static quants of https://huggingface.co/chuanli11/Llama-3.2-3B-Instruct-uncensored
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-3.2-3B-Instruct-uncensored-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 | Q2_K | 1.6 | |
| GGUF | IQ3_XS | 1.7 | |
| GGUF | IQ3_S | 1.8 | beats Q3_K* |
| GGUF | Q3_K_S | 1.8 | |
| GGUF | IQ3_M | 1.9 | |
| GGUF | Q3_K_M | 2.0 | lower quality |
| GGUF | Q3_K_L | 2.1 | |
| GGUF | IQ4_XS | 2.2 | |
| GGUF | Q4_K_S | 2.2 | fast, recommended |
| GGUF | Q4_K_M | 2.3 | fast, recommended |
| GGUF | Q5_K_S | 2.6 | |
| GGUF | Q5_K_M | 2.7 | |
| GGUF | Q6_K | 3.1 | very good quality |
| GGUF | Q8_0 | 3.9 | fast, best quality |
| GGUF | f16 | 7.3 | 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.
static quants of https://huggingface.co/chuanli11/Llama-3.2-3B-Instruct-uncensored
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-3.2-3B-Instruct-uncensored-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 | Q2_K | 1.6 | |
| GGUF | IQ3_XS | 1.7 | |
| GGUF | IQ3_S | 1.8 | beats Q3_K* |
| GGUF | Q3_K_S | 1.8 | |
| GGUF | IQ3_M | 1.9 | |
| GGUF | Q3_K_M | 2.0 | lower quality |
| GGUF | Q3_K_L | 2.1 | |
| GGUF | IQ4_XS | 2.2 | |
| GGUF | Q4_K_S | 2.2 | fast, recommended |
| GGUF | Q4_K_M | 2.3 | fast, recommended |
| GGUF | Q5_K_S | 2.6 | |
| GGUF | Q5_K_M | 2.7 | |
| GGUF | Q6_K | 3.1 | very good quality |
| GGUF | Q8_0 | 3.9 | fast, best quality |
| GGUF | f16 | 7.3 | 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.
