datasets:
- ehartford/samantha-data
language:
- en
library_name: transformers
license: llama2
quantized_by: mradermacher
About
weighted/imatrix quants of https://huggingface.co/cognitivecomputations/Samantha-1.1-70b
The weights were calculated using 164k semi-random english tokens.
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 | i1-IQ1_S | 15.0 | for the desperate |
| GGUF | i1-IQ2_XXS | 18.7 | |
| GGUF | i1-IQ2_XS | 20.8 | |
| GGUF | i1-IQ2_S | 21.8 | |
| GGUF | i1-IQ2_M | 23.7 | |
| GGUF | i1-Q2_K | 25.9 | IQ3_XXS probably better |
| GGUF | i1-IQ3_XXS | 27.4 | lower quality |
| GGUF | i1-IQ3_XS | 28.6 | |
| GGUF | i1-Q3_K_XS | 28.7 | |
| GGUF | i1-IQ3_S | 30.3 | beats Q3_K* |
| GGUF | i1-Q3_K_S | 30.3 | IQ3_XS probably better |
| GGUF | i1-IQ3_M | 31.4 | |
| GGUF | i1-Q3_K_M | 33.7 | IQ3_S probably better |
| GGUF | i1-Q3_K_L | 36.6 | IQ3_M probably better |
| GGUF | i1-Q4_K_S | 39.7 | optimal size/speed/quality |
| GGUF | i1-Q4_K_M | 41.8 | fast, recommended |
| GGUF | i1-Q5_K_S | 47.9 | |
| GGUF | i1-Q5_K_M | 49.2 | |
| PART 1 PART 2 | i1-Q6_K | 57.0 | practically like static Q6_K |
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
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.