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---
base_model: cognitivecomputations/MegaDolphin-120b
datasets:
- ehartford/dolphin
- jondurbin/airoboros-2.2.1
- ehartford/samantha-data
- ehartford/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split
language:
- en
library_name: transformers
license: llama2
quantized_by: mradermacher
---
## About
weighted/imatrix quants of https://huggingface.co/cognitivecomputations/MegaDolphin-120b
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## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) 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](https://huggingface.co/mradermacher/MegaDolphin-120b-i1-GGUF/resolve/main/MegaDolphin-120b.i1-IQ1_S.gguf) | i1-IQ1_S | 25.7 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/MegaDolphin-120b-i1-GGUF/resolve/main/MegaDolphin-120b.i1-IQ1_M.gguf) | i1-IQ1_M | 27.8 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/MegaDolphin-120b-i1-GGUF/resolve/main/MegaDolphin-120b.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 32.2 | |
| [GGUF](https://huggingface.co/mradermacher/MegaDolphin-120b-i1-GGUF/resolve/main/MegaDolphin-120b.i1-IQ2_XS.gguf) | i1-IQ2_XS | 35.8 | |
| [GGUF](https://huggingface.co/mradermacher/MegaDolphin-120b-i1-GGUF/resolve/main/MegaDolphin-120b.i1-IQ2_S.gguf) | i1-IQ2_S | 37.2 | |
| [GGUF](https://huggingface.co/mradermacher/MegaDolphin-120b-i1-GGUF/resolve/main/MegaDolphin-120b.i1-IQ2_M.gguf) | i1-IQ2_M | 40.5 | |
| [GGUF](https://huggingface.co/mradermacher/MegaDolphin-120b-i1-GGUF/resolve/main/MegaDolphin-120b.i1-Q2_K.gguf) | i1-Q2_K | 44.6 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/MegaDolphin-120b-i1-GGUF/resolve/main/MegaDolphin-120b.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 47.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/MegaDolphin-120b-i1-GGUF/resolve/main/MegaDolphin-120b.i1-IQ3_XS.gguf) | i1-IQ3_XS | 49.3 | |
| [PART 1](https://huggingface.co/mradermacher/MegaDolphin-120b-i1-GGUF/resolve/main/MegaDolphin-120b.i1-Q3_K_XS.gguf.split-aa) [PART 2](https://huggingface.co/mradermacher/MegaDolphin-120b-i1-GGUF/resolve/main/MegaDolphin-120b.i1-Q3_K_XS.gguf.split-ab) | i1-Q3_K_XS | 49.3 | |
| [PART 1](https://huggingface.co/mradermacher/MegaDolphin-120b-i1-GGUF/resolve/main/MegaDolphin-120b.i1-IQ3_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/MegaDolphin-120b-i1-GGUF/resolve/main/MegaDolphin-120b.i1-IQ3_S.gguf.part2of2) | i1-IQ3_S | 52.1 | beats Q3_K* |
| [PART 1](https://huggingface.co/mradermacher/MegaDolphin-120b-i1-GGUF/resolve/main/MegaDolphin-120b.i1-Q3_K_S.gguf.split-aa) [PART 2](https://huggingface.co/mradermacher/MegaDolphin-120b-i1-GGUF/resolve/main/MegaDolphin-120b.i1-Q3_K_S.gguf.split-ab) | i1-Q3_K_S | 52.2 | IQ3_XS probably better |
| [PART 1](https://huggingface.co/mradermacher/MegaDolphin-120b-i1-GGUF/resolve/main/MegaDolphin-120b.i1-IQ3_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/MegaDolphin-120b-i1-GGUF/resolve/main/MegaDolphin-120b.i1-IQ3_M.gguf.part2of2) | i1-IQ3_M | 53.8 | |
| [PART 1](https://huggingface.co/mradermacher/MegaDolphin-120b-i1-GGUF/resolve/main/MegaDolphin-120b.i1-Q3_K_M.gguf.split-aa) [PART 2](https://huggingface.co/mradermacher/MegaDolphin-120b-i1-GGUF/resolve/main/MegaDolphin-120b.i1-Q3_K_M.gguf.split-ab) | i1-Q3_K_M | 58.2 | IQ3_S probably better |
| [PART 1](https://huggingface.co/mradermacher/MegaDolphin-120b-i1-GGUF/resolve/main/MegaDolphin-120b.i1-Q3_K_L.gguf.split-aa) [PART 2](https://huggingface.co/mradermacher/MegaDolphin-120b-i1-GGUF/resolve/main/MegaDolphin-120b.i1-Q3_K_L.gguf.split-ab) | i1-Q3_K_L | 63.4 | IQ3_M probably better |
| [PART 1](https://huggingface.co/mradermacher/MegaDolphin-120b-i1-GGUF/resolve/main/MegaDolphin-120b.i1-IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/MegaDolphin-120b-i1-GGUF/resolve/main/MegaDolphin-120b.i1-IQ4_XS.gguf.part2of2) | i1-IQ4_XS | 64.3 | |
| [PART 1](https://huggingface.co/mradermacher/MegaDolphin-120b-i1-GGUF/resolve/main/MegaDolphin-120b.i1-Q4_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/MegaDolphin-120b-i1-GGUF/resolve/main/MegaDolphin-120b.i1-Q4_0.gguf.part2of2) | i1-Q4_0 | 68.1 | fast, low quality |
| [PART 1](https://huggingface.co/mradermacher/MegaDolphin-120b-i1-GGUF/resolve/main/MegaDolphin-120b.i1-Q4_K_S.gguf.split-aa) [PART 2](https://huggingface.co/mradermacher/MegaDolphin-120b-i1-GGUF/resolve/main/MegaDolphin-120b.i1-Q4_K_S.gguf.split-ab) | i1-Q4_K_S | 68.7 | optimal size/speed/quality |
| [PART 1](https://huggingface.co/mradermacher/MegaDolphin-120b-i1-GGUF/resolve/main/MegaDolphin-120b.i1-Q4_K_M.gguf.split-aa) [PART 2](https://huggingface.co/mradermacher/MegaDolphin-120b-i1-GGUF/resolve/main/MegaDolphin-120b.i1-Q4_K_M.gguf.split-ab) | i1-Q4_K_M | 72.6 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/MegaDolphin-120b-i1-GGUF/resolve/main/MegaDolphin-120b.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/MegaDolphin-120b-i1-GGUF/resolve/main/MegaDolphin-120b.i1-Q5_K_S.gguf.part2of2) | i1-Q5_K_S | 82.9 | |
| [PART 1](https://huggingface.co/mradermacher/MegaDolphin-120b-i1-GGUF/resolve/main/MegaDolphin-120b.i1-Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/MegaDolphin-120b-i1-GGUF/resolve/main/MegaDolphin-120b.i1-Q5_K_M.gguf.part2of2) | i1-Q5_K_M | 85.1 | |
| [PART 1](https://huggingface.co/mradermacher/MegaDolphin-120b-i1-GGUF/resolve/main/MegaDolphin-120b.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/MegaDolphin-120b-i1-GGUF/resolve/main/MegaDolphin-120b.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 98.8 | 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](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
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