---

license: mit

base_model: inclusionAI/Ling-3.0-flash-VL

library_name: llama.cpp

pipeline_tag: image-text-to-text

tags:

• gguf
• experimental

---

How to Run Ling 3.0 Flash VL Locally

<p style="margin-top: 0; margin-bottom: 0;">

<em>Built from InclusionAI's original weights using Atomic Chat's existing Ling importance matrix. The <a href="https://huggingface.co/datasets/AtomicChat/calib-corpora">calibration corpora</a> behind our builds are public.</em>

</p>

<div style="display: flex; gap: 8px; align-items: center; margin-top: 10px; margin-bottom: 10px;">

<a href="https://atomic.chat/?utm_source=huggingface&utm_medium=referral&utm_campaign=hf_ling_3_0_flash_vl&utm_content=btn_atomic"><img src="https://huggingface.co/AtomicChat/Ling-3.0-flash-VL-GGUF/resolve/main/btn_atomic.png" width="162" alt="Atomic Chat"></a>

<a href="https://discord.gg/8wGSsvmg4V"><img src="https://huggingface.co/AtomicChat/Ling-3.0-flash-VL-GGUF/resolve/main/btn_discord.png" width="119" alt="Discord"></a>

<a href="https://github.com/AtomicBot-ai/Atomic-Chat"><img src="https://huggingface.co/AtomicChat/Ling-3.0-flash-VL-GGUF/resolve/main/btn_github.png" width="115" alt="GitHub"></a>

</div>

<ul style="margin: 0 0 12px 0;">

<li>Ling 3.0 Flash VL is InclusionAI's vision-language model for text and image inputs.</li>

<li>Choose AD-Q4_K_M, AD-Q5_K_M, AD-Q6_K, or AD-Q8_0. Image inputs use the shared F32 vision projector.</li>

<li>These builds are an experimental preview. Use the accompanying runtime; full quality validation is still in progress.</li>

</ul>

<hr style="margin: 0 0 16px 0;">

Prepared from the original inclusionAI checkpoint, revision 869591498e8dbb41d4d96e3e2a5b428a2f70eb1e.

These are the Atomic AD layouts, made on CPU using the existing calibration iMatrix. The full nine-variant text comparison and CPU speed measurements are complete; see FINAL_REPORT.md. This repository is an experimental working preview, not a completed benchmark release.

Uploads arrive progressively. Check UPLOAD_STATUS.json before downloading a variant. Each language variant requires all six GGUF shards in the same directory; select shard 00001 when loading.

| Variant | Complete language files, decimal GB |

|---|---:|

| AD-Q4_K_M | 79.30 |

| AD-Q5_K_M | 89.44 |

| AD-Q6_K | 107.14 |

| AD-Q8_0 | 132.73 |

Vision requires the shared F32 mmproj, an additional 1.74 GB. File size is not a RAM requirement estimate.

Runtime requirement

Use the accompanying private runtime patch and added source files, based on AtomicBot-ai/atomic-llama-cpp-turboquant@cd560939087c95b93a1f30a95603d6b079436952. Stock llama.cpp and the released Atomic Chat app have not been validated for these artifacts. See RUNTIME.md.

Checks completed

All four variants: 917 tensor types/shapes verified, six-shard integrity checked, complete SHA-256 manifests, and all 382 protected F32 tensor payloads unchanged from BF16. Text and a spatial image passed on all four. Q4/Q6/Q8 additionally passed the OCR and object-count smoke cases. These are functional smoke checks, not a comprehensive vision benchmark.

Full text quality comparisons use the historical held-out 92 × 4096 protocol and a fresh BF16 reference from this checkpoint. Full text results are available in FINAL_REPORT.md. Video and maximum context are not validated.

The existing iMatrix comes from AtomicChat/Ling-3.0-flash-GGUF@253738fe190c15f329001f263f355fc1562bbe7c, SHA-256 7d3c0ebe9eb235cc08e0b7c91886f5c422772ea95eebbf0f53b0974a1c040991. Its 573 entries match the new language tensor dimensions. Four routed experts have no observations in that matrix; uniform importance was used for those entries.

manifest.json and SHA256SUMS describe the complete intended set. Actual upload completion is recorded separately in UPLOAD_STATUS.json.

Completed measurements

Full report · KLD chart · Metrics CSV.

The pilot results are separate from the full 92-block comparison. Completion is not a comprehensive vision or agentic quality certification.