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gnukeith/Ocelot-GGUF overview

Ocelot 1 VL GGUF Merged and quantized GGUF builds of bravesoftware/Ocelot 1 VL https://huggingface.co/bravesoftware/Ocelot 1 VL , a Qwen3 VL 4B LoRA specialize…

ggufllama.cppqwen3-vlvisionsummarizationimage-text-to-textbase_model:Qwen/Qwen3-VL-4B-Instructbase_model:quantized:Qwen/Qwen3-VL-4B-Instructlicense:apache-2.0endpoints_compatibleregion:usconversational

Runs locally from ~432.9 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).

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Repository Files & Downloads

17 GGUF files detected
Direct downloads for local inference
FileTypeQuantizationSizeLink
Ocelot-1-VL-BF16.ggufGGUFBF167.50 GBDownload
Ocelot-1-VL-IQ4_NL.ggufGGUFIQ4_NL2.23 GBDownload
Ocelot-1-VL-IQ4_XS.ggufGGUFIQ4_XS2.13 GBDownload
Ocelot-1-VL-Q2_K.ggufGGUFQ2_K1.55 GBDownload
Ocelot-1-VL-Q3_K_L.ggufGGUFQ3_K_L2.09 GBDownload
Ocelot-1-VL-Q3_K_M.ggufGGUFQ3_K_M1.93 GBDownload
Ocelot-1-VL-Q3_K_S.ggufGGUFQ3_K_S1.76 GBDownload
Ocelot-1-VL-Q4_0.ggufGGUFQ4_02.21 GBDownload
Ocelot-1-VL-Q4_1.ggufGGUFQ4_12.42 GBDownload
Ocelot-1-VL-Q4_K_M.ggufGGUFQ4_K_M2.33 GBDownload
Ocelot-1-VL-Q4_K_S.ggufGGUFQ4_K_S2.22 GBDownload
Ocelot-1-VL-Q5_K_M.ggufGGUFQ5_K_M2.69 GBDownload
Ocelot-1-VL-Q5_K_S.ggufGGUFQ5_K_S2.63 GBDownload
Ocelot-1-VL-Q6_K.ggufGGUFQ6_K3.08 GBDownload
Ocelot-1-VL-Q8_0.ggufGGUFQ8_03.99 GBDownload
mmproj-Ocelot-1-VL-BF16.ggufGGUFBF16800.4 MBDownload
mmproj-Ocelot-1-VL-Q8_0.ggufGGUFQ8_0432.9 MBDownload

Model Details

Model IDgnukeith/Ocelot-GGUF
Authorgnukeith
Pipelineimage-text-to-text
Licenseapache-2.0
Base modelbravesoftware/Ocelot-1-VL,Qwen/Qwen3-VL-4B-Instruct
Last modified2026-07-22T18:04:33.000Z

Model README

---

license: apache-2.0

base_model:

  • bravesoftware/Ocelot-1-VL
  • Qwen/Qwen3-VL-4B-Instruct

pipeline_tag: image-text-to-text

tags: [gguf, llama.cpp, qwen3-vl, vision, summarization]

---

Ocelot-1-VL GGUF

Merged and quantized GGUF builds of bravesoftware/Ocelot-1-VL, a Qwen3-VL-4B LoRA specialized for webpage summarization from page text or screenshots.

The Ocelot LoRA was merged into Qwen/Qwen3-VL-4B-Instruct in BF16 before conversion. The vision projector is separate and is unchanged by the language-side LoRA.

Intended use

This is not a general-purpose assistant. Use it only to summarize webpage text or webpage screenshots with the training-aligned prompt. See the original model card for the full prompt contract, limitations, and safety guidance.

The is the text of a webpage: <page>
... page plain text here ...
</page>
Summarise the content between the <page> tags, or if no content is found use the screenshots provided, in the Brave Summary style.

For screenshots, begin with The following is a screenshot of a webpage: and append the same fixed instruction.

Files

| Quant | Approx. size | Notes |

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

| BF16 | 7.5 GB | Maximum fidelity |

| Q8_0 | 4.0 GB | Near-BF16 quality |

| Q6_K | 3.1 GB | High quality |

| Q5_K_M / Q5_K_S | 2.7 / 2.6 GB | 5-bit variants |

| Q4_K_M / Q4_K_S | 2.3 / 2.2 GB | Recommended balance / smaller |

| Q4_1 / Q4_0 | 2.4 / 2.2 GB | Legacy compatibility |

| IQ4_NL / IQ4_XS | 2.2 / 2.1 GB | Non-linear 4-bit variants |

| Q3_K_L / Q3_K_M / Q3_K_S | 2.1 / 1.9 / 1.8 GB | 3-bit variants |

| Q2_K | 1.6 GB | Maximum compression, largest quality loss |

| mmproj-Q8_0 / mmproj-BF16 | 433 / 813 MB | Vision projectors |

Run

llama-cli \
  -m Ocelot-1-VL-Q4_K_M.gguf \
  -p 'The is the text of a webpage: <page>Page text here</page> Summarise the content between the <page> tags, or if no content is found use the screenshots provided, in the Brave Summary style.' \
  -n 512 -c 8192

For screenshots, add --mmproj mmproj-Ocelot-1-VL-Q8_0.gguf --image webpage.png and use the screenshot prompt described above.

Conversion and validation

  • Adapter revision: fbc473443eec370c3b551363556ab75d2b485db6
  • Base revision: ebb281ec70b05090aa6165b016eac8ec08e71b17
  • llama.cpp revision: 0278d8362d78c5de291bc03b76016f7f74b2ab77
  • All 15 language files passed generation smoke tests.
  • Q4_K_M with the Q8 projector passed screenshot summarization.
  • Apple M4 Pro Q4 benchmark: 313 prompt tokens/s and 26 generation tokens/s with Metal.

Quantization can reduce output quality, especially at 2 and 3 bits. Validate against your own webpage corpus before production use.

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