About
static quants of https://huggingface.co/cloudyu/Llama-3-70Bx2-MOE
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-3-70Bx2-MOE-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 |
|---|---|---|---|
| GGUF | Q2_K | 46.9 | |
| PART 1 PART 2 | IQ3_XS | 52.3 | |
| PART 1 PART 2 | IQ3_S | 55.2 | beats Q3_K* |
| PART 1 PART 2 | Q3_K_S | 55.2 | |
| PART 1 PART 2 | IQ3_M | 56.6 | |
| PART 1 PART 2 | Q3_K_M | 61.2 | lower quality |
| PART 1 PART 2 | Q3_K_L | 66.3 | |
| PART 1 PART 2 | IQ4_XS | 68.7 | |
| PART 1 PART 2 | Q4_K_S | 72.5 | fast, recommended |
| PART 1 PART 2 | Q4_K_M | 76.8 | fast, recommended |
| PART 1 PART 2 | Q5_K_S | 87.5 | |
| PART 1 PART 2 | Q5_K_M | 90.1 | |
| PART 1 PART 2 PART 3 | Q6_K | 104.2 | very good quality |
| PART 1 PART 2 PART 3 | Q8_0 | 135.0 | fast, best quality |
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/Llama-3-70Bx2-MOE-GGUF
Base model
cloudyu/Llama-3-70Bx2-MOE