---

license: mit

base_model:

- inclusionAI/Ling-3.0-tiny

pipeline_tag: text-generation

library_name: llama.cpp

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.

🔔 2026-08-21: added reasoning_effort support (low = thinking off, high = on, default same).

If you want reasoning_effort, re-download or override with chat_template.jinja.

🎉 bailingmoe3 (including the Q-LoRA attention path) is supported in stock llama.cpp since

PR #26608 (merged 2026-08-17, commit

3733366720). Any build from that commit onward loads these files directly:

llama-server -hf bloomer010/Ling-3.0-tiny-GGUF:Q4_K_M

Files

For tiny models, precision is especially crucial.

Generally...

Larger files = more precision.

More compression = more slop and misbehavin'.

Use UD-Q8_K_XL for near-full precision performance.

| Quant | Size | your memory |

| --- | ---: | --- |

| BF16 | 15.8 GB | 16 GB+ |

| UD-Q8_K_XL | 11.19 GB | 12 GB+ |

| Q8_0 | 8.41 GB | 10 GB+ |

| UD-Q6_K_XL | 7.27 GB | 8 GB+ |

| Q6_K | 6.50 GB | 8 GB+ |

| Q5_K_M | 5.64 GB | 7 GB+ |

| Q5_K_S | 5.48 GB | 6 GB+ |

| Q5_0 | 5.48 GB | 6 GB+ |

| Q4_K_M | 4.82 GB | 6 GB+ |

| Q4_K_S | 4.55 GB | 6 GB+ |

| Q4_0 | 4.53 GB | 6 GB+ |

| MXFP4_MOE | 4.72 GB | 6 GB+ ¹ |

| IQ4_XS | 4.29 GB | 5 GB+ |

| Q3_K_M | 3.84 GB | 5 GB+ |

| Q3_K_S | 3.51 GB | 5 GB+ |

| IQ3_S | 3.51 GB | 4 GB+ |

| IQ3_XXS | 3.13 GB | 4 GB+ |

| Q2_K | 2.99 GB | 4 GB+ |

| IQ2_M | 2.70 GB | 3 GB+ |

| IQ2_S | 2.48 GB | 3 GB+ |

| IQ2_XS | 2.43 GB | 3 GB+ |

| IQ2_XXS | 2.21 GB | 3 GB+ |

| IQ1_M | 1.93 GB | 3 GB+ |

| IQ1_S | 1.76 GB | 2 GB+ |

| Q1_0 | 1.30 GB | 2 GB+ |

¹ MXFP4_MOE runs its native path on MXFP4-capable GPUs (Blackwell RTX 50-series, GB10/DGX

Spark). Elsewhere it falls back to a slower dequant path — prefer a K-quant on older hardware.

Importance Matrix

The IQ-quant rungs (IQ1_S through IQ4_XS) were 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 for this model (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
• IQ1_S, IQ1_M, IQ2_S, IQ2_XS, IQ2_XXS, IQ3_XXS, IQ3_S, IQ4_XS, Q2_K, Q3_K_S, Q4_K_S, Q4_0, and

Q5_0 passed load and generation tests

• CUDA testing used an RTX 4070 and RTX 3060

Build

git clone https://github.com/ggml-org/llama.cpp.git   # bailingmoe3 merged 2026-08-17
# pre-merge builds:
# 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 \
  --temp 1.0 --top-p 0.95 --top-k 20 \
  --jinja

Thinking is enabled by default; disable per request with

"chat_template_kwargs": {"enable_thinking": false}. Recommended sampling parameters from the

source model card are temperature=1.0, top_p=0.95, and top_k=20.