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…
Runs locally from ~432.9 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).
Repository Files & Downloads
| File | Type | Quantization | Size | Link |
|---|---|---|---|---|
| Ocelot-1-VL-BF16.gguf | GGUF | BF16 | 7.50 GB | Download |
| Ocelot-1-VL-IQ4_NL.gguf | GGUF | IQ4_NL | 2.23 GB | Download |
| Ocelot-1-VL-IQ4_XS.gguf | GGUF | IQ4_XS | 2.13 GB | Download |
| Ocelot-1-VL-Q2_K.gguf | GGUF | Q2_K | 1.55 GB | Download |
| Ocelot-1-VL-Q3_K_L.gguf | GGUF | Q3_K_L | 2.09 GB | Download |
| Ocelot-1-VL-Q3_K_M.gguf | GGUF | Q3_K_M | 1.93 GB | Download |
| Ocelot-1-VL-Q3_K_S.gguf | GGUF | Q3_K_S | 1.76 GB | Download |
| Ocelot-1-VL-Q4_0.gguf | GGUF | Q4_0 | 2.21 GB | Download |
| Ocelot-1-VL-Q4_1.gguf | GGUF | Q4_1 | 2.42 GB | Download |
| Ocelot-1-VL-Q4_K_M.gguf | GGUF | Q4_K_M | 2.33 GB | Download |
| Ocelot-1-VL-Q4_K_S.gguf | GGUF | Q4_K_S | 2.22 GB | Download |
| Ocelot-1-VL-Q5_K_M.gguf | GGUF | Q5_K_M | 2.69 GB | Download |
| Ocelot-1-VL-Q5_K_S.gguf | GGUF | Q5_K_S | 2.63 GB | Download |
| Ocelot-1-VL-Q6_K.gguf | GGUF | Q6_K | 3.08 GB | Download |
| Ocelot-1-VL-Q8_0.gguf | GGUF | Q8_0 | 3.99 GB | Download |
| mmproj-Ocelot-1-VL-BF16.gguf | GGUF | BF16 | 800.4 MB | Download |
| mmproj-Ocelot-1-VL-Q8_0.gguf | GGUF | Q8_0 | 432.9 MB | Download |
Model Details
| Model ID | gnukeith/Ocelot-GGUF |
|---|---|
| Author | gnukeith |
| Pipeline | image-text-to-text |
| License | apache-2.0 |
| Base model | bravesoftware/Ocelot-1-VL,Qwen/Qwen3-VL-4B-Instruct |
| Last modified | 2026-07-22T18:04:33.000Z |
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_Mwith 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.
Run gnukeith/Ocelot-GGUF with guIDE
Download guIDE — the AI-native code editor with local LLM inference and 69 built-in tools.
Source: Hugging Face · Compare models