Cataloged from mradermacher/Qwen3-VL-8B-Instruct-abliterated-GGUF
static quants of https://huggingface.co/prithivMLmods/Qwen3-VL-8B-Instruct-abliterated-v1
For a convenient overview and download list, visit our model page for this model.
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen3-VL-8B-Instruct-abliterated-i1-GGUF
If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|---|---|---|---|
| GGUF | mmproj-Q8_0 | 0.9 | multi-modal supplement |
| GGUF | mmproj-f16 | 1.3 | multi-modal supplement |
| GGUF | Q2_K | 3.4 | |
| GGUF | Q3_K_S | 3.9 | |
| GGUF | Q3_K_M |
| 4.2 |
| lower quality |
| GGUF | Q3_K_L | 4.5 |
| GGUF | IQ4_XS | 4.7 |
| GGUF | Q4_K_S | 4.9 | fast, recommended |
| GGUF | Q4_K_M | 5.1 | fast, recommended |
| GGUF | Q5_K_S | 5.8 |
| GGUF | Q5_K_M | 6.0 |
| GGUF | Q6_K | 6.8 | very good quality |
| GGUF | Q8_0 | 8.8 | fast, best quality |
| GGUF | f16 | 16.5 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):
And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.
I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.