Instructions to use IHaveNoClueAndIMustPost/Ling-3.0-flash-SUPER-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 IHaveNoClueAndIMustPost/Ling-3.0-flash-SUPER-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 IHaveNoClueAndIMustPost/Ling-3.0-flash-SUPER-GGUF:Q3_K_M
# Run inference directly in the terminal:
llama cli -hf IHaveNoClueAndIMustPost/Ling-3.0-flash-SUPER-GGUF:Q3_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf IHaveNoClueAndIMustPost/Ling-3.0-flash-SUPER-GGUF:Q3_K_M
# Run inference directly in the terminal:
llama cli -hf IHaveNoClueAndIMustPost/Ling-3.0-flash-SUPER-GGUF:Q3_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 IHaveNoClueAndIMustPost/Ling-3.0-flash-SUPER-GGUF:Q3_K_M
# Run inference directly in the terminal:
./llama-cli -hf IHaveNoClueAndIMustPost/Ling-3.0-flash-SUPER-GGUF:Q3_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 IHaveNoClueAndIMustPost/Ling-3.0-flash-SUPER-GGUF:Q3_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf IHaveNoClueAndIMustPost/Ling-3.0-flash-SUPER-GGUF:Q3_K_M

Use Docker

docker model run hf.co/IHaveNoClueAndIMustPost/Ling-3.0-flash-SUPER-GGUF:Q3_K_M

LM Studio
Jan
Ollama
How to use IHaveNoClueAndIMustPost/Ling-3.0-flash-SUPER-GGUF with Ollama:
```
ollama run hf.co/IHaveNoClueAndIMustPost/Ling-3.0-flash-SUPER-GGUF:Q3_K_M
```
Unsloth Desktop

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

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf IHaveNoClueAndIMustPost/Ling-3.0-flash-SUPER-GGUF:Q3_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "IHaveNoClueAndIMustPost/Ling-3.0-flash-SUPER-GGUF:Q3_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use IHaveNoClueAndIMustPost/Ling-3.0-flash-SUPER-GGUF with Docker Model Runner:
```
docker model run hf.co/IHaveNoClueAndIMustPost/Ling-3.0-flash-SUPER-GGUF:Q3_K_M
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull IHaveNoClueAndIMustPost/Ling-3.0-flash-SUPER-GGUF:Q3_K_M

Run and chat with the model

lemonade run user.Ling-3.0-flash-SUPER-GGUF-Q3_K_M

List all available models

lemonade list

Hermes Agent

How to use IHaveNoClueAndIMustPost/Ling-3.0-flash-SUPER-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 IHaveNoClueAndIMustPost/Ling-3.0-flash-SUPER-GGUF:Q3_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 IHaveNoClueAndIMustPost/Ling-3.0-flash-SUPER-GGUF:Q3_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

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

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf IHaveNoClueAndIMustPost/Ling-3.0-flash-SUPER-GGUF:Q3_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 "IHaveNoClueAndIMustPost/Ling-3.0-flash-SUPER-GGUF:Q3_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"

Mixed-precision GGUF quants of inclusionAI/Ling-3.0-flash.

Zero slop TLDR: Just two vibe-quanted versions attempting to optimize size vs performance. Only recommended as temporary replacements until the pro-quanters release their dynamic versions.

Two builds are provided that cover two VRAM budgets, 64GB with some slight offloading and 96GB+ without.

File	Size	Strategy	PPL vs F16	Best for
`Ling-3.0-flash-f16.gguf`	243,268 MiB (~237.6 GiB)	Full precision (baseline)	4.3532 (reference)	Full size. N/A
`Ling-3.0-flash-SUPER-Q4_K_M.gguf`	82,476 MiB (~80.5 GiB)	Q8_0 signal path + Q4_K expert bulk	4.3849 (+0.73%)	96-128+ GB VRAM (long context); 64 GB with heavier offload
`Ling-3.0-flash-SUPER-Q3_K_M.gguf`	64,183 MiB (~62.7 GiB)	Q6_K signal path + Q3_K expert bulk	4.5289 (+4.04%)	~64 GB (2×32 GB), light offload
`Ling-3.0-flash-SUPER-Q2_K.gguf`	61,776 MiB (~60.3 GiB)	Q8_0 signal path + Q2_K expert bulk	4.9592 (+13.92%)	Failure reference. N/A

Reference: the full-precision F16 model scores PPL = 4.3532 ± 0.0256 on the wikitext-2 test set.

Performance

On the dual V100 32GB target system the diffence in TG was approx 10-15% between the different models, hovering around 30-35t/s with ngram drafting. This is an "e-waste" system with some non-standard/non-recommended build decisions so the numbers are likely worst rather than best case scenario.

Known model quirks

Repetition looping. Ling-3.0-flash can occasionally fall into a repetition loop. This is inherent to the full F16 model, not introduced by quantization, and is much rarer in the latest revision of the model. Standard repetition penalties / sampler settings apply.
MTP / speculative-decoding speed. The model's native MTP (multi-token prediction) path is reported to run slower than expected, which is mentioned in the PR and verified on my own system. The launch commands below therefore use ngram speculative decoding (--spec-type ngram-mod) only.
Recommendation: try the MTP and ngram combination first with --spec-type ngram-mod,draft-mtp, then ngram-only, and compare the results.

Ling-3.0-flash-SUPER-Q4_K_M (82,476 MiB, ~80.5 GiB)

Quantization method. Mixed-precision k-quant, no imatrix (deliberate — a uniform, calibration-free quant avoids biasing a general-purpose model toward one corpus). The "SUPER" tiering keeps the precision-sensitive signal path high and spends the savings on the expert bulk:

Q8_0 — the signal path: all attention (q/k/v/output/gate + MQA k_b/v_b/ kv_a_mqa), the gated-linear-attention (SSM) projections, the shared expert, the embedding, the MTP head, and the input/output edge experts (blocks 2-3 and 39-42).
Q6_K — a periodic set of down ("cycle-down") experts spaced through the stack, plus the two dense-block down projections; these act as high-fidelity checkpoints that stop quantization error accumulating down the model.
Q4_K — the remaining (middle) routed-expert bulk, where most of the size is saved.
F32 — all norms, routers, and small SSM params (auto-preserved); F16 — the final output.weight.

Type distribution (938 tensors): 436 Q8_0, 96 Q4_K, 15 Q6_K, 390 F32, 1 F16.

Perplexity (wikitext-2 test set, n_ctx=512):

PPL = 4.3849 ± 0.0258 vs F16 4.3532 → +0.0317 abs / +0.73%.
Full per-chunk logs (in this repo): Ling-PPL-Q4_K_M.txt (baseline Ling-PPL-F16.txt).

Launch (2×32 GB V100 / 64 GB total, reference config):

./llama-server -fa 1 -m Ling-3.0-flash-SUPER-Q4_K_M.gguf \
  --host 0.0.0.0 --port 8080 -c 131072 -ngl 99 \
  --load-mode none --parallel 1 -ctxcp 96 -cram -1 \
  -ub 1024 -b 4096 --spec-type ngram-mod \
  -ctk q8_0 -ctv q8_0 -t 8 -ts 7,4 -ncmoe 14

Ling-3.0-flash-SUPER-Q3_K_M (64,183 MiB, ~62.7 GiB)

Quantization method. Same tensor roles as the Q4_K_M, but the whole precision curve is flattened one step: the expensive top is demoted in order to pay for a higher-precision bulk, which is where the quality actually lives.

Q6_K — the signal path + edge experts (same set that is Q8_0 above).
Q5_K — the "cycle-down" experts + dense down projections.
Q3_K — the middle routed-expert bulk.
F32 — norms / routers / SSM params; F16 — output.weight.

Eight attn_k_b tensors keep Q8_0: their shape cannot be represented by any K-quant, so they fall back to the nearest compatible block type.

Type distribution (938 tensors): 428 Q6_K, 92 Q3_K, 15 Q5_K, 8 Q8_0, 4 Q4_K, 390 F32, 1 F16.

Perplexity (wikitext-2 test set, n_ctx=512):

PPL = 4.5289 ± 0.0267 vs F16 4.3532 → +0.1757 abs / +4.04%.
Full per-chunk logs (in this repo): Ling-PPL-Q3_K_M.txt (baseline Ling-PPL-F16.txt).

Launch (2×32 GB V100 / 64 GB total, reference config):

./llama-server -fa 1 -m Ling-3.0-flash-SUPER-Q3_K_M.gguf \
  --host 0.0.0.0 --port 8080 -c 131072 -ngl 99 \
  --load-mode none --parallel 1 -ctxcp 96 -cram -1 \
  -ub 1024 -b 4096 --spec-type ngram-mod \
  -ctk q8_0 -ctv q8_0 -t 8 -ts 7,6 -ncmoe 5

Credits

For great models and quantization guidance:
ubergarm
AesSedai
bloomer010
AtomicChat

For actually doing the heavy lifting:
Qwen-chan

For just tossing some prompts at an LLM:
Me. I did that.

Downloads last month: 248

GGUF

Model size

127B params

Architecture

bailingmoe3

Hardware compatibility

3-bit

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Model tree for IHaveNoClueAndIMustPost/Ling-3.0-flash-SUPER-GGUF

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