About
static quants of https://huggingface.co/stepenZEN/DeepSeek-R1-Distill-Llama-8B-Abliterated
weighted/imatrix quants are available at https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Llama-8B-Abliterated-i1-GGUF
Usage
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.
Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|---|---|---|---|
| PART 1 PART 2 | Q2_K | 6.5 | |
| PART 1 PART 2 | Q3_K_S | 7.4 | |
| PART 1 PART 2 | Q3_K_M | 8.1 | lower quality |
| PART 1 PART 2 | Q3_K_L | 8.7 | |
| PART 1 PART 2 | IQ4_XS | 9.1 | |
| PART 1 PART 2 | Q4_K_S | 9.5 | fast, recommended |
| PART 1 PART 2 | Q4_K_M | 9.9 | fast, recommended |
| PART 1 PART 2 | Q5_K_S | 11.3 | |
| PART 1 PART 2 | Q5_K_M | 11.6 | |
| PART 1 PART 2 | Q6_K | 13.3 | very good quality |
| PART 1 PART 2 | Q8_0 | 17.2 | fast, best quality |
| PART 1 PART 2 | f16 | 32.2 | 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
FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.
Thanks
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.
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Model tree for mradermacher/DeepSeek-R1-Distill-Llama-8B-Abliterated-GGUF
Base model
deepseek-ai/DeepSeek-R1-Distill-Llama-8B