README
About
static quants of https://huggingface.co/internlm/CapRL-3B
For a convenient overview and download list, visit our model page for this model.
weighted/imatrix quants are available at https://huggingface.co/mradermacher/CapRL-3B-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 | mmproj-Q8_0 | 0.9 | multi-modal supplement |
| GGUF | Q2_K | 1.4 | |
| GGUF | mmproj-f16 | 1.4 | multi-modal supplement |
| GGUF | Q3_K_S | 1.6 | |
| GGUF | Q3_K_M | 1.7 | lower quality |
| GGUF | Q3_K_L | 1.8 | |
| GGUF | IQ4_XS | 1.9 | |
| GGUF | Q4_K_S | 1.9 | fast, recommended |
| GGUF | Q4_K_M | 2.0 | fast, recommended |
| GGUF | Q5_K_S | 2.3 | |
| GGUF | Q5_K_M | 2.3 | |
| GGUF | Q6_K | 2.6 | very good quality |
| GGUF | Q8_0 | 3.4 | fast, best quality |
| GGUF | f16 | 6.3 | 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
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.
Similar repos
README
About
static quants of https://huggingface.co/internlm/CapRL-3B
For a convenient overview and download list, visit our model page for this model.
weighted/imatrix quants are available at https://huggingface.co/mradermacher/CapRL-3B-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 | mmproj-Q8_0 | 0.9 | multi-modal supplement |
| GGUF | Q2_K | 1.4 | |
| GGUF | mmproj-f16 | 1.4 | multi-modal supplement |
| GGUF | Q3_K_S | 1.6 | |
| GGUF | Q3_K_M | 1.7 | lower quality |
| GGUF | Q3_K_L | 1.8 | |
| GGUF | IQ4_XS | 1.9 | |
| GGUF | Q4_K_S | 1.9 | fast, recommended |
| GGUF | Q4_K_M | 2.0 | fast, recommended |
| GGUF | Q5_K_S | 2.3 | |
| GGUF | Q5_K_M | 2.3 | |
| GGUF | Q6_K | 2.6 | very good quality |
| GGUF | Q8_0 | 3.4 | fast, best quality |
| GGUF | f16 | 6.3 | 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
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.