base_model: cognitivecomputations/dolphin-2.9-mixtral-8x22b
datasets:
- cognitivecomputations/Dolphin-2.9
- teknium/OpenHermes-2.5
- m-a-p/CodeFeedback-Filtered-Instruction
- cognitivecomputations/dolphin-coder
- cognitivecomputations/samantha-data
- HuggingFaceH4/ultrachat_200k
- microsoft/orca-math-word-problems-200k
- abacusai/SystemChat-1.1
- Locutusque/function-calling-chatml
- internlm/Agent-FLAN
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- generated_from_trainer
- axolotl
About
weighted/imatrix quants of https://huggingface.co/cognitivecomputations/dolphin-2.9-mixtral-8x22b
static quants are available at https://huggingface.co/mradermacher/dolphin-2.9-mixtral-8x22b-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 | i1-IQ1_S | 29.7 | for the desperate |
| GGUF | i1-IQ1_M | 32.8 | mostly desperate |
| GGUF | i1-IQ2_XXS | 38.0 | |
| GGUF | i1-IQ2_XS | 42.1 | |
| GGUF | i1-IQ2_S | 42.7 | |
| GGUF | i1-IQ2_M | 46.8 | |
| PART 1 PART 2 | i1-Q2_K | 52.2 | IQ3_XXS probably better |
| PART 1 PART 2 | i1-IQ3_XXS | 55.0 | lower quality |
| PART 1 PART 2 | i1-IQ3_XS | 58.3 | |
| PART 1 PART 2 | i1-IQ3_S | 61.6 | beats Q3_K* |
| PART 1 PART 2 | i1-Q3_K_S | 61.6 | IQ3_XS probably better |
| PART 1 PART 2 | i1-IQ3_M | 64.6 | |
| PART 1 PART 2 | i1-Q3_K_M | 67.9 | IQ3_S probably better |
| PART 1 PART 2 | i1-Q3_K_L | 72.7 | IQ3_M probably better |
| PART 1 PART 2 | i1-IQ4_XS | 75.6 | |
| PART 1 PART 2 | i1-Q4_0 | 80.0 | fast, low quality |
| PART 1 PART 2 | i1-Q4_K_S | 80.6 | optimal size/speed/quality |
| PART 1 PART 2 | i1-Q4_K_M | 85.7 | fast, recommended |
| PART 1 PART 2 | i1-Q5_K_S | 97.1 | |
| PART 1 PART 2 PART 3 | i1-Q5_K_M | 100.1 | |
| PART 1 PART 2 PART 3 | i1-Q6_K | 115.6 | 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
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.