)

Ingredients: the bf16 checkpoint, the PR-26608 converter, llama.cpp's quantizer.

# 1. Download the bf16 checkpoint (~240 GB)
huggingface-cli download inclusionAI/Ling-3.0-flash --local-dir $NV/inclusionAI-Ling-3.0-flash

# 2. Convert to f16 GGUF using the PR-26608 (aetherbird) converter
#    (conversion/bailingmoe3.py in the PR; registers BailingMoeV3ForCausalLM
#     -> model_arch bailingmoe3, exp's A_log, writes kda.safe_gate + lower_bound)
cd <aetherbird-llama.cpp>   # PR #26608
python3 -m convert_hf_to_gguf $NV/inclusionAI-Ling-3.0-flash \
  --outtype f16 --outfile $NV/Ling-3.0-flash-f16.gguf --split-max-size 40G

# 3. Quantize to IQ4_XS
llama-quantize $NV/Ling-3.0-flash-f16-00001-of-00007.gguf \
  $NV/Ling-3.0-flash-bailingmoe3-IQ4_XS.gguf IQ4_XS

Disk discipline: peak ~540 GiB (bf16 240 + f16 255 + IQ4_XS 64); delete the bf16 checkpoint after step 2 to stay within ~700 GiB.

Run it

git clone git@github.com:ljubomirj/Ling-3.0-flash-llama.cpp.git
cd Ling-3.0-flash-llama.cpp
cmake -S . -B build -DCMAKE_BUILD_TYPE=Release \
  -DGGML_METAL=ON -DGGML_METAL_EMBED_LIBRARY=ON -DGGML_ACCELERATE=ON
cmake --build build --config Release --target llama-cli llama-server -j8

./build/bin/llama-cli -m Ling-3.0-flash-bailingmoe3-IQ4_XS.gguf \
  -ngl 99 -p "The capital of France is" -n 16

Measured performance (Apple M2 Max 96 GB, Metal)

llama-bench -m Ling-3.0-flash-bailingmoe3-IQ4_XS.gguf -ngl 99 -r 2:

Test	t/s
pp2048 @ d0	301.5
pp2048 @ d29664	154.4
tg128 @ d0	35.0
tg128 @ d29664	24.3

Decode is memory-bandwidth-bound — M2 Max ~400 GB/s ceiling. Roofline check: ~2.96 GB active bytes per token at 8/512 experts (only the selected experts are read; 97% of the file is MoE weights) → 400 GB/s ÷ 2.96 GB ≈ 135 t/s ceiling, and we measure 35 t/s — only ~27% of the roofline. There is clearly a lot of scope for improvement here. Flash attention is enabled by default and mandatory at depth (fa=0 collapses to ~5 t/s @64K).

Verification / golden logits

The conversion was validated against an independent HF-bf16 golden reference (golden-hf-bf16.json: 16 steps, full logits, vocab 157,184), captured via a torch shim of fla-core's KDA kernels (triton is unavailable on macOS). The first-token distributions match within IQ4_XS noise (p(198): golden 0.296 vs this file 0.370); later divergence is near-tie argmax flips compounding through the recurrent state — no graph bug. Details in the runtime repo's README.

License / provenance notes

Model: inclusionAI/Ling-3.0-flash (check its license/card for usage terms).
Conversion tooling: PR-26608 (Apache-2.0, llama.cpp).
Runtime: ljubomirj/Ling-3.0-flash-llama.cpp, derived from AtomicBot-ai/atomic-llama-cpp-turboquant feat/bailingmoe3 — the Atomic team's bailingmoe3 architecture support is the foundation this work builds on; our thanks. Atomic llama.cpp itself is a fork of ggml-org/llama.cpp.

Downloads last month: -

GGUF

Model size

127B params

Architecture

bailingmoe3

Hardware compatibility

4-bit

Model tree for ljupco/Ling-3.0-flash-GGUF

Base model

inclusionAI/Ling-3.0-flash

Quantized

(24)

this model