metadata
base_model: cognitivecomputations/dolphin-2.9.2-Phi-3-Medium-abliterated
datasets:
- cognitivecomputations/Dolphin-2.9.2
- teknium/OpenHermes-2.5
- m-a-p/CodeFeedback-Filtered-Instruction
- cognitivecomputations/dolphin-coder
- cognitivecomputations/samantha-data
- microsoft/orca-math-word-problems-200k
- internlm/Agent-FLAN
- cognitivecomputations/SystemChat-2.0
language:
- en
library_name: transformers
license: mit
quantized_by: mradermacher
tags:
- torrent
About
static quants of https://huggingface.co/cognitivecomputations/dolphin-2.9.2-Phi-3-Medium-abliterated
weighted/imatrix quants are available at https://huggingface.co/mradermacher/dolphin-2.9.2-Phi-3-Medium-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 |
|---|---|---|---|
| GGUF | Q2_K | 5.3 | |
| GGUF | IQ3_XS | 5.9 | |
| GGUF | Q3_K_S | 6.2 | |
| GGUF | IQ3_S | 6.2 | beats Q3_K* |
| GGUF | IQ3_M | 6.4 | |
| GGUF | Q3_K_M | 6.9 | lower quality |
| GGUF | Q3_K_L | 7.4 | |
| GGUF | IQ4_XS | 7.7 | |
| GGUF | Q4_K_S | 8.1 | fast, recommended |
| GGUF | Q4_K_M | 8.5 | fast, recommended |
| GGUF | Q5_K_S | 9.7 | |
| GGUF | Q5_K_M | 10.0 | |
| GGUF | Q6_K | 11.6 | very good quality |
| GGUF | Q8_0 | 14.9 | 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