---
base_model:
- cognitivecomputations/dolphin-2.2-70b
- cognitivecomputations/dolphin-2.2-70b
- cognitivecomputations/dolphin-2.2-70b
- cognitivecomputations/dolphin-2.2-70b
- cognitivecomputations/dolphin-2.2-70b
- cognitivecomputations/dolphin-2.2-70b
- cognitivecomputations/dolphin-2.2-70b
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-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-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 | fast, lower quality |
| [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-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-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-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, medium quality |


Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):

![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)

And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

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