modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-03 00:36:49
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 535
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-03 00:36:49
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
mradermacher/ANIMA-biodesign-7B-slerp-GGUF
|
mradermacher
| 2024-05-06T05:25:07Z | 7 | 0 |
transformers
|
[
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"MaziyarPanahi/ANIMA-Phi-Neptune-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp",
"Severian/ANIMA-Neural-Hermes",
"en",
"base_model:allknowingroger/ANIMA-biodesign-7B-slerp",
"base_model:quantized:allknowingroger/ANIMA-biodesign-7B-slerp",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-01T12:00:25Z |
---
base_model: allknowingroger/ANIMA-biodesign-7B-slerp
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- MaziyarPanahi/ANIMA-Phi-Neptune-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp
- Severian/ANIMA-Neural-Hermes
---
## About
static quants of https://huggingface.co/allknowingroger/ANIMA-biodesign-7B-slerp
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/ANIMA-biodesign-7B-slerp-GGUF/resolve/main/ANIMA-biodesign-7B-slerp.Q2_K.gguf) | Q2_K | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/ANIMA-biodesign-7B-slerp-GGUF/resolve/main/ANIMA-biodesign-7B-slerp.IQ3_XS.gguf) | IQ3_XS | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/ANIMA-biodesign-7B-slerp-GGUF/resolve/main/ANIMA-biodesign-7B-slerp.Q3_K_S.gguf) | Q3_K_S | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/ANIMA-biodesign-7B-slerp-GGUF/resolve/main/ANIMA-biodesign-7B-slerp.IQ3_S.gguf) | IQ3_S | 3.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/ANIMA-biodesign-7B-slerp-GGUF/resolve/main/ANIMA-biodesign-7B-slerp.IQ3_M.gguf) | IQ3_M | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/ANIMA-biodesign-7B-slerp-GGUF/resolve/main/ANIMA-biodesign-7B-slerp.Q3_K_M.gguf) | Q3_K_M | 3.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/ANIMA-biodesign-7B-slerp-GGUF/resolve/main/ANIMA-biodesign-7B-slerp.Q3_K_L.gguf) | Q3_K_L | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/ANIMA-biodesign-7B-slerp-GGUF/resolve/main/ANIMA-biodesign-7B-slerp.IQ4_XS.gguf) | IQ4_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/ANIMA-biodesign-7B-slerp-GGUF/resolve/main/ANIMA-biodesign-7B-slerp.Q4_K_S.gguf) | Q4_K_S | 4.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ANIMA-biodesign-7B-slerp-GGUF/resolve/main/ANIMA-biodesign-7B-slerp.Q4_K_M.gguf) | Q4_K_M | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ANIMA-biodesign-7B-slerp-GGUF/resolve/main/ANIMA-biodesign-7B-slerp.Q5_K_S.gguf) | Q5_K_S | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/ANIMA-biodesign-7B-slerp-GGUF/resolve/main/ANIMA-biodesign-7B-slerp.Q5_K_M.gguf) | Q5_K_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/ANIMA-biodesign-7B-slerp-GGUF/resolve/main/ANIMA-biodesign-7B-slerp.Q6_K.gguf) | Q6_K | 6.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/ANIMA-biodesign-7B-slerp-GGUF/resolve/main/ANIMA-biodesign-7B-slerp.Q8_0.gguf) | Q8_0 | 7.9 | 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
## 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.
<!-- end -->
|
mradermacher/Yeet_51b_200k-GGUF
|
mradermacher
| 2024-05-06T05:24:43Z | 106 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:MarsupialAI/Yeet_51b_200k",
"base_model:quantized:MarsupialAI/Yeet_51b_200k",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-04-01T16:03:18Z |
---
base_model: MarsupialAI/Yeet_51b_200k
language:
- en
library_name: transformers
license: other
license_name: yi-other
quantized_by: mradermacher
---
## About
static quants of https://huggingface.co/MarsupialAI/Yeet_51b_200k
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Yeet_51b_200k-i1-GGUF
## 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/Yeet_51b_200k-GGUF/resolve/main/Yeet_51b_200k.Q2_K.gguf) | Q2_K | 19.6 | |
| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-GGUF/resolve/main/Yeet_51b_200k.IQ3_XS.gguf) | IQ3_XS | 21.7 | |
| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-GGUF/resolve/main/Yeet_51b_200k.Q3_K_S.gguf) | Q3_K_S | 22.8 | |
| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-GGUF/resolve/main/Yeet_51b_200k.IQ3_S.gguf) | IQ3_S | 22.9 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-GGUF/resolve/main/Yeet_51b_200k.IQ3_M.gguf) | IQ3_M | 23.7 | |
| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-GGUF/resolve/main/Yeet_51b_200k.Q3_K_M.gguf) | Q3_K_M | 25.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-GGUF/resolve/main/Yeet_51b_200k.Q3_K_L.gguf) | Q3_K_L | 27.6 | |
| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-GGUF/resolve/main/Yeet_51b_200k.IQ4_XS.gguf) | IQ4_XS | 28.3 | |
| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-GGUF/resolve/main/Yeet_51b_200k.Q4_K_S.gguf) | Q4_K_S | 29.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-GGUF/resolve/main/Yeet_51b_200k.Q4_K_M.gguf) | Q4_K_M | 31.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-GGUF/resolve/main/Yeet_51b_200k.Q5_K_S.gguf) | Q5_K_S | 35.9 | |
| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-GGUF/resolve/main/Yeet_51b_200k.Q5_K_M.gguf) | Q5_K_M | 36.8 | |
| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-GGUF/resolve/main/Yeet_51b_200k.Q6_K.gguf) | Q6_K | 42.6 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Yeet_51b_200k-GGUF/resolve/main/Yeet_51b_200k.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Yeet_51b_200k-GGUF/resolve/main/Yeet_51b_200k.Q8_0.gguf.part2of2) | Q8_0 | 54.9 | 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
## 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.
<!-- end -->
|
mradermacher/TimeMax-20B-GGUF
|
mradermacher
| 2024-05-06T05:24:37Z | 2 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:R136a1/TimeMax-20B",
"base_model:quantized:R136a1/TimeMax-20B",
"endpoints_compatible",
"region:us"
] | null | 2024-04-01T16:14:35Z |
---
base_model: R136a1/TimeMax-20B
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
static quants of https://huggingface.co/R136a1/TimeMax-20B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF
## 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/TimeMax-20B-GGUF/resolve/main/TimeMax-20B.Q2_K.gguf) | Q2_K | 7.7 | |
| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-GGUF/resolve/main/TimeMax-20B.IQ3_XS.gguf) | IQ3_XS | 8.5 | |
| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-GGUF/resolve/main/TimeMax-20B.IQ3_S.gguf) | IQ3_S | 9.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-GGUF/resolve/main/TimeMax-20B.Q3_K_S.gguf) | Q3_K_S | 9.0 | |
| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-GGUF/resolve/main/TimeMax-20B.IQ3_M.gguf) | IQ3_M | 9.4 | |
| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-GGUF/resolve/main/TimeMax-20B.Q3_K_M.gguf) | Q3_K_M | 10.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-GGUF/resolve/main/TimeMax-20B.Q3_K_L.gguf) | Q3_K_L | 10.9 | |
| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-GGUF/resolve/main/TimeMax-20B.IQ4_XS.gguf) | IQ4_XS | 11.0 | |
| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-GGUF/resolve/main/TimeMax-20B.Q4_K_S.gguf) | Q4_K_S | 11.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-GGUF/resolve/main/TimeMax-20B.Q4_K_M.gguf) | Q4_K_M | 12.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-GGUF/resolve/main/TimeMax-20B.Q5_K_S.gguf) | Q5_K_S | 14.1 | |
| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-GGUF/resolve/main/TimeMax-20B.Q5_K_M.gguf) | Q5_K_M | 14.5 | |
| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-GGUF/resolve/main/TimeMax-20B.Q6_K.gguf) | Q6_K | 16.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-GGUF/resolve/main/TimeMax-20B.Q8_0.gguf) | Q8_0 | 21.5 | 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
## 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.
<!-- end -->
|
mradermacher/TimeMax-20B-i1-GGUF
|
mradermacher
| 2024-05-06T05:24:20Z | 6 | 1 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"text-generation",
"en",
"base_model:R136a1/TimeMax-20B",
"base_model:quantized:R136a1/TimeMax-20B",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-04-01T20:10:41Z |
---
base_model: R136a1/TimeMax-20B
language:
- en
library_name: transformers
pipeline_tag: text-generation
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
weighted/imatrix quants of https://huggingface.co/R136a1/TimeMax-20B
**Only 50k tokens from my standard set have been used, as more caused an overflow. This is likely a problem with the model itself.**
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/TimeMax-20B-GGUF
## 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/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-IQ1_S.gguf) | i1-IQ1_S | 4.7 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-IQ1_M.gguf) | i1-IQ1_M | 5.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 6.3 | |
| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-IQ2_S.gguf) | i1-IQ2_S | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-IQ2_M.gguf) | i1-IQ2_M | 7.2 | |
| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-Q2_K.gguf) | i1-Q2_K | 7.7 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 7.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 8.5 | |
| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-IQ3_S.gguf) | i1-IQ3_S | 9.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 9.0 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-IQ3_M.gguf) | i1-IQ3_M | 9.4 | |
| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 10.0 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 10.9 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 11.0 | |
| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-Q4_0.gguf) | i1-Q4_0 | 11.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 11.7 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 12.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 14.1 | |
| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 14.5 | |
| [GGUF](https://huggingface.co/mradermacher/TimeMax-20B-i1-GGUF/resolve/main/TimeMax-20B.i1-Q6_K.gguf) | i1-Q6_K | 16.7 | 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.
<!-- end -->
|
mradermacher/NeuralStock-7B-GGUF
|
mradermacher
| 2024-05-06T05:24:12Z | 73 | 0 |
transformers
|
[
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"liminerity/M7-7b",
"Gille/StrangeMerges_32-7B-slerp",
"automerger/YamShadow-7B",
"en",
"base_model:Kukedlc/NeuralStock-7B",
"base_model:quantized:Kukedlc/NeuralStock-7B",
"endpoints_compatible",
"region:us"
] | null | 2024-04-01T21:52:33Z |
---
base_model: Kukedlc/NeuralStock-7B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- liminerity/M7-7b
- Gille/StrangeMerges_32-7B-slerp
- automerger/YamShadow-7B
---
## About
static quants of https://huggingface.co/Kukedlc/NeuralStock-7B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/NeuralStock-7B-GGUF/resolve/main/NeuralStock-7B.Q2_K.gguf) | Q2_K | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralStock-7B-GGUF/resolve/main/NeuralStock-7B.IQ3_XS.gguf) | IQ3_XS | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralStock-7B-GGUF/resolve/main/NeuralStock-7B.Q3_K_S.gguf) | Q3_K_S | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralStock-7B-GGUF/resolve/main/NeuralStock-7B.IQ3_S.gguf) | IQ3_S | 3.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/NeuralStock-7B-GGUF/resolve/main/NeuralStock-7B.IQ3_M.gguf) | IQ3_M | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralStock-7B-GGUF/resolve/main/NeuralStock-7B.Q3_K_M.gguf) | Q3_K_M | 3.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/NeuralStock-7B-GGUF/resolve/main/NeuralStock-7B.Q3_K_L.gguf) | Q3_K_L | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralStock-7B-GGUF/resolve/main/NeuralStock-7B.IQ4_XS.gguf) | IQ4_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralStock-7B-GGUF/resolve/main/NeuralStock-7B.Q4_K_S.gguf) | Q4_K_S | 4.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/NeuralStock-7B-GGUF/resolve/main/NeuralStock-7B.Q4_K_M.gguf) | Q4_K_M | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/NeuralStock-7B-GGUF/resolve/main/NeuralStock-7B.Q5_K_S.gguf) | Q5_K_S | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralStock-7B-GGUF/resolve/main/NeuralStock-7B.Q5_K_M.gguf) | Q5_K_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralStock-7B-GGUF/resolve/main/NeuralStock-7B.Q6_K.gguf) | Q6_K | 6.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/NeuralStock-7B-GGUF/resolve/main/NeuralStock-7B.Q8_0.gguf) | Q8_0 | 7.9 | 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
## 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.
<!-- end -->
|
mradermacher/WinterGoliath-123b-32k-i1-GGUF
|
mradermacher
| 2024-05-06T05:24:09Z | 8 | 0 |
transformers
|
[
"transformers",
"gguf",
"merge",
"mergekit",
"en",
"base_model:ChuckMcSneed/WinterGoliath-123b-32k",
"base_model:quantized:ChuckMcSneed/WinterGoliath-123b-32k",
"license:llama2",
"endpoints_compatible",
"region:us"
] | null | 2024-04-01T22:22:44Z |
---
base_model: ChuckMcSneed/WinterGoliath-123b-32k
language:
- en
library_name: transformers
license: llama2
quantized_by: mradermacher
tags:
- merge
- mergekit
---
## About
weighted/imatrix quants of https://huggingface.co/ChuckMcSneed/WinterGoliath-123b-32k
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/WinterGoliath-123b-32k-GGUF
## 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/WinterGoliath-123b-32k-i1-GGUF/resolve/main/WinterGoliath-123b-32k.i1-IQ1_S.gguf) | i1-IQ1_S | 26.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/WinterGoliath-123b-32k-i1-GGUF/resolve/main/WinterGoliath-123b-32k.i1-IQ1_M.gguf) | i1-IQ1_M | 28.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/WinterGoliath-123b-32k-i1-GGUF/resolve/main/WinterGoliath-123b-32k.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 33.2 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoliath-123b-32k-i1-GGUF/resolve/main/WinterGoliath-123b-32k.i1-IQ2_XS.gguf) | i1-IQ2_XS | 36.8 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoliath-123b-32k-i1-GGUF/resolve/main/WinterGoliath-123b-32k.i1-IQ2_S.gguf) | i1-IQ2_S | 38.6 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoliath-123b-32k-i1-GGUF/resolve/main/WinterGoliath-123b-32k.i1-IQ2_M.gguf) | i1-IQ2_M | 42.0 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoliath-123b-32k-i1-GGUF/resolve/main/WinterGoliath-123b-32k.i1-Q2_K.gguf) | i1-Q2_K | 45.8 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/WinterGoliath-123b-32k-i1-GGUF/resolve/main/WinterGoliath-123b-32k.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 47.9 | lower quality |
| [PART 1](https://huggingface.co/mradermacher/WinterGoliath-123b-32k-i1-GGUF/resolve/main/WinterGoliath-123b-32k.i1-IQ3_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/WinterGoliath-123b-32k-i1-GGUF/resolve/main/WinterGoliath-123b-32k.i1-IQ3_XS.gguf.part2of2) | i1-IQ3_XS | 51.0 | |
| [PART 1](https://huggingface.co/mradermacher/WinterGoliath-123b-32k-i1-GGUF/resolve/main/WinterGoliath-123b-32k.i1-Q3_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/WinterGoliath-123b-32k-i1-GGUF/resolve/main/WinterGoliath-123b-32k.i1-Q3_K_S.gguf.part2of2) | i1-Q3_K_S | 53.7 | IQ3_XS probably better |
| [PART 1](https://huggingface.co/mradermacher/WinterGoliath-123b-32k-i1-GGUF/resolve/main/WinterGoliath-123b-32k.i1-IQ3_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/WinterGoliath-123b-32k-i1-GGUF/resolve/main/WinterGoliath-123b-32k.i1-IQ3_S.gguf.part2of2) | i1-IQ3_S | 53.9 | beats Q3_K* |
| [PART 1](https://huggingface.co/mradermacher/WinterGoliath-123b-32k-i1-GGUF/resolve/main/WinterGoliath-123b-32k.i1-IQ3_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/WinterGoliath-123b-32k-i1-GGUF/resolve/main/WinterGoliath-123b-32k.i1-IQ3_M.gguf.part2of2) | i1-IQ3_M | 55.7 | |
| [PART 1](https://huggingface.co/mradermacher/WinterGoliath-123b-32k-i1-GGUF/resolve/main/WinterGoliath-123b-32k.i1-Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/WinterGoliath-123b-32k-i1-GGUF/resolve/main/WinterGoliath-123b-32k.i1-Q3_K_M.gguf.part2of2) | i1-Q3_K_M | 59.9 | IQ3_S probably better |
| [PART 1](https://huggingface.co/mradermacher/WinterGoliath-123b-32k-i1-GGUF/resolve/main/WinterGoliath-123b-32k.i1-Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/WinterGoliath-123b-32k-i1-GGUF/resolve/main/WinterGoliath-123b-32k.i1-Q3_K_L.gguf.part2of2) | i1-Q3_K_L | 65.2 | IQ3_M probably better |
| [PART 1](https://huggingface.co/mradermacher/WinterGoliath-123b-32k-i1-GGUF/resolve/main/WinterGoliath-123b-32k.i1-IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/WinterGoliath-123b-32k-i1-GGUF/resolve/main/WinterGoliath-123b-32k.i1-IQ4_XS.gguf.part2of2) | i1-IQ4_XS | 66.4 | |
| [PART 1](https://huggingface.co/mradermacher/WinterGoliath-123b-32k-i1-GGUF/resolve/main/WinterGoliath-123b-32k.i1-Q4_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/WinterGoliath-123b-32k-i1-GGUF/resolve/main/WinterGoliath-123b-32k.i1-Q4_0.gguf.part2of2) | i1-Q4_0 | 70.4 | fast, low quality |
| [PART 1](https://huggingface.co/mradermacher/WinterGoliath-123b-32k-i1-GGUF/resolve/main/WinterGoliath-123b-32k.i1-Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/WinterGoliath-123b-32k-i1-GGUF/resolve/main/WinterGoliath-123b-32k.i1-Q4_K_S.gguf.part2of2) | i1-Q4_K_S | 70.6 | optimal size/speed/quality |
| [PART 1](https://huggingface.co/mradermacher/WinterGoliath-123b-32k-i1-GGUF/resolve/main/WinterGoliath-123b-32k.i1-Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/WinterGoliath-123b-32k-i1-GGUF/resolve/main/WinterGoliath-123b-32k.i1-Q4_K_M.gguf.part2of2) | i1-Q4_K_M | 74.6 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/WinterGoliath-123b-32k-i1-GGUF/resolve/main/WinterGoliath-123b-32k.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/WinterGoliath-123b-32k-i1-GGUF/resolve/main/WinterGoliath-123b-32k.i1-Q5_K_S.gguf.part2of2) | i1-Q5_K_S | 85.5 | |
| [PART 1](https://huggingface.co/mradermacher/WinterGoliath-123b-32k-i1-GGUF/resolve/main/WinterGoliath-123b-32k.i1-Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/WinterGoliath-123b-32k-i1-GGUF/resolve/main/WinterGoliath-123b-32k.i1-Q5_K_M.gguf.part2of2) | i1-Q5_K_M | 87.8 | |
| [PART 1](https://huggingface.co/mradermacher/WinterGoliath-123b-32k-i1-GGUF/resolve/main/WinterGoliath-123b-32k.i1-Q6_K.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/WinterGoliath-123b-32k-i1-GGUF/resolve/main/WinterGoliath-123b-32k.i1-Q6_K.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/WinterGoliath-123b-32k-i1-GGUF/resolve/main/WinterGoliath-123b-32k.i1-Q6_K.gguf.part3of3) | i1-Q6_K | 101.9 | 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.
<!-- end -->
|
mradermacher/Zebrafish-linear-7B-GGUF
|
mradermacher
| 2024-05-06T05:24:05Z | 13 | 0 |
transformers
|
[
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"en",
"base_model:mlabonne/Zebrafish-linear-7B",
"base_model:quantized:mlabonne/Zebrafish-linear-7B",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-02T00:44:08Z |
---
base_model: mlabonne/Zebrafish-linear-7B
language:
- en
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
---
## About
static quants of https://huggingface.co/mlabonne/Zebrafish-linear-7B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/Zebrafish-linear-7B-GGUF/resolve/main/Zebrafish-linear-7B.Q2_K.gguf) | Q2_K | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Zebrafish-linear-7B-GGUF/resolve/main/Zebrafish-linear-7B.IQ3_XS.gguf) | IQ3_XS | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Zebrafish-linear-7B-GGUF/resolve/main/Zebrafish-linear-7B.Q3_K_S.gguf) | Q3_K_S | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Zebrafish-linear-7B-GGUF/resolve/main/Zebrafish-linear-7B.IQ3_S.gguf) | IQ3_S | 3.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Zebrafish-linear-7B-GGUF/resolve/main/Zebrafish-linear-7B.IQ3_M.gguf) | IQ3_M | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/Zebrafish-linear-7B-GGUF/resolve/main/Zebrafish-linear-7B.Q3_K_M.gguf) | Q3_K_M | 3.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Zebrafish-linear-7B-GGUF/resolve/main/Zebrafish-linear-7B.Q3_K_L.gguf) | Q3_K_L | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/Zebrafish-linear-7B-GGUF/resolve/main/Zebrafish-linear-7B.IQ4_XS.gguf) | IQ4_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Zebrafish-linear-7B-GGUF/resolve/main/Zebrafish-linear-7B.Q4_K_S.gguf) | Q4_K_S | 4.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Zebrafish-linear-7B-GGUF/resolve/main/Zebrafish-linear-7B.Q4_K_M.gguf) | Q4_K_M | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Zebrafish-linear-7B-GGUF/resolve/main/Zebrafish-linear-7B.Q5_K_S.gguf) | Q5_K_S | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/Zebrafish-linear-7B-GGUF/resolve/main/Zebrafish-linear-7B.Q5_K_M.gguf) | Q5_K_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Zebrafish-linear-7B-GGUF/resolve/main/Zebrafish-linear-7B.Q6_K.gguf) | Q6_K | 6.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Zebrafish-linear-7B-GGUF/resolve/main/Zebrafish-linear-7B.Q8_0.gguf) | Q8_0 | 7.9 | 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
## 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.
<!-- end -->
|
mradermacher/MonarchPipe-7B-slerp-GGUF
|
mradermacher
| 2024-05-06T05:23:52Z | 25 | 0 |
transformers
|
[
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"OpenPipe/mistral-ft-optimized-1227",
"mlabonne/AlphaMonarch-7B",
"en",
"base_model:ichigoberry/MonarchPipe-7B-slerp",
"base_model:quantized:ichigoberry/MonarchPipe-7B-slerp",
"license:cc-by-nc-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-02T02:11:17Z |
---
base_model: ichigoberry/MonarchPipe-7B-slerp
language:
- en
library_name: transformers
license: cc-by-nc-2.0
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- OpenPipe/mistral-ft-optimized-1227
- mlabonne/AlphaMonarch-7B
---
## About
static quants of https://huggingface.co/ichigoberry/MonarchPipe-7B-slerp
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/MonarchPipe-7B-slerp-GGUF/resolve/main/MonarchPipe-7B-slerp.Q2_K.gguf) | Q2_K | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/MonarchPipe-7B-slerp-GGUF/resolve/main/MonarchPipe-7B-slerp.IQ3_XS.gguf) | IQ3_XS | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/MonarchPipe-7B-slerp-GGUF/resolve/main/MonarchPipe-7B-slerp.Q3_K_S.gguf) | Q3_K_S | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/MonarchPipe-7B-slerp-GGUF/resolve/main/MonarchPipe-7B-slerp.IQ3_S.gguf) | IQ3_S | 3.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/MonarchPipe-7B-slerp-GGUF/resolve/main/MonarchPipe-7B-slerp.IQ3_M.gguf) | IQ3_M | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/MonarchPipe-7B-slerp-GGUF/resolve/main/MonarchPipe-7B-slerp.Q3_K_M.gguf) | Q3_K_M | 3.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/MonarchPipe-7B-slerp-GGUF/resolve/main/MonarchPipe-7B-slerp.Q3_K_L.gguf) | Q3_K_L | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/MonarchPipe-7B-slerp-GGUF/resolve/main/MonarchPipe-7B-slerp.IQ4_XS.gguf) | IQ4_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/MonarchPipe-7B-slerp-GGUF/resolve/main/MonarchPipe-7B-slerp.Q4_K_S.gguf) | Q4_K_S | 4.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MonarchPipe-7B-slerp-GGUF/resolve/main/MonarchPipe-7B-slerp.Q4_K_M.gguf) | Q4_K_M | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MonarchPipe-7B-slerp-GGUF/resolve/main/MonarchPipe-7B-slerp.Q5_K_S.gguf) | Q5_K_S | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/MonarchPipe-7B-slerp-GGUF/resolve/main/MonarchPipe-7B-slerp.Q5_K_M.gguf) | Q5_K_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/MonarchPipe-7B-slerp-GGUF/resolve/main/MonarchPipe-7B-slerp.Q6_K.gguf) | Q6_K | 6.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/MonarchPipe-7B-slerp-GGUF/resolve/main/MonarchPipe-7B-slerp.Q8_0.gguf) | Q8_0 | 7.9 | 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
## 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.
<!-- end -->
|
mradermacher/UNAversal-8x7B-v1beta-GGUF
|
mradermacher
| 2024-05-06T05:23:33Z | 69 | 0 |
transformers
|
[
"transformers",
"gguf",
"UNA",
"juanako",
"mixtral",
"MoE",
"en",
"base_model:fblgit/UNAversal-8x7B-v1beta",
"base_model:quantized:fblgit/UNAversal-8x7B-v1beta",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-02T05:11:23Z |
---
base_model: fblgit/UNAversal-8x7B-v1beta
language:
- en
library_name: transformers
license: cc-by-nc-sa-4.0
quantized_by: mradermacher
tags:
- UNA
- juanako
- mixtral
- MoE
---
## About
static quants of https://huggingface.co/fblgit/UNAversal-8x7B-v1beta
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF
## 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/UNAversal-8x7B-v1beta-GGUF/resolve/main/UNAversal-8x7B-v1beta.Q2_K.gguf) | Q2_K | 17.6 | |
| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-GGUF/resolve/main/UNAversal-8x7B-v1beta.IQ3_XS.gguf) | IQ3_XS | 19.5 | |
| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-GGUF/resolve/main/UNAversal-8x7B-v1beta.IQ3_S.gguf) | IQ3_S | 20.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-GGUF/resolve/main/UNAversal-8x7B-v1beta.Q3_K_S.gguf) | Q3_K_S | 20.7 | |
| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-GGUF/resolve/main/UNAversal-8x7B-v1beta.IQ3_M.gguf) | IQ3_M | 21.7 | |
| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-GGUF/resolve/main/UNAversal-8x7B-v1beta.Q3_K_M.gguf) | Q3_K_M | 22.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-GGUF/resolve/main/UNAversal-8x7B-v1beta.Q3_K_L.gguf) | Q3_K_L | 24.4 | |
| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-GGUF/resolve/main/UNAversal-8x7B-v1beta.IQ4_XS.gguf) | IQ4_XS | 25.6 | |
| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-GGUF/resolve/main/UNAversal-8x7B-v1beta.Q4_K_S.gguf) | Q4_K_S | 27.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-GGUF/resolve/main/UNAversal-8x7B-v1beta.Q4_K_M.gguf) | Q4_K_M | 28.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-GGUF/resolve/main/UNAversal-8x7B-v1beta.Q5_K_S.gguf) | Q5_K_S | 32.5 | |
| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-GGUF/resolve/main/UNAversal-8x7B-v1beta.Q5_K_M.gguf) | Q5_K_M | 33.5 | |
| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-GGUF/resolve/main/UNAversal-8x7B-v1beta.Q6_K.gguf) | Q6_K | 38.6 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-GGUF/resolve/main/UNAversal-8x7B-v1beta.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-GGUF/resolve/main/UNAversal-8x7B-v1beta.Q8_0.gguf.part2of2) | Q8_0 | 49.8 | 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
## 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.
<!-- end -->
|
mradermacher/bagel-dpo-20b-v04-GGUF
|
mradermacher
| 2024-05-06T05:23:25Z | 206 | 2 |
transformers
|
[
"transformers",
"gguf",
"en",
"dataset:ai2_arc",
"dataset:allenai/ultrafeedback_binarized_cleaned",
"dataset:argilla/distilabel-intel-orca-dpo-pairs",
"dataset:jondurbin/airoboros-3.2",
"dataset:codeparrot/apps",
"dataset:facebook/belebele",
"dataset:bluemoon-fandom-1-1-rp-cleaned",
"dataset:boolq",
"dataset:camel-ai/biology",
"dataset:camel-ai/chemistry",
"dataset:camel-ai/math",
"dataset:camel-ai/physics",
"dataset:jondurbin/contextual-dpo-v0.1",
"dataset:jondurbin/gutenberg-dpo-v0.1",
"dataset:jondurbin/py-dpo-v0.1",
"dataset:jondurbin/truthy-dpo-v0.1",
"dataset:LDJnr/Capybara",
"dataset:jondurbin/cinematika-v0.1",
"dataset:WizardLM/WizardLM_evol_instruct_70k",
"dataset:glaiveai/glaive-function-calling-v2",
"dataset:grimulkan/LimaRP-augmented",
"dataset:lmsys/lmsys-chat-1m",
"dataset:ParisNeo/lollms_aware_dataset",
"dataset:TIGER-Lab/MathInstruct",
"dataset:Muennighoff/natural-instructions",
"dataset:openbookqa",
"dataset:kingbri/PIPPA-shareGPT",
"dataset:piqa",
"dataset:Vezora/Tested-22k-Python-Alpaca",
"dataset:ropes",
"dataset:cakiki/rosetta-code",
"dataset:Open-Orca/SlimOrca",
"dataset:b-mc2/sql-create-context",
"dataset:squad_v2",
"dataset:mattpscott/airoboros-summarization",
"dataset:migtissera/Synthia-v1.3",
"dataset:unalignment/toxic-dpo-v0.2",
"dataset:WhiteRabbitNeo/WRN-Chapter-1",
"dataset:WhiteRabbitNeo/WRN-Chapter-2",
"dataset:winogrande",
"base_model:jondurbin/bagel-dpo-20b-v04",
"base_model:quantized:jondurbin/bagel-dpo-20b-v04",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-02T06:31:44Z |
---
base_model: jondurbin/bagel-dpo-20b-v04
datasets:
- ai2_arc
- allenai/ultrafeedback_binarized_cleaned
- argilla/distilabel-intel-orca-dpo-pairs
- jondurbin/airoboros-3.2
- codeparrot/apps
- facebook/belebele
- bluemoon-fandom-1-1-rp-cleaned
- boolq
- camel-ai/biology
- camel-ai/chemistry
- camel-ai/math
- camel-ai/physics
- jondurbin/contextual-dpo-v0.1
- jondurbin/gutenberg-dpo-v0.1
- jondurbin/py-dpo-v0.1
- jondurbin/truthy-dpo-v0.1
- LDJnr/Capybara
- jondurbin/cinematika-v0.1
- WizardLM/WizardLM_evol_instruct_70k
- glaiveai/glaive-function-calling-v2
- jondurbin/gutenberg-dpo-v0.1
- grimulkan/LimaRP-augmented
- lmsys/lmsys-chat-1m
- ParisNeo/lollms_aware_dataset
- TIGER-Lab/MathInstruct
- Muennighoff/natural-instructions
- openbookqa
- kingbri/PIPPA-shareGPT
- piqa
- Vezora/Tested-22k-Python-Alpaca
- ropes
- cakiki/rosetta-code
- Open-Orca/SlimOrca
- b-mc2/sql-create-context
- squad_v2
- mattpscott/airoboros-summarization
- migtissera/Synthia-v1.3
- unalignment/toxic-dpo-v0.2
- WhiteRabbitNeo/WRN-Chapter-1
- WhiteRabbitNeo/WRN-Chapter-2
- winogrande
language:
- en
library_name: transformers
license: other
license_link: https://huggingface.co/internlm/internlm2-20b#open-source-license
license_name: internlm2-20b
quantized_by: mradermacher
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/jondurbin/bagel-dpo-20b-v04
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/bagel-dpo-20b-v04-i1-GGUF
## 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/bagel-dpo-20b-v04-GGUF/resolve/main/bagel-dpo-20b-v04.Q2_K.gguf) | Q2_K | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-GGUF/resolve/main/bagel-dpo-20b-v04.IQ3_XS.gguf) | IQ3_XS | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-GGUF/resolve/main/bagel-dpo-20b-v04.Q3_K_S.gguf) | Q3_K_S | 9.5 | |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-GGUF/resolve/main/bagel-dpo-20b-v04.IQ3_S.gguf) | IQ3_S | 9.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-GGUF/resolve/main/bagel-dpo-20b-v04.IQ3_M.gguf) | IQ3_M | 9.9 | |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-GGUF/resolve/main/bagel-dpo-20b-v04.Q3_K_M.gguf) | Q3_K_M | 10.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-GGUF/resolve/main/bagel-dpo-20b-v04.Q3_K_L.gguf) | Q3_K_L | 11.3 | |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-GGUF/resolve/main/bagel-dpo-20b-v04.IQ4_XS.gguf) | IQ4_XS | 11.6 | |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-GGUF/resolve/main/bagel-dpo-20b-v04.Q4_K_S.gguf) | Q4_K_S | 12.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-GGUF/resolve/main/bagel-dpo-20b-v04.Q4_K_M.gguf) | Q4_K_M | 12.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-GGUF/resolve/main/bagel-dpo-20b-v04.Q5_K_S.gguf) | Q5_K_S | 14.5 | |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-GGUF/resolve/main/bagel-dpo-20b-v04.Q5_K_M.gguf) | Q5_K_M | 14.8 | |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-GGUF/resolve/main/bagel-dpo-20b-v04.Q6_K.gguf) | Q6_K | 17.1 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-GGUF/resolve/main/bagel-dpo-20b-v04.Q8_0.gguf) | Q8_0 | 21.7 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-20b-v04-GGUF/resolve/main/bagel-dpo-20b-v04.SOURCE.gguf) | SOURCE | 39.8 | source gguf, only provided when it was hard to come by |
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.
<!-- end -->
|
mradermacher/Yeet_51b_200k-i1-GGUF
|
mradermacher
| 2024-05-06T05:23:14Z | 30 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:MarsupialAI/Yeet_51b_200k",
"base_model:quantized:MarsupialAI/Yeet_51b_200k",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-04-02T09:58:07Z |
---
base_model: MarsupialAI/Yeet_51b_200k
language:
- en
library_name: transformers
license: other
license_name: yi-other
no_imatrix: 'IQ3_XXS GGML_ASSERT: llama.cpp/ggml-quants.c:11239: grid_index >= 0'
quantized_by: mradermacher
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/MarsupialAI/Yeet_51b_200k
**No more quants forthcoming, as llama.cpp crashes.**
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Yeet_51b_200k-GGUF
## 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/Yeet_51b_200k-i1-GGUF/resolve/main/Yeet_51b_200k.i1-Q2_K.gguf) | i1-Q2_K | 19.6 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-i1-GGUF/resolve/main/Yeet_51b_200k.i1-Q3_K_S.gguf) | i1-Q3_K_S | 22.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-i1-GGUF/resolve/main/Yeet_51b_200k.i1-Q3_K_M.gguf) | i1-Q3_K_M | 25.3 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-i1-GGUF/resolve/main/Yeet_51b_200k.i1-Q3_K_L.gguf) | i1-Q3_K_L | 27.6 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-i1-GGUF/resolve/main/Yeet_51b_200k.i1-Q4_0.gguf) | i1-Q4_0 | 29.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-i1-GGUF/resolve/main/Yeet_51b_200k.i1-Q4_K_S.gguf) | i1-Q4_K_S | 29.7 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-i1-GGUF/resolve/main/Yeet_51b_200k.i1-Q4_K_M.gguf) | i1-Q4_K_M | 31.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-i1-GGUF/resolve/main/Yeet_51b_200k.i1-Q5_K_S.gguf) | i1-Q5_K_S | 35.9 | |
| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-i1-GGUF/resolve/main/Yeet_51b_200k.i1-Q5_K_M.gguf) | i1-Q5_K_M | 36.8 | |
| [GGUF](https://huggingface.co/mradermacher/Yeet_51b_200k-i1-GGUF/resolve/main/Yeet_51b_200k.i1-Q6_K.gguf) | i1-Q6_K | 42.6 | 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](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/Maxine-7B-0401-ties-GGUF
|
mradermacher
| 2024-05-06T05:23:06Z | 1 | 1 |
transformers
|
[
"transformers",
"gguf",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-02T12:15:43Z |
---
base_model: louisbrulenaudet/Maxine-7B-0401-ties
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/louisbrulenaudet/Maxine-7B-0401-ties
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/Maxine-7B-0401-ties-GGUF/resolve/main/Maxine-7B-0401-ties.Q2_K.gguf) | Q2_K | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Maxine-7B-0401-ties-GGUF/resolve/main/Maxine-7B-0401-ties.IQ3_XS.gguf) | IQ3_XS | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Maxine-7B-0401-ties-GGUF/resolve/main/Maxine-7B-0401-ties.Q3_K_S.gguf) | Q3_K_S | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Maxine-7B-0401-ties-GGUF/resolve/main/Maxine-7B-0401-ties.IQ3_S.gguf) | IQ3_S | 3.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Maxine-7B-0401-ties-GGUF/resolve/main/Maxine-7B-0401-ties.IQ3_M.gguf) | IQ3_M | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/Maxine-7B-0401-ties-GGUF/resolve/main/Maxine-7B-0401-ties.Q3_K_M.gguf) | Q3_K_M | 3.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Maxine-7B-0401-ties-GGUF/resolve/main/Maxine-7B-0401-ties.Q3_K_L.gguf) | Q3_K_L | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/Maxine-7B-0401-ties-GGUF/resolve/main/Maxine-7B-0401-ties.IQ4_XS.gguf) | IQ4_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Maxine-7B-0401-ties-GGUF/resolve/main/Maxine-7B-0401-ties.Q4_K_S.gguf) | Q4_K_S | 4.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Maxine-7B-0401-ties-GGUF/resolve/main/Maxine-7B-0401-ties.Q4_K_M.gguf) | Q4_K_M | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Maxine-7B-0401-ties-GGUF/resolve/main/Maxine-7B-0401-ties.Q5_K_S.gguf) | Q5_K_S | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/Maxine-7B-0401-ties-GGUF/resolve/main/Maxine-7B-0401-ties.Q5_K_M.gguf) | Q5_K_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Maxine-7B-0401-ties-GGUF/resolve/main/Maxine-7B-0401-ties.Q6_K.gguf) | Q6_K | 6.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Maxine-7B-0401-ties-GGUF/resolve/main/Maxine-7B-0401-ties.Q8_0.gguf) | Q8_0 | 7.9 | 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
## 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.
<!-- end -->
|
nuebaek/komt_mistral_mss_user_111_max_steps_80
|
nuebaek
| 2024-05-06T05:22:39Z | 76 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-05-06T05:19:43Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mradermacher/MultiVerse_70B-GGUF
|
mradermacher
| 2024-05-06T05:22:12Z | 22 | 4 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:MTSAIR/MultiVerse_70B",
"base_model:quantized:MTSAIR/MultiVerse_70B",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-04-02T19:02:24Z |
---
base_model: MTSAIR/MultiVerse_70B
language:
- en
library_name: transformers
license: other
license_link: https://huggingface.co/Qwen/Qwen1.5-72B-Chat/blob/main/LICENSE
license_name: qwen
quantized_by: mradermacher
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/MTSAIR/MultiVerse_70B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF
## 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/MultiVerse_70B-GGUF/resolve/main/MultiVerse_70B.Q2_K.gguf) | Q2_K | 28.6 | |
| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-GGUF/resolve/main/MultiVerse_70B.IQ3_XS.gguf) | IQ3_XS | 31.5 | |
| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-GGUF/resolve/main/MultiVerse_70B.IQ3_S.gguf) | IQ3_S | 33.1 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-GGUF/resolve/main/MultiVerse_70B.Q3_K_S.gguf) | Q3_K_S | 33.1 | |
| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-GGUF/resolve/main/MultiVerse_70B.IQ3_M.gguf) | IQ3_M | 34.8 | |
| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-GGUF/resolve/main/MultiVerse_70B.Q3_K_M.gguf) | Q3_K_M | 36.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-GGUF/resolve/main/MultiVerse_70B.Q3_K_L.gguf) | Q3_K_L | 40.1 | |
| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-GGUF/resolve/main/MultiVerse_70B.IQ4_XS.gguf) | IQ4_XS | 40.7 | |
| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-GGUF/resolve/main/MultiVerse_70B.Q4_K_S.gguf) | Q4_K_S | 42.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-GGUF/resolve/main/MultiVerse_70B.Q4_K_M.gguf) | Q4_K_M | 45.3 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/MultiVerse_70B-GGUF/resolve/main/MultiVerse_70B.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/MultiVerse_70B-GGUF/resolve/main/MultiVerse_70B.Q5_K_S.gguf.part2of2) | Q5_K_S | 51.5 | |
| [PART 1](https://huggingface.co/mradermacher/MultiVerse_70B-GGUF/resolve/main/MultiVerse_70B.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/MultiVerse_70B-GGUF/resolve/main/MultiVerse_70B.Q5_K_M.gguf.part2of2) | Q5_K_M | 52.9 | |
| [PART 1](https://huggingface.co/mradermacher/MultiVerse_70B-GGUF/resolve/main/MultiVerse_70B.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/MultiVerse_70B-GGUF/resolve/main/MultiVerse_70B.Q6_K.gguf.part2of2) | Q6_K | 60.9 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/MultiVerse_70B-GGUF/resolve/main/MultiVerse_70B.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/MultiVerse_70B-GGUF/resolve/main/MultiVerse_70B.Q8_0.gguf.part2of2) | Q8_0 | 78.1 | 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
## 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.
<!-- end -->
|
mradermacher/HyouKan-3x7B-GGUF
|
mradermacher
| 2024-05-06T05:22:02Z | 53 | 1 |
transformers
|
[
"transformers",
"gguf",
"moe",
"merge",
"Roleplay",
"en",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-02T23:18:27Z |
---
base_model: Alsebay/HyouKan-3x7B
language:
- en
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
tags:
- moe
- merge
- Roleplay
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Alsebay/HyouKan-3x7B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/HyouKan-3x7B-GGUF/resolve/main/HyouKan-3x7B.Q2_K.gguf) | Q2_K | 7.1 | |
| [GGUF](https://huggingface.co/mradermacher/HyouKan-3x7B-GGUF/resolve/main/HyouKan-3x7B.IQ3_XS.gguf) | IQ3_XS | 7.8 | |
| [GGUF](https://huggingface.co/mradermacher/HyouKan-3x7B-GGUF/resolve/main/HyouKan-3x7B.Q3_K_S.gguf) | Q3_K_S | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/HyouKan-3x7B-GGUF/resolve/main/HyouKan-3x7B.IQ3_S.gguf) | IQ3_S | 8.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/HyouKan-3x7B-GGUF/resolve/main/HyouKan-3x7B.IQ3_M.gguf) | IQ3_M | 8.4 | |
| [GGUF](https://huggingface.co/mradermacher/HyouKan-3x7B-GGUF/resolve/main/HyouKan-3x7B.Q3_K_M.gguf) | Q3_K_M | 9.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/HyouKan-3x7B-GGUF/resolve/main/HyouKan-3x7B.Q3_K_L.gguf) | Q3_K_L | 9.9 | |
| [GGUF](https://huggingface.co/mradermacher/HyouKan-3x7B-GGUF/resolve/main/HyouKan-3x7B.IQ4_XS.gguf) | IQ4_XS | 10.3 | |
| [GGUF](https://huggingface.co/mradermacher/HyouKan-3x7B-GGUF/resolve/main/HyouKan-3x7B.Q4_K_S.gguf) | Q4_K_S | 10.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/HyouKan-3x7B-GGUF/resolve/main/HyouKan-3x7B.Q4_K_M.gguf) | Q4_K_M | 11.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/HyouKan-3x7B-GGUF/resolve/main/HyouKan-3x7B.Q5_K_S.gguf) | Q5_K_S | 13.0 | |
| [GGUF](https://huggingface.co/mradermacher/HyouKan-3x7B-GGUF/resolve/main/HyouKan-3x7B.Q5_K_M.gguf) | Q5_K_M | 13.4 | |
| [GGUF](https://huggingface.co/mradermacher/HyouKan-3x7B-GGUF/resolve/main/HyouKan-3x7B.Q6_K.gguf) | Q6_K | 15.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/HyouKan-3x7B-GGUF/resolve/main/HyouKan-3x7B.Q8_0.gguf) | Q8_0 | 19.9 | 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
## 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.
<!-- end -->
|
mradermacher/airoboros-34b-3.3-GGUF
|
mradermacher
| 2024-05-06T05:21:59Z | 60 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"dataset:jondurbin/airoboros-3.2",
"dataset:bluemoon-fandom-1-1-rp-cleaned",
"dataset:boolq",
"dataset:jondurbin/gutenberg-dpo-v0.1",
"dataset:LDJnr/Capybara",
"dataset:jondurbin/cinematika-v0.1",
"dataset:glaiveai/glaive-function-calling-v2",
"dataset:grimulkan/LimaRP-augmented",
"dataset:piqa",
"dataset:Vezora/Tested-22k-Python-Alpaca",
"dataset:mattpscott/airoboros-summarization",
"dataset:unalignment/toxic-dpo-v0.2",
"base_model:jondurbin/airoboros-34b-3.3",
"base_model:quantized:jondurbin/airoboros-34b-3.3",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-04-02T23:45:26Z |
---
base_model: jondurbin/airoboros-34b-3.3
datasets:
- jondurbin/airoboros-3.2
- bluemoon-fandom-1-1-rp-cleaned
- boolq
- jondurbin/gutenberg-dpo-v0.1
- LDJnr/Capybara
- jondurbin/cinematika-v0.1
- glaiveai/glaive-function-calling-v2
- grimulkan/LimaRP-augmented
- piqa
- Vezora/Tested-22k-Python-Alpaca
- mattpscott/airoboros-summarization
- unalignment/toxic-dpo-v0.2
language:
- en
library_name: transformers
license: other
license_link: https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE
license_name: yi-license
quantized_by: mradermacher
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/jondurbin/airoboros-34b-3.3
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/airoboros-34b-3.3-i1-GGUF
## 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/airoboros-34b-3.3-GGUF/resolve/main/airoboros-34b-3.3.Q2_K.gguf) | Q2_K | 13.5 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-34b-3.3-GGUF/resolve/main/airoboros-34b-3.3.IQ3_XS.gguf) | IQ3_XS | 14.9 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-34b-3.3-GGUF/resolve/main/airoboros-34b-3.3.Q3_K_S.gguf) | Q3_K_S | 15.6 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-34b-3.3-GGUF/resolve/main/airoboros-34b-3.3.IQ3_S.gguf) | IQ3_S | 15.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/airoboros-34b-3.3-GGUF/resolve/main/airoboros-34b-3.3.IQ3_M.gguf) | IQ3_M | 16.2 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-34b-3.3-GGUF/resolve/main/airoboros-34b-3.3.Q3_K_M.gguf) | Q3_K_M | 17.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/airoboros-34b-3.3-GGUF/resolve/main/airoboros-34b-3.3.Q3_K_L.gguf) | Q3_K_L | 18.8 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-34b-3.3-GGUF/resolve/main/airoboros-34b-3.3.IQ4_XS.gguf) | IQ4_XS | 19.3 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-34b-3.3-GGUF/resolve/main/airoboros-34b-3.3.Q4_K_S.gguf) | Q4_K_S | 20.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/airoboros-34b-3.3-GGUF/resolve/main/airoboros-34b-3.3.Q4_K_M.gguf) | Q4_K_M | 21.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/airoboros-34b-3.3-GGUF/resolve/main/airoboros-34b-3.3.Q5_K_S.gguf) | Q5_K_S | 24.3 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-34b-3.3-GGUF/resolve/main/airoboros-34b-3.3.Q5_K_M.gguf) | Q5_K_M | 25.0 | |
| [GGUF](https://huggingface.co/mradermacher/airoboros-34b-3.3-GGUF/resolve/main/airoboros-34b-3.3.Q6_K.gguf) | Q6_K | 28.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/airoboros-34b-3.3-GGUF/resolve/main/airoboros-34b-3.3.Q8_0.gguf) | Q8_0 | 37.1 | 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
## 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.
<!-- end -->
|
mradermacher/Mistral-12.25B-Instruct-v0.2-GGUF
|
mradermacher
| 2024-05-06T05:21:54Z | 17 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:Joseph717171/Mistral-12.25B-Instruct-v0.2",
"base_model:quantized:Joseph717171/Mistral-12.25B-Instruct-v0.2",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-03T00:13:46Z |
---
base_model: Joseph717171/Mistral-12.25B-Instruct-v0.2
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Joseph717171/Mistral-12.25B-Instruct-v0.2
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/Mistral-12.25B-Instruct-v0.2-GGUF/resolve/main/Mistral-12.25B-Instruct-v0.2.Q2_K.gguf) | Q2_K | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-12.25B-Instruct-v0.2-GGUF/resolve/main/Mistral-12.25B-Instruct-v0.2.IQ3_XS.gguf) | IQ3_XS | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-12.25B-Instruct-v0.2-GGUF/resolve/main/Mistral-12.25B-Instruct-v0.2.Q3_K_S.gguf) | Q3_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-12.25B-Instruct-v0.2-GGUF/resolve/main/Mistral-12.25B-Instruct-v0.2.IQ3_S.gguf) | IQ3_S | 5.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Mistral-12.25B-Instruct-v0.2-GGUF/resolve/main/Mistral-12.25B-Instruct-v0.2.IQ3_M.gguf) | IQ3_M | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-12.25B-Instruct-v0.2-GGUF/resolve/main/Mistral-12.25B-Instruct-v0.2.Q3_K_M.gguf) | Q3_K_M | 6.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-12.25B-Instruct-v0.2-GGUF/resolve/main/Mistral-12.25B-Instruct-v0.2.Q3_K_L.gguf) | Q3_K_L | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-12.25B-Instruct-v0.2-GGUF/resolve/main/Mistral-12.25B-Instruct-v0.2.IQ4_XS.gguf) | IQ4_XS | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-12.25B-Instruct-v0.2-GGUF/resolve/main/Mistral-12.25B-Instruct-v0.2.Q4_K_S.gguf) | Q4_K_S | 7.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mistral-12.25B-Instruct-v0.2-GGUF/resolve/main/Mistral-12.25B-Instruct-v0.2.Q4_K_M.gguf) | Q4_K_M | 7.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mistral-12.25B-Instruct-v0.2-GGUF/resolve/main/Mistral-12.25B-Instruct-v0.2.Q5_K_S.gguf) | Q5_K_S | 8.9 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-12.25B-Instruct-v0.2-GGUF/resolve/main/Mistral-12.25B-Instruct-v0.2.Q5_K_M.gguf) | Q5_K_M | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-12.25B-Instruct-v0.2-GGUF/resolve/main/Mistral-12.25B-Instruct-v0.2.Q6_K.gguf) | Q6_K | 10.5 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-12.25B-Instruct-v0.2-GGUF/resolve/main/Mistral-12.25B-Instruct-v0.2.Q8_0.gguf) | Q8_0 | 13.5 | 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
## 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.
<!-- end -->
|
mradermacher/bagel-dpo-34b-v0.5-i1-GGUF
|
mradermacher
| 2024-05-06T05:21:48Z | 238 | 4 |
transformers
|
[
"transformers",
"gguf",
"en",
"dataset:ai2_arc",
"dataset:allenai/ultrafeedback_binarized_cleaned",
"dataset:argilla/distilabel-intel-orca-dpo-pairs",
"dataset:jondurbin/airoboros-3.2",
"dataset:codeparrot/apps",
"dataset:facebook/belebele",
"dataset:bluemoon-fandom-1-1-rp-cleaned",
"dataset:boolq",
"dataset:camel-ai/biology",
"dataset:camel-ai/chemistry",
"dataset:camel-ai/math",
"dataset:camel-ai/physics",
"dataset:jondurbin/contextual-dpo-v0.1",
"dataset:jondurbin/gutenberg-dpo-v0.1",
"dataset:jondurbin/py-dpo-v0.1",
"dataset:jondurbin/truthy-dpo-v0.1",
"dataset:LDJnr/Capybara",
"dataset:jondurbin/cinematika-v0.1",
"dataset:WizardLM/WizardLM_evol_instruct_70k",
"dataset:glaiveai/glaive-function-calling-v2",
"dataset:grimulkan/LimaRP-augmented",
"dataset:lmsys/lmsys-chat-1m",
"dataset:ParisNeo/lollms_aware_dataset",
"dataset:TIGER-Lab/MathInstruct",
"dataset:Muennighoff/natural-instructions",
"dataset:openbookqa",
"dataset:kingbri/PIPPA-shareGPT",
"dataset:piqa",
"dataset:Vezora/Tested-22k-Python-Alpaca",
"dataset:ropes",
"dataset:cakiki/rosetta-code",
"dataset:Open-Orca/SlimOrca",
"dataset:b-mc2/sql-create-context",
"dataset:squad_v2",
"dataset:mattpscott/airoboros-summarization",
"dataset:migtissera/Synthia-v1.3",
"dataset:unalignment/toxic-dpo-v0.2",
"dataset:WhiteRabbitNeo/WRN-Chapter-1",
"dataset:WhiteRabbitNeo/WRN-Chapter-2",
"dataset:winogrande",
"base_model:jondurbin/bagel-dpo-34b-v0.5",
"base_model:quantized:jondurbin/bagel-dpo-34b-v0.5",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-04-03T00:45:48Z |
---
base_model: jondurbin/bagel-dpo-34b-v0.5
datasets:
- ai2_arc
- allenai/ultrafeedback_binarized_cleaned
- argilla/distilabel-intel-orca-dpo-pairs
- jondurbin/airoboros-3.2
- codeparrot/apps
- facebook/belebele
- bluemoon-fandom-1-1-rp-cleaned
- boolq
- camel-ai/biology
- camel-ai/chemistry
- camel-ai/math
- camel-ai/physics
- jondurbin/contextual-dpo-v0.1
- jondurbin/gutenberg-dpo-v0.1
- jondurbin/py-dpo-v0.1
- jondurbin/truthy-dpo-v0.1
- LDJnr/Capybara
- jondurbin/cinematika-v0.1
- WizardLM/WizardLM_evol_instruct_70k
- glaiveai/glaive-function-calling-v2
- jondurbin/gutenberg-dpo-v0.1
- grimulkan/LimaRP-augmented
- lmsys/lmsys-chat-1m
- ParisNeo/lollms_aware_dataset
- TIGER-Lab/MathInstruct
- Muennighoff/natural-instructions
- openbookqa
- kingbri/PIPPA-shareGPT
- piqa
- Vezora/Tested-22k-Python-Alpaca
- ropes
- cakiki/rosetta-code
- Open-Orca/SlimOrca
- b-mc2/sql-create-context
- squad_v2
- mattpscott/airoboros-summarization
- migtissera/Synthia-v1.3
- unalignment/toxic-dpo-v0.2
- WhiteRabbitNeo/WRN-Chapter-1
- WhiteRabbitNeo/WRN-Chapter-2
- winogrande
language:
- en
library_name: transformers
license: other
license_link: https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE
license_name: yi-license
quantized_by: mradermacher
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/jondurbin/bagel-dpo-34b-v0.5
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/bagel-dpo-34b-v0.5-GGUF
## 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/bagel-dpo-34b-v0.5-i1-GGUF/resolve/main/bagel-dpo-34b-v0.5.i1-IQ1_S.gguf) | i1-IQ1_S | 8.2 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-34b-v0.5-i1-GGUF/resolve/main/bagel-dpo-34b-v0.5.i1-IQ1_M.gguf) | i1-IQ1_M | 8.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-34b-v0.5-i1-GGUF/resolve/main/bagel-dpo-34b-v0.5.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 10.0 | |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-34b-v0.5-i1-GGUF/resolve/main/bagel-dpo-34b-v0.5.i1-IQ2_XS.gguf) | i1-IQ2_XS | 11.0 | |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-34b-v0.5-i1-GGUF/resolve/main/bagel-dpo-34b-v0.5.i1-IQ2_S.gguf) | i1-IQ2_S | 11.6 | |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-34b-v0.5-i1-GGUF/resolve/main/bagel-dpo-34b-v0.5.i1-IQ2_M.gguf) | i1-IQ2_M | 12.5 | |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-34b-v0.5-i1-GGUF/resolve/main/bagel-dpo-34b-v0.5.i1-Q2_K.gguf) | i1-Q2_K | 13.5 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-34b-v0.5-i1-GGUF/resolve/main/bagel-dpo-34b-v0.5.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 14.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-34b-v0.5-i1-GGUF/resolve/main/bagel-dpo-34b-v0.5.i1-IQ3_XS.gguf) | i1-IQ3_XS | 14.9 | |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-34b-v0.5-i1-GGUF/resolve/main/bagel-dpo-34b-v0.5.i1-Q3_K_S.gguf) | i1-Q3_K_S | 15.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-34b-v0.5-i1-GGUF/resolve/main/bagel-dpo-34b-v0.5.i1-IQ3_S.gguf) | i1-IQ3_S | 15.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-34b-v0.5-i1-GGUF/resolve/main/bagel-dpo-34b-v0.5.i1-IQ3_M.gguf) | i1-IQ3_M | 16.2 | |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-34b-v0.5-i1-GGUF/resolve/main/bagel-dpo-34b-v0.5.i1-Q3_K_M.gguf) | i1-Q3_K_M | 17.3 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-34b-v0.5-i1-GGUF/resolve/main/bagel-dpo-34b-v0.5.i1-Q3_K_L.gguf) | i1-Q3_K_L | 18.8 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-34b-v0.5-i1-GGUF/resolve/main/bagel-dpo-34b-v0.5.i1-IQ4_XS.gguf) | i1-IQ4_XS | 19.1 | |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-34b-v0.5-i1-GGUF/resolve/main/bagel-dpo-34b-v0.5.i1-Q4_0.gguf) | i1-Q4_0 | 20.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-34b-v0.5-i1-GGUF/resolve/main/bagel-dpo-34b-v0.5.i1-Q4_K_S.gguf) | i1-Q4_K_S | 20.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-34b-v0.5-i1-GGUF/resolve/main/bagel-dpo-34b-v0.5.i1-Q4_K_M.gguf) | i1-Q4_K_M | 21.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-34b-v0.5-i1-GGUF/resolve/main/bagel-dpo-34b-v0.5.i1-Q5_K_S.gguf) | i1-Q5_K_S | 24.3 | |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-34b-v0.5-i1-GGUF/resolve/main/bagel-dpo-34b-v0.5.i1-Q5_K_M.gguf) | i1-Q5_K_M | 25.0 | |
| [GGUF](https://huggingface.co/mradermacher/bagel-dpo-34b-v0.5-i1-GGUF/resolve/main/bagel-dpo-34b-v0.5.i1-Q6_K.gguf) | i1-Q6_K | 28.9 | 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.
<!-- end -->
|
mradermacher/Pearl-34B-ties-GGUF
|
mradermacher
| 2024-05-06T05:21:42Z | 19 | 0 |
transformers
|
[
"transformers",
"gguf",
"merge",
"mergekit",
"jondurbin/bagel-dpo-34b-v0.2",
"abacusai/MetaMath-Bagel-DPO-34B",
"en",
"base_model:louisbrulenaudet/Pearl-34B-ties",
"base_model:quantized:louisbrulenaudet/Pearl-34B-ties",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-03T01:27:03Z |
---
base_model: louisbrulenaudet/Pearl-34B-ties
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- merge
- mergekit
- jondurbin/bagel-dpo-34b-v0.2
- abacusai/MetaMath-Bagel-DPO-34B
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/louisbrulenaudet/Pearl-34B-ties
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Pearl-34B-ties-i1-GGUF
## 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/Pearl-34B-ties-GGUF/resolve/main/Pearl-34B-ties.Q2_K.gguf) | Q2_K | 13.5 | |
| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-GGUF/resolve/main/Pearl-34B-ties.IQ3_XS.gguf) | IQ3_XS | 14.9 | |
| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-GGUF/resolve/main/Pearl-34B-ties.Q3_K_S.gguf) | Q3_K_S | 15.6 | |
| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-GGUF/resolve/main/Pearl-34B-ties.IQ3_S.gguf) | IQ3_S | 15.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-GGUF/resolve/main/Pearl-34B-ties.IQ3_M.gguf) | IQ3_M | 16.2 | |
| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-GGUF/resolve/main/Pearl-34B-ties.Q3_K_M.gguf) | Q3_K_M | 17.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-GGUF/resolve/main/Pearl-34B-ties.Q3_K_L.gguf) | Q3_K_L | 18.8 | |
| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-GGUF/resolve/main/Pearl-34B-ties.IQ4_XS.gguf) | IQ4_XS | 19.3 | |
| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-GGUF/resolve/main/Pearl-34B-ties.Q4_K_S.gguf) | Q4_K_S | 20.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-GGUF/resolve/main/Pearl-34B-ties.Q4_K_M.gguf) | Q4_K_M | 21.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-GGUF/resolve/main/Pearl-34B-ties.Q5_K_S.gguf) | Q5_K_S | 24.3 | |
| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-GGUF/resolve/main/Pearl-34B-ties.Q5_K_M.gguf) | Q5_K_M | 25.0 | |
| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-GGUF/resolve/main/Pearl-34B-ties.Q6_K.gguf) | Q6_K | 28.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Pearl-34B-ties-GGUF/resolve/main/Pearl-34B-ties.Q8_0.gguf) | Q8_0 | 37.1 | 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
## 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.
<!-- end -->
|
mradermacher/Mixtral_AI_Cyber_5.0_SFT-GGUF
|
mradermacher
| 2024-05-06T05:21:37Z | 18 | 1 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-03T02:30:42Z |
---
base_model: LeroyDyer/Mixtral_AI_Cyber_5.0_SFT
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/LeroyDyer/Mixtral_AI_Cyber_5.0_SFT
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/Mixtral_AI_Cyber_5.0_SFT-GGUF/resolve/main/Mixtral_AI_Cyber_5.0_SFT.Q2_K.gguf) | Q2_K | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_5.0_SFT-GGUF/resolve/main/Mixtral_AI_Cyber_5.0_SFT.IQ3_XS.gguf) | IQ3_XS | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_5.0_SFT-GGUF/resolve/main/Mixtral_AI_Cyber_5.0_SFT.Q3_K_S.gguf) | Q3_K_S | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_5.0_SFT-GGUF/resolve/main/Mixtral_AI_Cyber_5.0_SFT.IQ3_S.gguf) | IQ3_S | 3.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_5.0_SFT-GGUF/resolve/main/Mixtral_AI_Cyber_5.0_SFT.IQ3_M.gguf) | IQ3_M | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_5.0_SFT-GGUF/resolve/main/Mixtral_AI_Cyber_5.0_SFT.Q3_K_M.gguf) | Q3_K_M | 3.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_5.0_SFT-GGUF/resolve/main/Mixtral_AI_Cyber_5.0_SFT.Q3_K_L.gguf) | Q3_K_L | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_5.0_SFT-GGUF/resolve/main/Mixtral_AI_Cyber_5.0_SFT.IQ4_XS.gguf) | IQ4_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_5.0_SFT-GGUF/resolve/main/Mixtral_AI_Cyber_5.0_SFT.Q4_K_S.gguf) | Q4_K_S | 4.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_5.0_SFT-GGUF/resolve/main/Mixtral_AI_Cyber_5.0_SFT.Q4_K_M.gguf) | Q4_K_M | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_5.0_SFT-GGUF/resolve/main/Mixtral_AI_Cyber_5.0_SFT.Q5_K_S.gguf) | Q5_K_S | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_5.0_SFT-GGUF/resolve/main/Mixtral_AI_Cyber_5.0_SFT.Q5_K_M.gguf) | Q5_K_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_5.0_SFT-GGUF/resolve/main/Mixtral_AI_Cyber_5.0_SFT.Q6_K.gguf) | Q6_K | 6.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Mixtral_AI_Cyber_5.0_SFT-GGUF/resolve/main/Mixtral_AI_Cyber_5.0_SFT.Q8_0.gguf) | Q8_0 | 7.9 | 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
## 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.
<!-- end -->
|
Wespeaker/wespeaker-cnceleb-resnet34
|
Wespeaker
| 2024-05-06T05:21:36Z | 2 | 1 | null |
[
"onnx",
"license:apache-2.0",
"region:us"
] | null | 2024-05-06T05:14:50Z |
---
license: apache-2.0
---
|
mradermacher/Mistral-dolphin-2.8-grok-instract-2-7B-slerp-GGUF
|
mradermacher
| 2024-05-06T05:21:23Z | 99 | 2 |
transformers
|
[
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"nasiruddin15/Mistral-grok-instract-2-7B-slerp",
"cognitivecomputations/dolphin-2.8-mistral-7b-v02",
"en",
"base_model:nasiruddin15/Mistral-dolphin-2.8-grok-instract-2-7B-slerp",
"base_model:quantized:nasiruddin15/Mistral-dolphin-2.8-grok-instract-2-7B-slerp",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-03T05:36:56Z |
---
base_model: nasiruddin15/Mistral-dolphin-2.8-grok-instract-2-7B-slerp
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- nasiruddin15/Mistral-grok-instract-2-7B-slerp
- cognitivecomputations/dolphin-2.8-mistral-7b-v02
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/nasiruddin15/Mistral-dolphin-2.8-grok-instract-2-7B-slerp
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/Mistral-dolphin-2.8-grok-instract-2-7B-slerp-GGUF/resolve/main/Mistral-dolphin-2.8-grok-instract-2-7B-slerp.Q2_K.gguf) | Q2_K | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-dolphin-2.8-grok-instract-2-7B-slerp-GGUF/resolve/main/Mistral-dolphin-2.8-grok-instract-2-7B-slerp.IQ3_XS.gguf) | IQ3_XS | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-dolphin-2.8-grok-instract-2-7B-slerp-GGUF/resolve/main/Mistral-dolphin-2.8-grok-instract-2-7B-slerp.Q3_K_S.gguf) | Q3_K_S | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-dolphin-2.8-grok-instract-2-7B-slerp-GGUF/resolve/main/Mistral-dolphin-2.8-grok-instract-2-7B-slerp.IQ3_S.gguf) | IQ3_S | 3.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Mistral-dolphin-2.8-grok-instract-2-7B-slerp-GGUF/resolve/main/Mistral-dolphin-2.8-grok-instract-2-7B-slerp.IQ3_M.gguf) | IQ3_M | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-dolphin-2.8-grok-instract-2-7B-slerp-GGUF/resolve/main/Mistral-dolphin-2.8-grok-instract-2-7B-slerp.Q3_K_M.gguf) | Q3_K_M | 3.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-dolphin-2.8-grok-instract-2-7B-slerp-GGUF/resolve/main/Mistral-dolphin-2.8-grok-instract-2-7B-slerp.Q3_K_L.gguf) | Q3_K_L | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-dolphin-2.8-grok-instract-2-7B-slerp-GGUF/resolve/main/Mistral-dolphin-2.8-grok-instract-2-7B-slerp.IQ4_XS.gguf) | IQ4_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-dolphin-2.8-grok-instract-2-7B-slerp-GGUF/resolve/main/Mistral-dolphin-2.8-grok-instract-2-7B-slerp.Q4_K_S.gguf) | Q4_K_S | 4.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mistral-dolphin-2.8-grok-instract-2-7B-slerp-GGUF/resolve/main/Mistral-dolphin-2.8-grok-instract-2-7B-slerp.Q4_K_M.gguf) | Q4_K_M | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mistral-dolphin-2.8-grok-instract-2-7B-slerp-GGUF/resolve/main/Mistral-dolphin-2.8-grok-instract-2-7B-slerp.Q5_K_S.gguf) | Q5_K_S | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-dolphin-2.8-grok-instract-2-7B-slerp-GGUF/resolve/main/Mistral-dolphin-2.8-grok-instract-2-7B-slerp.Q5_K_M.gguf) | Q5_K_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-dolphin-2.8-grok-instract-2-7B-slerp-GGUF/resolve/main/Mistral-dolphin-2.8-grok-instract-2-7B-slerp.Q6_K.gguf) | Q6_K | 6.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-dolphin-2.8-grok-instract-2-7B-slerp-GGUF/resolve/main/Mistral-dolphin-2.8-grok-instract-2-7B-slerp.Q8_0.gguf) | Q8_0 | 7.9 | 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
## 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.
<!-- end -->
|
mradermacher/MultiVerse_70B-i1-GGUF
|
mradermacher
| 2024-05-06T05:21:09Z | 35 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:MTSAIR/MultiVerse_70B",
"base_model:quantized:MTSAIR/MultiVerse_70B",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-04-03T08:54:49Z |
---
base_model: MTSAIR/MultiVerse_70B
language:
- en
library_name: transformers
license: other
license_link: https://huggingface.co/Qwen/Qwen1.5-72B-Chat/blob/main/LICENSE
license_name: qwen
quantized_by: mradermacher
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/MTSAIR/MultiVerse_70B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/MultiVerse_70B-GGUF
## 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/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-IQ1_S.gguf) | i1-IQ1_S | 18.0 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-IQ1_M.gguf) | i1-IQ1_M | 19.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 21.5 | |
| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 23.5 | |
| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-IQ2_S.gguf) | i1-IQ2_S | 25.1 | |
| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-IQ2_M.gguf) | i1-IQ2_M | 26.9 | |
| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-Q2_K.gguf) | i1-Q2_K | 28.6 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 29.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 31.5 | |
| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-IQ3_S.gguf) | i1-IQ3_S | 33.1 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 33.1 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-IQ3_M.gguf) | i1-IQ3_M | 34.8 | |
| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 36.8 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 40.1 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 40.4 | |
| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-Q4_0.gguf) | i1-Q4_0 | 42.7 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 42.9 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 45.3 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-Q5_K_S.gguf.part2of2) | i1-Q5_K_S | 51.5 | |
| [PART 1](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-Q5_K_M.gguf.part2of2) | i1-Q5_K_M | 52.9 | |
| [PART 1](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/MultiVerse_70B-i1-GGUF/resolve/main/MultiVerse_70B.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 60.9 | 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.
<!-- end -->
|
mradermacher/Synatra-7B-v0.3-RP-GGUF
|
mradermacher
| 2024-05-06T05:21:06Z | 17 | 1 |
transformers
|
[
"transformers",
"gguf",
"ko",
"base_model:maywell/Synatra-7B-v0.3-RP",
"base_model:quantized:maywell/Synatra-7B-v0.3-RP",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-03T09:51:38Z |
---
base_model: maywell/Synatra-7B-v0.3-RP
language:
- ko
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/maywell/Synatra-7B-v0.3-RP
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/Synatra-7B-v0.3-RP-GGUF/resolve/main/Synatra-7B-v0.3-RP.Q2_K.gguf) | Q2_K | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Synatra-7B-v0.3-RP-GGUF/resolve/main/Synatra-7B-v0.3-RP.IQ3_XS.gguf) | IQ3_XS | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Synatra-7B-v0.3-RP-GGUF/resolve/main/Synatra-7B-v0.3-RP.Q3_K_S.gguf) | Q3_K_S | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Synatra-7B-v0.3-RP-GGUF/resolve/main/Synatra-7B-v0.3-RP.IQ3_S.gguf) | IQ3_S | 3.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Synatra-7B-v0.3-RP-GGUF/resolve/main/Synatra-7B-v0.3-RP.IQ3_M.gguf) | IQ3_M | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/Synatra-7B-v0.3-RP-GGUF/resolve/main/Synatra-7B-v0.3-RP.Q3_K_M.gguf) | Q3_K_M | 3.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Synatra-7B-v0.3-RP-GGUF/resolve/main/Synatra-7B-v0.3-RP.Q3_K_L.gguf) | Q3_K_L | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/Synatra-7B-v0.3-RP-GGUF/resolve/main/Synatra-7B-v0.3-RP.IQ4_XS.gguf) | IQ4_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Synatra-7B-v0.3-RP-GGUF/resolve/main/Synatra-7B-v0.3-RP.Q4_K_S.gguf) | Q4_K_S | 4.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Synatra-7B-v0.3-RP-GGUF/resolve/main/Synatra-7B-v0.3-RP.Q4_K_M.gguf) | Q4_K_M | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Synatra-7B-v0.3-RP-GGUF/resolve/main/Synatra-7B-v0.3-RP.Q5_K_S.gguf) | Q5_K_S | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/Synatra-7B-v0.3-RP-GGUF/resolve/main/Synatra-7B-v0.3-RP.Q5_K_M.gguf) | Q5_K_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Synatra-7B-v0.3-RP-GGUF/resolve/main/Synatra-7B-v0.3-RP.Q6_K.gguf) | Q6_K | 6.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Synatra-7B-v0.3-RP-GGUF/resolve/main/Synatra-7B-v0.3-RP.Q8_0.gguf) | Q8_0 | 7.9 | 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
## 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.
<!-- end -->
|
mradermacher/pandafish-7b-GGUF
|
mradermacher
| 2024-05-06T05:20:56Z | 9 | 1 |
transformers
|
[
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"en",
"base_model:ichigoberry/pandafish-7b",
"base_model:quantized:ichigoberry/pandafish-7b",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-03T12:13:43Z |
---
base_model: ichigoberry/pandafish-7b
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/ichigoberry/pandafish-7b
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/pandafish-7b-GGUF/resolve/main/pandafish-7b.Q2_K.gguf) | Q2_K | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-7b-GGUF/resolve/main/pandafish-7b.IQ3_XS.gguf) | IQ3_XS | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-7b-GGUF/resolve/main/pandafish-7b.Q3_K_S.gguf) | Q3_K_S | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-7b-GGUF/resolve/main/pandafish-7b.IQ3_S.gguf) | IQ3_S | 3.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/pandafish-7b-GGUF/resolve/main/pandafish-7b.IQ3_M.gguf) | IQ3_M | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-7b-GGUF/resolve/main/pandafish-7b.Q3_K_M.gguf) | Q3_K_M | 3.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/pandafish-7b-GGUF/resolve/main/pandafish-7b.Q3_K_L.gguf) | Q3_K_L | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-7b-GGUF/resolve/main/pandafish-7b.IQ4_XS.gguf) | IQ4_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-7b-GGUF/resolve/main/pandafish-7b.Q4_K_S.gguf) | Q4_K_S | 4.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/pandafish-7b-GGUF/resolve/main/pandafish-7b.Q4_K_M.gguf) | Q4_K_M | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/pandafish-7b-GGUF/resolve/main/pandafish-7b.Q5_K_S.gguf) | Q5_K_S | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-7b-GGUF/resolve/main/pandafish-7b.Q5_K_M.gguf) | Q5_K_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-7b-GGUF/resolve/main/pandafish-7b.Q6_K.gguf) | Q6_K | 6.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/pandafish-7b-GGUF/resolve/main/pandafish-7b.Q8_0.gguf) | Q8_0 | 7.9 | 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
## 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.
<!-- end -->
|
mradermacher/Eurus-7b-sft-GGUF
|
mradermacher
| 2024-05-06T05:20:27Z | 130 | 0 |
transformers
|
[
"transformers",
"gguf",
"reasoning",
"en",
"dataset:openbmb/UltraInteract",
"dataset:stingning/ultrachat",
"dataset:openchat/openchat_sharegpt4_dataset",
"dataset:Open-Orca/OpenOrca",
"base_model:pharaouk/Eurus-7b-sft",
"base_model:quantized:pharaouk/Eurus-7b-sft",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-03T16:05:16Z |
---
base_model: pharaouk/Eurus-7b-sft
datasets:
- openbmb/UltraInteract
- stingning/ultrachat
- openchat/openchat_sharegpt4_dataset
- Open-Orca/OpenOrca
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- reasoning
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/pharaouk/Eurus-7b-sft
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/Eurus-7b-sft-GGUF/resolve/main/Eurus-7b-sft.Q2_K.gguf) | Q2_K | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Eurus-7b-sft-GGUF/resolve/main/Eurus-7b-sft.IQ3_XS.gguf) | IQ3_XS | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Eurus-7b-sft-GGUF/resolve/main/Eurus-7b-sft.Q3_K_S.gguf) | Q3_K_S | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Eurus-7b-sft-GGUF/resolve/main/Eurus-7b-sft.IQ3_S.gguf) | IQ3_S | 3.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Eurus-7b-sft-GGUF/resolve/main/Eurus-7b-sft.IQ3_M.gguf) | IQ3_M | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/Eurus-7b-sft-GGUF/resolve/main/Eurus-7b-sft.Q3_K_M.gguf) | Q3_K_M | 3.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Eurus-7b-sft-GGUF/resolve/main/Eurus-7b-sft.Q3_K_L.gguf) | Q3_K_L | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/Eurus-7b-sft-GGUF/resolve/main/Eurus-7b-sft.IQ4_XS.gguf) | IQ4_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Eurus-7b-sft-GGUF/resolve/main/Eurus-7b-sft.Q4_K_S.gguf) | Q4_K_S | 4.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Eurus-7b-sft-GGUF/resolve/main/Eurus-7b-sft.Q4_K_M.gguf) | Q4_K_M | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Eurus-7b-sft-GGUF/resolve/main/Eurus-7b-sft.Q5_K_S.gguf) | Q5_K_S | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/Eurus-7b-sft-GGUF/resolve/main/Eurus-7b-sft.Q5_K_M.gguf) | Q5_K_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Eurus-7b-sft-GGUF/resolve/main/Eurus-7b-sft.Q6_K.gguf) | Q6_K | 6.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Eurus-7b-sft-GGUF/resolve/main/Eurus-7b-sft.Q8_0.gguf) | Q8_0 | 7.9 | 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
## 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.
<!-- end -->
|
mradermacher/HeatherSpell-7b-GGUF
|
mradermacher
| 2024-05-06T05:20:25Z | 34 | 0 |
transformers
|
[
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"yam-peleg/Experiment26-7B",
"Kukedlc/NeuralExperiment-7b-MagicCoder-v7.5",
"en",
"base_model:MysticFoxMagic/HeatherSpell-7b",
"base_model:quantized:MysticFoxMagic/HeatherSpell-7b",
"endpoints_compatible",
"region:us"
] | null | 2024-04-03T16:44:58Z |
---
base_model: MysticFoxMagic/HeatherSpell-7b
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- yam-peleg/Experiment26-7B
- Kukedlc/NeuralExperiment-7b-MagicCoder-v7.5
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/MysticFoxMagic/HeatherSpell-7b
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/HeatherSpell-7b-GGUF/resolve/main/HeatherSpell-7b.Q2_K.gguf) | Q2_K | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/HeatherSpell-7b-GGUF/resolve/main/HeatherSpell-7b.IQ3_XS.gguf) | IQ3_XS | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/HeatherSpell-7b-GGUF/resolve/main/HeatherSpell-7b.Q3_K_S.gguf) | Q3_K_S | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/HeatherSpell-7b-GGUF/resolve/main/HeatherSpell-7b.IQ3_S.gguf) | IQ3_S | 3.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/HeatherSpell-7b-GGUF/resolve/main/HeatherSpell-7b.IQ3_M.gguf) | IQ3_M | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/HeatherSpell-7b-GGUF/resolve/main/HeatherSpell-7b.Q3_K_M.gguf) | Q3_K_M | 3.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/HeatherSpell-7b-GGUF/resolve/main/HeatherSpell-7b.Q3_K_L.gguf) | Q3_K_L | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/HeatherSpell-7b-GGUF/resolve/main/HeatherSpell-7b.IQ4_XS.gguf) | IQ4_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/HeatherSpell-7b-GGUF/resolve/main/HeatherSpell-7b.Q4_K_S.gguf) | Q4_K_S | 4.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/HeatherSpell-7b-GGUF/resolve/main/HeatherSpell-7b.Q4_K_M.gguf) | Q4_K_M | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/HeatherSpell-7b-GGUF/resolve/main/HeatherSpell-7b.Q5_K_S.gguf) | Q5_K_S | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/HeatherSpell-7b-GGUF/resolve/main/HeatherSpell-7b.Q5_K_M.gguf) | Q5_K_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/HeatherSpell-7b-GGUF/resolve/main/HeatherSpell-7b.Q6_K.gguf) | Q6_K | 6.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/HeatherSpell-7b-GGUF/resolve/main/HeatherSpell-7b.Q8_0.gguf) | Q8_0 | 7.9 | 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
## 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.
<!-- end -->
|
mradermacher/KittyNyanster-v1-GGUF
|
mradermacher
| 2024-05-06T05:20:17Z | 191 | 2 |
transformers
|
[
"transformers",
"gguf",
"roleplay",
"chat",
"mistral",
"en",
"base_model:arlineka/KittyNyanster-v1",
"base_model:quantized:arlineka/KittyNyanster-v1",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-03T18:30:14Z |
---
base_model: arlineka/KittyNyanster-v1
language:
- en
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
tags:
- roleplay
- chat
- mistral
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/arlineka/KittyNyanster-v1
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/KittyNyanster-v1-GGUF/resolve/main/KittyNyanster-v1.Q2_K.gguf) | Q2_K | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/KittyNyanster-v1-GGUF/resolve/main/KittyNyanster-v1.IQ3_XS.gguf) | IQ3_XS | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/KittyNyanster-v1-GGUF/resolve/main/KittyNyanster-v1.Q3_K_S.gguf) | Q3_K_S | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/KittyNyanster-v1-GGUF/resolve/main/KittyNyanster-v1.IQ3_S.gguf) | IQ3_S | 3.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/KittyNyanster-v1-GGUF/resolve/main/KittyNyanster-v1.IQ3_M.gguf) | IQ3_M | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/KittyNyanster-v1-GGUF/resolve/main/KittyNyanster-v1.Q3_K_M.gguf) | Q3_K_M | 3.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/KittyNyanster-v1-GGUF/resolve/main/KittyNyanster-v1.Q3_K_L.gguf) | Q3_K_L | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/KittyNyanster-v1-GGUF/resolve/main/KittyNyanster-v1.IQ4_XS.gguf) | IQ4_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/KittyNyanster-v1-GGUF/resolve/main/KittyNyanster-v1.Q4_K_S.gguf) | Q4_K_S | 4.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/KittyNyanster-v1-GGUF/resolve/main/KittyNyanster-v1.Q4_K_M.gguf) | Q4_K_M | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/KittyNyanster-v1-GGUF/resolve/main/KittyNyanster-v1.Q5_K_S.gguf) | Q5_K_S | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/KittyNyanster-v1-GGUF/resolve/main/KittyNyanster-v1.Q5_K_M.gguf) | Q5_K_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/KittyNyanster-v1-GGUF/resolve/main/KittyNyanster-v1.Q6_K.gguf) | Q6_K | 6.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/KittyNyanster-v1-GGUF/resolve/main/KittyNyanster-v1.Q8_0.gguf) | Q8_0 | 7.9 | 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
## 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.
<!-- end -->
|
mradermacher/mistral-7b-medical-assistance-GGUF
|
mradermacher
| 2024-05-06T05:20:12Z | 17 | 1 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"sft",
"en",
"base_model:Hdhsjfjdsj/mistral-7b-medical-assistance",
"base_model:quantized:Hdhsjfjdsj/mistral-7b-medical-assistance",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-03T18:55:27Z |
---
base_model: Hdhsjfjdsj/mistral-7b-medical-assistance
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
- sft
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Hdhsjfjdsj/mistral-7b-medical-assistance
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/mistral-7b-medical-assistance-GGUF/resolve/main/mistral-7b-medical-assistance.Q2_K.gguf) | Q2_K | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/mistral-7b-medical-assistance-GGUF/resolve/main/mistral-7b-medical-assistance.IQ3_XS.gguf) | IQ3_XS | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/mistral-7b-medical-assistance-GGUF/resolve/main/mistral-7b-medical-assistance.Q3_K_S.gguf) | Q3_K_S | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/mistral-7b-medical-assistance-GGUF/resolve/main/mistral-7b-medical-assistance.IQ3_S.gguf) | IQ3_S | 3.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/mistral-7b-medical-assistance-GGUF/resolve/main/mistral-7b-medical-assistance.IQ3_M.gguf) | IQ3_M | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/mistral-7b-medical-assistance-GGUF/resolve/main/mistral-7b-medical-assistance.Q3_K_M.gguf) | Q3_K_M | 3.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/mistral-7b-medical-assistance-GGUF/resolve/main/mistral-7b-medical-assistance.Q3_K_L.gguf) | Q3_K_L | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/mistral-7b-medical-assistance-GGUF/resolve/main/mistral-7b-medical-assistance.IQ4_XS.gguf) | IQ4_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/mistral-7b-medical-assistance-GGUF/resolve/main/mistral-7b-medical-assistance.Q4_K_S.gguf) | Q4_K_S | 4.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/mistral-7b-medical-assistance-GGUF/resolve/main/mistral-7b-medical-assistance.Q4_K_M.gguf) | Q4_K_M | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/mistral-7b-medical-assistance-GGUF/resolve/main/mistral-7b-medical-assistance.Q5_K_S.gguf) | Q5_K_S | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/mistral-7b-medical-assistance-GGUF/resolve/main/mistral-7b-medical-assistance.Q5_K_M.gguf) | Q5_K_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/mistral-7b-medical-assistance-GGUF/resolve/main/mistral-7b-medical-assistance.Q6_K.gguf) | Q6_K | 6.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/mistral-7b-medical-assistance-GGUF/resolve/main/mistral-7b-medical-assistance.Q8_0.gguf) | Q8_0 | 7.9 | 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
## 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.
<!-- end -->
|
mradermacher/pandafish-dt-7b-GGUF
|
mradermacher
| 2024-05-06T05:20:10Z | 64 | 1 |
transformers
|
[
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"CultriX/MergeCeption-7B-v3",
"en",
"base_model:ichigoberry/pandafish-dt-7b",
"base_model:quantized:ichigoberry/pandafish-dt-7b",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-03T19:03:45Z |
---
base_model: ichigoberry/pandafish-dt-7b
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- CultriX/MergeCeption-7B-v3
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/ichigoberry/pandafish-dt-7b
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.Q2_K.gguf) | Q2_K | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.IQ3_XS.gguf) | IQ3_XS | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.Q3_K_S.gguf) | Q3_K_S | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.IQ3_S.gguf) | IQ3_S | 3.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.IQ3_M.gguf) | IQ3_M | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.Q3_K_M.gguf) | Q3_K_M | 3.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.Q3_K_L.gguf) | Q3_K_L | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.IQ4_XS.gguf) | IQ4_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.Q4_K_S.gguf) | Q4_K_S | 4.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.Q4_K_M.gguf) | Q4_K_M | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.Q5_K_S.gguf) | Q5_K_S | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.Q5_K_M.gguf) | Q5_K_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.Q6_K.gguf) | Q6_K | 6.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/pandafish-dt-7b-GGUF/resolve/main/pandafish-dt-7b.Q8_0.gguf) | Q8_0 | 7.9 | 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
## 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.
<!-- end -->
|
mradermacher/KunoichiVerse-7B-GGUF
|
mradermacher
| 2024-05-06T05:19:51Z | 28 | 1 |
transformers
|
[
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"en",
"base_model:Ppoyaa/KunoichiVerse-7B",
"base_model:quantized:Ppoyaa/KunoichiVerse-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-03T21:48:59Z |
---
base_model: Ppoyaa/KunoichiVerse-7B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Ppoyaa/KunoichiVerse-7B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.Q2_K.gguf) | Q2_K | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.IQ3_XS.gguf) | IQ3_XS | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.Q3_K_S.gguf) | Q3_K_S | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.IQ3_S.gguf) | IQ3_S | 3.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.IQ3_M.gguf) | IQ3_M | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.Q3_K_M.gguf) | Q3_K_M | 3.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.Q3_K_L.gguf) | Q3_K_L | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.IQ4_XS.gguf) | IQ4_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.Q4_K_S.gguf) | Q4_K_S | 4.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.Q4_K_M.gguf) | Q4_K_M | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.Q5_K_S.gguf) | Q5_K_S | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.Q5_K_M.gguf) | Q5_K_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.Q6_K.gguf) | Q6_K | 6.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/KunoichiVerse-7B-GGUF/resolve/main/KunoichiVerse-7B.Q8_0.gguf) | Q8_0 | 7.9 | 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
## 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.
<!-- end -->
|
mradermacher/bagel-20b-v04-GGUF
|
mradermacher
| 2024-05-06T05:19:46Z | 65 | 1 |
transformers
|
[
"transformers",
"gguf",
"en",
"dataset:ai2_arc",
"dataset:allenai/ultrafeedback_binarized_cleaned",
"dataset:argilla/distilabel-intel-orca-dpo-pairs",
"dataset:jondurbin/airoboros-3.2",
"dataset:codeparrot/apps",
"dataset:facebook/belebele",
"dataset:bluemoon-fandom-1-1-rp-cleaned",
"dataset:boolq",
"dataset:camel-ai/biology",
"dataset:camel-ai/chemistry",
"dataset:camel-ai/math",
"dataset:camel-ai/physics",
"dataset:jondurbin/contextual-dpo-v0.1",
"dataset:jondurbin/gutenberg-dpo-v0.1",
"dataset:jondurbin/py-dpo-v0.1",
"dataset:jondurbin/truthy-dpo-v0.1",
"dataset:LDJnr/Capybara",
"dataset:jondurbin/cinematika-v0.1",
"dataset:WizardLM/WizardLM_evol_instruct_70k",
"dataset:glaiveai/glaive-function-calling-v2",
"dataset:grimulkan/LimaRP-augmented",
"dataset:lmsys/lmsys-chat-1m",
"dataset:ParisNeo/lollms_aware_dataset",
"dataset:TIGER-Lab/MathInstruct",
"dataset:Muennighoff/natural-instructions",
"dataset:openbookqa",
"dataset:kingbri/PIPPA-shareGPT",
"dataset:piqa",
"dataset:Vezora/Tested-22k-Python-Alpaca",
"dataset:ropes",
"dataset:cakiki/rosetta-code",
"dataset:Open-Orca/SlimOrca",
"dataset:b-mc2/sql-create-context",
"dataset:squad_v2",
"dataset:mattpscott/airoboros-summarization",
"dataset:migtissera/Synthia-v1.3",
"dataset:unalignment/toxic-dpo-v0.2",
"dataset:WhiteRabbitNeo/WRN-Chapter-1",
"dataset:WhiteRabbitNeo/WRN-Chapter-2",
"dataset:winogrande",
"base_model:jondurbin/bagel-20b-v04",
"base_model:quantized:jondurbin/bagel-20b-v04",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-03T22:08:35Z |
---
base_model: jondurbin/bagel-20b-v04
datasets:
- ai2_arc
- allenai/ultrafeedback_binarized_cleaned
- argilla/distilabel-intel-orca-dpo-pairs
- jondurbin/airoboros-3.2
- codeparrot/apps
- facebook/belebele
- bluemoon-fandom-1-1-rp-cleaned
- boolq
- camel-ai/biology
- camel-ai/chemistry
- camel-ai/math
- camel-ai/physics
- jondurbin/contextual-dpo-v0.1
- jondurbin/gutenberg-dpo-v0.1
- jondurbin/py-dpo-v0.1
- jondurbin/truthy-dpo-v0.1
- LDJnr/Capybara
- jondurbin/cinematika-v0.1
- WizardLM/WizardLM_evol_instruct_70k
- glaiveai/glaive-function-calling-v2
- jondurbin/gutenberg-dpo-v0.1
- grimulkan/LimaRP-augmented
- lmsys/lmsys-chat-1m
- ParisNeo/lollms_aware_dataset
- TIGER-Lab/MathInstruct
- Muennighoff/natural-instructions
- openbookqa
- kingbri/PIPPA-shareGPT
- piqa
- Vezora/Tested-22k-Python-Alpaca
- ropes
- cakiki/rosetta-code
- Open-Orca/SlimOrca
- b-mc2/sql-create-context
- squad_v2
- mattpscott/airoboros-summarization
- migtissera/Synthia-v1.3
- unalignment/toxic-dpo-v0.2
- WhiteRabbitNeo/WRN-Chapter-1
- WhiteRabbitNeo/WRN-Chapter-2
- winogrande
language:
- en
library_name: transformers
license: other
license_link: https://huggingface.co/internlm/internlm2-20b#open-source-license
license_name: internlm2-20b
quantized_by: mradermacher
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/jondurbin/bagel-20b-v04
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/bagel-20b-v04-GGUF/resolve/main/bagel-20b-v04.Q2_K.gguf) | Q2_K | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/bagel-20b-v04-GGUF/resolve/main/bagel-20b-v04.IQ3_XS.gguf) | IQ3_XS | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/bagel-20b-v04-GGUF/resolve/main/bagel-20b-v04.Q3_K_S.gguf) | Q3_K_S | 9.5 | |
| [GGUF](https://huggingface.co/mradermacher/bagel-20b-v04-GGUF/resolve/main/bagel-20b-v04.IQ3_S.gguf) | IQ3_S | 9.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/bagel-20b-v04-GGUF/resolve/main/bagel-20b-v04.IQ3_M.gguf) | IQ3_M | 9.9 | |
| [GGUF](https://huggingface.co/mradermacher/bagel-20b-v04-GGUF/resolve/main/bagel-20b-v04.Q3_K_M.gguf) | Q3_K_M | 10.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/bagel-20b-v04-GGUF/resolve/main/bagel-20b-v04.Q3_K_L.gguf) | Q3_K_L | 11.3 | |
| [GGUF](https://huggingface.co/mradermacher/bagel-20b-v04-GGUF/resolve/main/bagel-20b-v04.IQ4_XS.gguf) | IQ4_XS | 11.6 | |
| [GGUF](https://huggingface.co/mradermacher/bagel-20b-v04-GGUF/resolve/main/bagel-20b-v04.Q4_K_S.gguf) | Q4_K_S | 12.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/bagel-20b-v04-GGUF/resolve/main/bagel-20b-v04.Q4_K_M.gguf) | Q4_K_M | 12.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/bagel-20b-v04-GGUF/resolve/main/bagel-20b-v04.Q5_K_S.gguf) | Q5_K_S | 14.5 | |
| [GGUF](https://huggingface.co/mradermacher/bagel-20b-v04-GGUF/resolve/main/bagel-20b-v04.Q5_K_M.gguf) | Q5_K_M | 14.8 | |
| [GGUF](https://huggingface.co/mradermacher/bagel-20b-v04-GGUF/resolve/main/bagel-20b-v04.Q6_K.gguf) | Q6_K | 17.1 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/bagel-20b-v04-GGUF/resolve/main/bagel-20b-v04.Q8_0.gguf) | Q8_0 | 21.7 | 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
## 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.
<!-- end -->
|
Wespeaker/wespeaker-cnceleb-resnet34-LM
|
Wespeaker
| 2024-05-06T05:19:40Z | 4 | 3 | null |
[
"onnx",
"license:apache-2.0",
"region:us"
] | null | 2024-05-06T05:15:14Z |
---
license: apache-2.0
---
|
mradermacher/StarMonarch-7B-GGUF
|
mradermacher
| 2024-05-06T05:19:34Z | 71 | 0 |
transformers
|
[
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"en",
"base_model:Ppoyaa/StarMonarch-7B",
"base_model:quantized:Ppoyaa/StarMonarch-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-04T00:13:47Z |
---
base_model: Ppoyaa/StarMonarch-7B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Ppoyaa/StarMonarch-7B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.Q2_K.gguf) | Q2_K | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.IQ3_XS.gguf) | IQ3_XS | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.Q3_K_S.gguf) | Q3_K_S | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.IQ3_S.gguf) | IQ3_S | 3.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.IQ3_M.gguf) | IQ3_M | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.Q3_K_M.gguf) | Q3_K_M | 3.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.Q3_K_L.gguf) | Q3_K_L | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.IQ4_XS.gguf) | IQ4_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.Q4_K_S.gguf) | Q4_K_S | 4.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.Q4_K_M.gguf) | Q4_K_M | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.Q5_K_S.gguf) | Q5_K_S | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.Q5_K_M.gguf) | Q5_K_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.Q6_K.gguf) | Q6_K | 6.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/StarMonarch-7B-GGUF/resolve/main/StarMonarch-7B.Q8_0.gguf) | Q8_0 | 7.9 | 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
## 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.
<!-- end -->
|
mradermacher/Qwen1.5-7B-Translator-GGUF
|
mradermacher
| 2024-05-06T05:19:32Z | 5 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:DeyangKong/Qwen1.5-7B-Translator",
"base_model:quantized:DeyangKong/Qwen1.5-7B-Translator",
"license:gpl-3.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-04T00:24:50Z |
---
base_model: DeyangKong/Qwen1.5-7B-Translator
language:
- en
library_name: transformers
license: gpl-3.0
quantized_by: mradermacher
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/DeyangKong/Qwen1.5-7B-Translator
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/Qwen1.5-7B-Translator-GGUF/resolve/main/Qwen1.5-7B-Translator.Q2_K.gguf) | Q2_K | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-7B-Translator-GGUF/resolve/main/Qwen1.5-7B-Translator.IQ3_XS.gguf) | IQ3_XS | 4.3 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-7B-Translator-GGUF/resolve/main/Qwen1.5-7B-Translator.IQ3_S.gguf) | IQ3_S | 4.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-7B-Translator-GGUF/resolve/main/Qwen1.5-7B-Translator.Q3_K_S.gguf) | Q3_K_S | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-7B-Translator-GGUF/resolve/main/Qwen1.5-7B-Translator.IQ3_M.gguf) | IQ3_M | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-7B-Translator-GGUF/resolve/main/Qwen1.5-7B-Translator.Q3_K_M.gguf) | Q3_K_M | 4.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-7B-Translator-GGUF/resolve/main/Qwen1.5-7B-Translator.Q3_K_L.gguf) | Q3_K_L | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-7B-Translator-GGUF/resolve/main/Qwen1.5-7B-Translator.IQ4_XS.gguf) | IQ4_XS | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-7B-Translator-GGUF/resolve/main/Qwen1.5-7B-Translator.Q4_K_S.gguf) | Q4_K_S | 5.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-7B-Translator-GGUF/resolve/main/Qwen1.5-7B-Translator.Q4_K_M.gguf) | Q4_K_M | 5.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-7B-Translator-GGUF/resolve/main/Qwen1.5-7B-Translator.Q5_K_S.gguf) | Q5_K_S | 6.2 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-7B-Translator-GGUF/resolve/main/Qwen1.5-7B-Translator.Q5_K_M.gguf) | Q5_K_M | 6.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-7B-Translator-GGUF/resolve/main/Qwen1.5-7B-Translator.Q6_K.gguf) | Q6_K | 7.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen1.5-7B-Translator-GGUF/resolve/main/Qwen1.5-7B-Translator.Q8_0.gguf) | Q8_0 | 8.9 | 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
## 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.
<!-- end -->
|
mradermacher/Athena-v4-i1-GGUF
|
mradermacher
| 2024-05-06T05:19:17Z | 117 | 1 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:IkariDev/Athena-v4",
"base_model:quantized:IkariDev/Athena-v4",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-04T01:14:19Z |
---
base_model: IkariDev/Athena-v4
language:
- en
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/IkariDev/Athena-v4
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Athena-v4-GGUF
## 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/Athena-v4-i1-GGUF/resolve/main/Athena-v4.i1-IQ1_S.gguf) | i1-IQ1_S | 3.2 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Athena-v4-i1-GGUF/resolve/main/Athena-v4.i1-IQ1_M.gguf) | i1-IQ1_M | 3.5 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Athena-v4-i1-GGUF/resolve/main/Athena-v4.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Athena-v4-i1-GGUF/resolve/main/Athena-v4.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Athena-v4-i1-GGUF/resolve/main/Athena-v4.i1-IQ2_S.gguf) | i1-IQ2_S | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Athena-v4-i1-GGUF/resolve/main/Athena-v4.i1-IQ2_M.gguf) | i1-IQ2_M | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/Athena-v4-i1-GGUF/resolve/main/Athena-v4.i1-Q2_K.gguf) | i1-Q2_K | 5.1 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Athena-v4-i1-GGUF/resolve/main/Athena-v4.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Athena-v4-i1-GGUF/resolve/main/Athena-v4.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Athena-v4-i1-GGUF/resolve/main/Athena-v4.i1-IQ3_S.gguf) | i1-IQ3_S | 6.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Athena-v4-i1-GGUF/resolve/main/Athena-v4.i1-Q3_K_S.gguf) | i1-Q3_K_S | 6.0 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Athena-v4-i1-GGUF/resolve/main/Athena-v4.i1-IQ3_M.gguf) | i1-IQ3_M | 6.3 | |
| [GGUF](https://huggingface.co/mradermacher/Athena-v4-i1-GGUF/resolve/main/Athena-v4.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.6 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Athena-v4-i1-GGUF/resolve/main/Athena-v4.i1-Q3_K_L.gguf) | i1-Q3_K_L | 7.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Athena-v4-i1-GGUF/resolve/main/Athena-v4.i1-IQ4_XS.gguf) | i1-IQ4_XS | 7.3 | |
| [GGUF](https://huggingface.co/mradermacher/Athena-v4-i1-GGUF/resolve/main/Athena-v4.i1-Q4_0.gguf) | i1-Q4_0 | 7.7 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Athena-v4-i1-GGUF/resolve/main/Athena-v4.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.7 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Athena-v4-i1-GGUF/resolve/main/Athena-v4.i1-Q4_K_M.gguf) | i1-Q4_K_M | 8.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Athena-v4-i1-GGUF/resolve/main/Athena-v4.i1-Q5_K_S.gguf) | i1-Q5_K_S | 9.3 | |
| [GGUF](https://huggingface.co/mradermacher/Athena-v4-i1-GGUF/resolve/main/Athena-v4.i1-Q5_K_M.gguf) | i1-Q5_K_M | 9.5 | |
| [GGUF](https://huggingface.co/mradermacher/Athena-v4-i1-GGUF/resolve/main/Athena-v4.i1-Q6_K.gguf) | i1-Q6_K | 11.0 | 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
## 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.
<!-- end -->
|
mradermacher/AuroraRP-8x7B-GGUF
|
mradermacher
| 2024-05-06T05:18:57Z | 26 | 1 |
transformers
|
[
"transformers",
"gguf",
"roleplay",
"rp",
"mergekit",
"merge",
"en",
"endpoints_compatible",
"region:us"
] | null | 2024-04-04T04:00:24Z |
---
base_model: Fredithefish/AuroraRP-8x7B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- roleplay
- rp
- mergekit
- merge
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Fredithefish/AuroraRP-8x7B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/AuroraRP-8x7B-i1-GGUF
## 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/AuroraRP-8x7B-GGUF/resolve/main/AuroraRP-8x7B.Q2_K.gguf) | Q2_K | 17.6 | |
| [GGUF](https://huggingface.co/mradermacher/AuroraRP-8x7B-GGUF/resolve/main/AuroraRP-8x7B.IQ3_XS.gguf) | IQ3_XS | 19.5 | |
| [GGUF](https://huggingface.co/mradermacher/AuroraRP-8x7B-GGUF/resolve/main/AuroraRP-8x7B.IQ3_S.gguf) | IQ3_S | 20.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/AuroraRP-8x7B-GGUF/resolve/main/AuroraRP-8x7B.Q3_K_S.gguf) | Q3_K_S | 20.7 | |
| [GGUF](https://huggingface.co/mradermacher/AuroraRP-8x7B-GGUF/resolve/main/AuroraRP-8x7B.IQ3_M.gguf) | IQ3_M | 21.7 | |
| [GGUF](https://huggingface.co/mradermacher/AuroraRP-8x7B-GGUF/resolve/main/AuroraRP-8x7B.Q3_K_M.gguf) | Q3_K_M | 22.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/AuroraRP-8x7B-GGUF/resolve/main/AuroraRP-8x7B.Q3_K_L.gguf) | Q3_K_L | 24.4 | |
| [GGUF](https://huggingface.co/mradermacher/AuroraRP-8x7B-GGUF/resolve/main/AuroraRP-8x7B.IQ4_XS.gguf) | IQ4_XS | 25.6 | |
| [GGUF](https://huggingface.co/mradermacher/AuroraRP-8x7B-GGUF/resolve/main/AuroraRP-8x7B.Q4_K_S.gguf) | Q4_K_S | 27.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/AuroraRP-8x7B-GGUF/resolve/main/AuroraRP-8x7B.Q4_K_M.gguf) | Q4_K_M | 28.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/AuroraRP-8x7B-GGUF/resolve/main/AuroraRP-8x7B.Q5_K_S.gguf) | Q5_K_S | 32.5 | |
| [GGUF](https://huggingface.co/mradermacher/AuroraRP-8x7B-GGUF/resolve/main/AuroraRP-8x7B.Q5_K_M.gguf) | Q5_K_M | 33.5 | |
| [GGUF](https://huggingface.co/mradermacher/AuroraRP-8x7B-GGUF/resolve/main/AuroraRP-8x7B.Q6_K.gguf) | Q6_K | 38.6 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/AuroraRP-8x7B-GGUF/resolve/main/AuroraRP-8x7B.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/AuroraRP-8x7B-GGUF/resolve/main/AuroraRP-8x7B.Q8_0.gguf.part2of2) | Q8_0 | 49.8 | 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
## 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.
<!-- end -->
|
mradermacher/MeliodasT3qm7-7B-GGUF
|
mradermacher
| 2024-05-06T05:18:47Z | 11 | 0 |
transformers
|
[
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"automerger",
"en",
"base_model:automerger/MeliodasT3qm7-7B",
"base_model:quantized:automerger/MeliodasT3qm7-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-04T05:21:38Z |
---
base_model: automerger/MeliodasT3qm7-7B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- automerger
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/automerger/MeliodasT3qm7-7B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/MeliodasT3qm7-7B-GGUF/resolve/main/MeliodasT3qm7-7B.Q2_K.gguf) | Q2_K | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/MeliodasT3qm7-7B-GGUF/resolve/main/MeliodasT3qm7-7B.IQ3_XS.gguf) | IQ3_XS | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/MeliodasT3qm7-7B-GGUF/resolve/main/MeliodasT3qm7-7B.Q3_K_S.gguf) | Q3_K_S | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/MeliodasT3qm7-7B-GGUF/resolve/main/MeliodasT3qm7-7B.IQ3_S.gguf) | IQ3_S | 3.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/MeliodasT3qm7-7B-GGUF/resolve/main/MeliodasT3qm7-7B.IQ3_M.gguf) | IQ3_M | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/MeliodasT3qm7-7B-GGUF/resolve/main/MeliodasT3qm7-7B.Q3_K_M.gguf) | Q3_K_M | 3.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/MeliodasT3qm7-7B-GGUF/resolve/main/MeliodasT3qm7-7B.Q3_K_L.gguf) | Q3_K_L | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/MeliodasT3qm7-7B-GGUF/resolve/main/MeliodasT3qm7-7B.IQ4_XS.gguf) | IQ4_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/MeliodasT3qm7-7B-GGUF/resolve/main/MeliodasT3qm7-7B.Q4_K_S.gguf) | Q4_K_S | 4.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MeliodasT3qm7-7B-GGUF/resolve/main/MeliodasT3qm7-7B.Q4_K_M.gguf) | Q4_K_M | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MeliodasT3qm7-7B-GGUF/resolve/main/MeliodasT3qm7-7B.Q5_K_S.gguf) | Q5_K_S | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/MeliodasT3qm7-7B-GGUF/resolve/main/MeliodasT3qm7-7B.Q5_K_M.gguf) | Q5_K_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/MeliodasT3qm7-7B-GGUF/resolve/main/MeliodasT3qm7-7B.Q6_K.gguf) | Q6_K | 6.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/MeliodasT3qm7-7B-GGUF/resolve/main/MeliodasT3qm7-7B.Q8_0.gguf) | Q8_0 | 7.9 | 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
## 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.
<!-- end -->
|
mradermacher/IsenHumourAI-GGUF
|
mradermacher
| 2024-05-06T05:18:38Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"sft",
"en",
"base_model:jberni29/IsenHumourAI",
"base_model:quantized:jberni29/IsenHumourAI",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-04T06:46:50Z |
---
base_model: jberni29/IsenHumourAI
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
- sft
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/jberni29/IsenHumourAI
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/IsenHumourAI-GGUF/resolve/main/IsenHumourAI.Q2_K.gguf) | Q2_K | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/IsenHumourAI-GGUF/resolve/main/IsenHumourAI.IQ3_XS.gguf) | IQ3_XS | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/IsenHumourAI-GGUF/resolve/main/IsenHumourAI.Q3_K_S.gguf) | Q3_K_S | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/IsenHumourAI-GGUF/resolve/main/IsenHumourAI.IQ3_S.gguf) | IQ3_S | 3.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/IsenHumourAI-GGUF/resolve/main/IsenHumourAI.IQ3_M.gguf) | IQ3_M | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/IsenHumourAI-GGUF/resolve/main/IsenHumourAI.Q3_K_M.gguf) | Q3_K_M | 3.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/IsenHumourAI-GGUF/resolve/main/IsenHumourAI.Q3_K_L.gguf) | Q3_K_L | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/IsenHumourAI-GGUF/resolve/main/IsenHumourAI.IQ4_XS.gguf) | IQ4_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/IsenHumourAI-GGUF/resolve/main/IsenHumourAI.Q4_K_S.gguf) | Q4_K_S | 4.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/IsenHumourAI-GGUF/resolve/main/IsenHumourAI.Q4_K_M.gguf) | Q4_K_M | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/IsenHumourAI-GGUF/resolve/main/IsenHumourAI.Q5_K_S.gguf) | Q5_K_S | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/IsenHumourAI-GGUF/resolve/main/IsenHumourAI.Q5_K_M.gguf) | Q5_K_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/IsenHumourAI-GGUF/resolve/main/IsenHumourAI.Q6_K.gguf) | Q6_K | 6.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/IsenHumourAI-GGUF/resolve/main/IsenHumourAI.Q8_0.gguf) | Q8_0 | 7.9 | 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
## 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.
<!-- end -->
|
mradermacher/MistarlingMaid-2x7B-base-GGUF
|
mradermacher
| 2024-05-06T05:18:27Z | 74 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:dawn17/MistarlingMaid-2x7B-base",
"base_model:quantized:dawn17/MistarlingMaid-2x7B-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-04T08:20:09Z |
---
base_model: dawn17/MistarlingMaid-2x7B-base
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/dawn17/MistarlingMaid-2x7B-base
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.Q2_K.gguf) | Q2_K | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.IQ3_XS.gguf) | IQ3_XS | 5.6 | |
| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.Q3_K_S.gguf) | Q3_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.IQ3_S.gguf) | IQ3_S | 5.9 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.IQ3_M.gguf) | IQ3_M | 6.0 | |
| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.Q3_K_M.gguf) | Q3_K_M | 6.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.Q3_K_L.gguf) | Q3_K_L | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.IQ4_XS.gguf) | IQ4_XS | 7.2 | |
| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.Q4_K_S.gguf) | Q4_K_S | 7.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.Q5_K_S.gguf) | Q5_K_S | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.Q5_K_M.gguf) | Q5_K_M | 9.4 | |
| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.Q6_K.gguf) | Q6_K | 10.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/MistarlingMaid-2x7B-base-GGUF/resolve/main/MistarlingMaid-2x7B-base.Q8_0.gguf) | Q8_0 | 13.9 | 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
## 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.
<!-- end -->
|
mradermacher/UNAversal-8x7B-v1beta-i1-GGUF
|
mradermacher
| 2024-05-06T05:18:09Z | 61 | 1 |
transformers
|
[
"transformers",
"gguf",
"UNA",
"juanako",
"mixtral",
"MoE",
"en",
"base_model:fblgit/UNAversal-8x7B-v1beta",
"base_model:quantized:fblgit/UNAversal-8x7B-v1beta",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-04T09:42:55Z |
---
base_model: fblgit/UNAversal-8x7B-v1beta
language:
- en
library_name: transformers
license: cc-by-nc-sa-4.0
quantized_by: mradermacher
tags:
- UNA
- juanako
- mixtral
- MoE
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/fblgit/UNAversal-8x7B-v1beta
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-GGUF
## 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/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-IQ1_S.gguf) | i1-IQ1_S | 10.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-IQ1_M.gguf) | i1-IQ1_M | 11.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 12.8 | |
| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-IQ2_XS.gguf) | i1-IQ2_XS | 14.2 | |
| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-IQ2_S.gguf) | i1-IQ2_S | 14.4 | |
| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-IQ2_M.gguf) | i1-IQ2_M | 15.8 | |
| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-Q2_K.gguf) | i1-Q2_K | 17.6 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 18.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-IQ3_XS.gguf) | i1-IQ3_XS | 19.5 | |
| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-IQ3_S.gguf) | i1-IQ3_S | 20.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-Q3_K_S.gguf) | i1-Q3_K_S | 20.7 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-IQ3_M.gguf) | i1-IQ3_M | 21.7 | |
| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-Q3_K_M.gguf) | i1-Q3_K_M | 22.8 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-Q3_K_L.gguf) | i1-Q3_K_L | 24.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-IQ4_XS.gguf) | i1-IQ4_XS | 25.3 | |
| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-Q4_0.gguf) | i1-Q4_0 | 26.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-Q4_K_S.gguf) | i1-Q4_K_S | 27.0 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-Q4_K_M.gguf) | i1-Q4_K_M | 28.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-Q5_K_S.gguf) | i1-Q5_K_S | 32.5 | |
| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-Q5_K_M.gguf) | i1-Q5_K_M | 33.5 | |
| [GGUF](https://huggingface.co/mradermacher/UNAversal-8x7B-v1beta-i1-GGUF/resolve/main/UNAversal-8x7B-v1beta.i1-Q6_K.gguf) | i1-Q6_K | 38.6 | 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](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF
|
mradermacher
| 2024-05-06T05:17:59Z | 1 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:ddh0/Mistral-10.7B-Instruct-v0.2",
"base_model:quantized:ddh0/Mistral-10.7B-Instruct-v0.2",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-04T12:08:09Z |
---
base_model: ddh0/Mistral-10.7B-Instruct-v0.2
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/ddh0/Mistral-10.7B-Instruct-v0.2
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.Q2_K.gguf) | Q2_K | 4.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.IQ3_XS.gguf) | IQ3_XS | 4.7 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.Q3_K_S.gguf) | Q3_K_S | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.IQ3_S.gguf) | IQ3_S | 4.9 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.IQ3_M.gguf) | IQ3_M | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.Q3_K_M.gguf) | Q3_K_M | 5.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.Q3_K_L.gguf) | Q3_K_L | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.IQ4_XS.gguf) | IQ4_XS | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.Q4_K_S.gguf) | Q4_K_S | 6.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.Q4_K_M.gguf) | Q4_K_M | 6.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.Q5_K_S.gguf) | Q5_K_S | 7.7 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.Q5_K_M.gguf) | Q5_K_M | 7.9 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.Q6_K.gguf) | Q6_K | 9.1 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-10.7B-Instruct-v0.2-GGUF/resolve/main/Mistral-10.7B-Instruct-v0.2.Q8_0.gguf) | Q8_0 | 11.6 | 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
## 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.
<!-- end -->
|
mradermacher/MermaidMixtral-2x6.5b-GGUF
|
mradermacher
| 2024-05-06T05:17:28Z | 90 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:TroyDoesAI/MermaidMixtral-2x6.5b",
"base_model:quantized:TroyDoesAI/MermaidMixtral-2x6.5b",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-04T15:36:55Z |
---
base_model: TroyDoesAI/MermaidMixtral-2x6.5b
language:
- en
library_name: transformers
license: cc-by-4.0
quantized_by: mradermacher
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/TroyDoesAI/MermaidMixtral-2x6.5b
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/MermaidMixtral-2x6.5b-GGUF/resolve/main/MermaidMixtral-2x6.5b.Q2_K.gguf) | Q2_K | 4.7 | |
| [GGUF](https://huggingface.co/mradermacher/MermaidMixtral-2x6.5b-GGUF/resolve/main/MermaidMixtral-2x6.5b.IQ3_XS.gguf) | IQ3_XS | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/MermaidMixtral-2x6.5b-GGUF/resolve/main/MermaidMixtral-2x6.5b.Q3_K_S.gguf) | Q3_K_S | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/MermaidMixtral-2x6.5b-GGUF/resolve/main/MermaidMixtral-2x6.5b.IQ3_S.gguf) | IQ3_S | 5.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/MermaidMixtral-2x6.5b-GGUF/resolve/main/MermaidMixtral-2x6.5b.IQ3_M.gguf) | IQ3_M | 5.6 | |
| [GGUF](https://huggingface.co/mradermacher/MermaidMixtral-2x6.5b-GGUF/resolve/main/MermaidMixtral-2x6.5b.Q3_K_M.gguf) | Q3_K_M | 6.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/MermaidMixtral-2x6.5b-GGUF/resolve/main/MermaidMixtral-2x6.5b.Q3_K_L.gguf) | Q3_K_L | 6.6 | |
| [GGUF](https://huggingface.co/mradermacher/MermaidMixtral-2x6.5b-GGUF/resolve/main/MermaidMixtral-2x6.5b.IQ4_XS.gguf) | IQ4_XS | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/MermaidMixtral-2x6.5b-GGUF/resolve/main/MermaidMixtral-2x6.5b.Q4_K_S.gguf) | Q4_K_S | 7.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MermaidMixtral-2x6.5b-GGUF/resolve/main/MermaidMixtral-2x6.5b.Q4_K_M.gguf) | Q4_K_M | 7.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MermaidMixtral-2x6.5b-GGUF/resolve/main/MermaidMixtral-2x6.5b.Q5_K_S.gguf) | Q5_K_S | 8.6 | |
| [GGUF](https://huggingface.co/mradermacher/MermaidMixtral-2x6.5b-GGUF/resolve/main/MermaidMixtral-2x6.5b.Q5_K_M.gguf) | Q5_K_M | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/MermaidMixtral-2x6.5b-GGUF/resolve/main/MermaidMixtral-2x6.5b.Q6_K.gguf) | Q6_K | 10.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/MermaidMixtral-2x6.5b-GGUF/resolve/main/MermaidMixtral-2x6.5b.Q8_0.gguf) | Q8_0 | 13.1 | 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
## 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.
<!-- end -->
|
mradermacher/Twizzler-7B-GGUF
|
mradermacher
| 2024-05-06T05:17:26Z | 69 | 1 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:son-of-man/Twizzler-7B",
"base_model:quantized:son-of-man/Twizzler-7B",
"endpoints_compatible",
"region:us"
] | null | 2024-04-04T15:44:57Z |
---
base_model: son-of-man/Twizzler-7B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/son-of-man/Twizzler-7B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/Twizzler-7B-GGUF/resolve/main/Twizzler-7B.Q2_K.gguf) | Q2_K | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Twizzler-7B-GGUF/resolve/main/Twizzler-7B.IQ3_XS.gguf) | IQ3_XS | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Twizzler-7B-GGUF/resolve/main/Twizzler-7B.Q3_K_S.gguf) | Q3_K_S | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Twizzler-7B-GGUF/resolve/main/Twizzler-7B.IQ3_S.gguf) | IQ3_S | 3.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Twizzler-7B-GGUF/resolve/main/Twizzler-7B.IQ3_M.gguf) | IQ3_M | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/Twizzler-7B-GGUF/resolve/main/Twizzler-7B.Q3_K_M.gguf) | Q3_K_M | 3.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Twizzler-7B-GGUF/resolve/main/Twizzler-7B.Q3_K_L.gguf) | Q3_K_L | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/Twizzler-7B-GGUF/resolve/main/Twizzler-7B.IQ4_XS.gguf) | IQ4_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Twizzler-7B-GGUF/resolve/main/Twizzler-7B.Q4_K_S.gguf) | Q4_K_S | 4.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Twizzler-7B-GGUF/resolve/main/Twizzler-7B.Q4_K_M.gguf) | Q4_K_M | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Twizzler-7B-GGUF/resolve/main/Twizzler-7B.Q5_K_S.gguf) | Q5_K_S | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/Twizzler-7B-GGUF/resolve/main/Twizzler-7B.Q5_K_M.gguf) | Q5_K_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Twizzler-7B-GGUF/resolve/main/Twizzler-7B.Q6_K.gguf) | Q6_K | 6.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Twizzler-7B-GGUF/resolve/main/Twizzler-7B.Q8_0.gguf) | Q8_0 | 7.9 | 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
## 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.
<!-- end -->
|
mradermacher/Mermaid_11.5B-GGUF
|
mradermacher
| 2024-05-06T05:17:13Z | 19 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:TroyDoesAI/Mermaid_11.5B",
"base_model:quantized:TroyDoesAI/Mermaid_11.5B",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-04T19:16:21Z |
---
base_model: TroyDoesAI/Mermaid_11.5B
language:
- en
library_name: transformers
license: cc-by-4.0
quantized_by: mradermacher
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/TroyDoesAI/Mermaid_11.5B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/Mermaid_11.5B-GGUF/resolve/main/Mermaid_11.5B.Q2_K.gguf) | Q2_K | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_11.5B-GGUF/resolve/main/Mermaid_11.5B.IQ3_XS.gguf) | IQ3_XS | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_11.5B-GGUF/resolve/main/Mermaid_11.5B.Q3_K_S.gguf) | Q3_K_S | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_11.5B-GGUF/resolve/main/Mermaid_11.5B.IQ3_S.gguf) | IQ3_S | 5.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_11.5B-GGUF/resolve/main/Mermaid_11.5B.IQ3_M.gguf) | IQ3_M | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_11.5B-GGUF/resolve/main/Mermaid_11.5B.Q3_K_M.gguf) | Q3_K_M | 6.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_11.5B-GGUF/resolve/main/Mermaid_11.5B.Q3_K_L.gguf) | Q3_K_L | 6.6 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_11.5B-GGUF/resolve/main/Mermaid_11.5B.IQ4_XS.gguf) | IQ4_XS | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_11.5B-GGUF/resolve/main/Mermaid_11.5B.Q4_K_S.gguf) | Q4_K_S | 7.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_11.5B-GGUF/resolve/main/Mermaid_11.5B.Q4_K_M.gguf) | Q4_K_M | 7.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_11.5B-GGUF/resolve/main/Mermaid_11.5B.Q5_K_S.gguf) | Q5_K_S | 8.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_11.5B-GGUF/resolve/main/Mermaid_11.5B.Q5_K_M.gguf) | Q5_K_M | 8.6 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_11.5B-GGUF/resolve/main/Mermaid_11.5B.Q6_K.gguf) | Q6_K | 9.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_11.5B-GGUF/resolve/main/Mermaid_11.5B.Q8_0.gguf) | Q8_0 | 12.7 | 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
## 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.
<!-- end -->
|
mradermacher/Swallow-70b-RP-GGUF
|
mradermacher
| 2024-05-06T05:17:10Z | 72 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"ja",
"base_model:nitky/Swallow-70b-RP",
"base_model:quantized:nitky/Swallow-70b-RP",
"license:llama2",
"endpoints_compatible",
"region:us"
] | null | 2024-04-04T20:51:26Z |
---
base_model: nitky/Swallow-70b-RP
language:
- en
- ja
library_name: transformers
license: llama2
model_type: llama
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/nitky/Swallow-70b-RP
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Swallow-70b-RP-i1-GGUF
## 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/Swallow-70b-RP-GGUF/resolve/main/Swallow-70b-RP.Q2_K.gguf) | Q2_K | 25.7 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-RP-GGUF/resolve/main/Swallow-70b-RP.IQ3_XS.gguf) | IQ3_XS | 28.5 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-RP-GGUF/resolve/main/Swallow-70b-RP.IQ3_S.gguf) | IQ3_S | 30.1 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-RP-GGUF/resolve/main/Swallow-70b-RP.Q3_K_S.gguf) | Q3_K_S | 30.1 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-RP-GGUF/resolve/main/Swallow-70b-RP.IQ3_M.gguf) | IQ3_M | 31.2 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-RP-GGUF/resolve/main/Swallow-70b-RP.Q3_K_M.gguf) | Q3_K_M | 33.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-RP-GGUF/resolve/main/Swallow-70b-RP.Q3_K_L.gguf) | Q3_K_L | 36.4 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-RP-GGUF/resolve/main/Swallow-70b-RP.IQ4_XS.gguf) | IQ4_XS | 37.4 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-RP-GGUF/resolve/main/Swallow-70b-RP.Q4_K_S.gguf) | Q4_K_S | 39.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-RP-GGUF/resolve/main/Swallow-70b-RP.Q4_K_M.gguf) | Q4_K_M | 41.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-RP-GGUF/resolve/main/Swallow-70b-RP.Q5_K_S.gguf) | Q5_K_S | 47.7 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-RP-GGUF/resolve/main/Swallow-70b-RP.Q5_K_M.gguf) | Q5_K_M | 49.0 | |
| [PART 1](https://huggingface.co/mradermacher/Swallow-70b-RP-GGUF/resolve/main/Swallow-70b-RP.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Swallow-70b-RP-GGUF/resolve/main/Swallow-70b-RP.Q6_K.gguf.part2of2) | Q6_K | 56.8 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Swallow-70b-RP-GGUF/resolve/main/Swallow-70b-RP.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Swallow-70b-RP-GGUF/resolve/main/Swallow-70b-RP.Q8_0.gguf.part2of2) | Q8_0 | 73.6 | 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
## 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.
<!-- end -->
|
mradermacher/Alpacino13b-GGUF
|
mradermacher
| 2024-05-06T05:17:07Z | 31 | 0 |
transformers
|
[
"transformers",
"gguf",
"alpaca",
"en",
"base_model:digitous/Alpacino13b",
"base_model:quantized:digitous/Alpacino13b",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-04-04T20:54:42Z |
---
base_model: digitous/Alpacino13b
language:
- en
library_name: transformers
license: other
quantized_by: mradermacher
tags:
- alpaca
---
## About
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/digitous/Alpacino13b
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/Alpacino13b-GGUF/resolve/main/Alpacino13b.Q2_K.gguf) | Q2_K | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/Alpacino13b-GGUF/resolve/main/Alpacino13b.IQ3_XS.gguf) | IQ3_XS | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/Alpacino13b-GGUF/resolve/main/Alpacino13b.IQ3_S.gguf) | IQ3_S | 5.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Alpacino13b-GGUF/resolve/main/Alpacino13b.Q3_K_S.gguf) | Q3_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Alpacino13b-GGUF/resolve/main/Alpacino13b.IQ3_M.gguf) | IQ3_M | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/Alpacino13b-GGUF/resolve/main/Alpacino13b.Q3_K_M.gguf) | Q3_K_M | 6.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Alpacino13b-GGUF/resolve/main/Alpacino13b.Q3_K_L.gguf) | Q3_K_L | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/Alpacino13b-GGUF/resolve/main/Alpacino13b.IQ4_XS.gguf) | IQ4_XS | 7.1 | |
| [GGUF](https://huggingface.co/mradermacher/Alpacino13b-GGUF/resolve/main/Alpacino13b.Q4_K_S.gguf) | Q4_K_S | 7.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Alpacino13b-GGUF/resolve/main/Alpacino13b.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Alpacino13b-GGUF/resolve/main/Alpacino13b.Q5_K_S.gguf) | Q5_K_S | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/Alpacino13b-GGUF/resolve/main/Alpacino13b.Q5_K_M.gguf) | Q5_K_M | 9.3 | |
| [GGUF](https://huggingface.co/mradermacher/Alpacino13b-GGUF/resolve/main/Alpacino13b.Q6_K.gguf) | Q6_K | 10.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Alpacino13b-GGUF/resolve/main/Alpacino13b.Q8_0.gguf) | Q8_0 | 13.9 | 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
## 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.
<!-- end -->
|
mradermacher/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic-GGUF
|
mradermacher
| 2024-05-06T05:16:57Z | 2 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:TroyDoesAI/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic",
"base_model:quantized:TroyDoesAI/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-04T22:43:02Z |
---
base_model: TroyDoesAI/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic
language:
- en
library_name: transformers
license: cc-by-4.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/TroyDoesAI/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic-GGUF/resolve/main/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic.Q2_K.gguf) | Q2_K | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic-GGUF/resolve/main/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic.IQ3_XS.gguf) | IQ3_XS | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic-GGUF/resolve/main/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic.Q3_K_S.gguf) | Q3_K_S | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic-GGUF/resolve/main/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic.IQ3_S.gguf) | IQ3_S | 4.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic-GGUF/resolve/main/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic.IQ3_M.gguf) | IQ3_M | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic-GGUF/resolve/main/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic.Q3_K_M.gguf) | Q3_K_M | 4.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic-GGUF/resolve/main/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic.Q3_K_L.gguf) | Q3_K_L | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic-GGUF/resolve/main/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic.IQ4_XS.gguf) | IQ4_XS | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic-GGUF/resolve/main/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic.Q4_K_S.gguf) | Q4_K_S | 5.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic-GGUF/resolve/main/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic.Q4_K_M.gguf) | Q4_K_M | 5.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic-GGUF/resolve/main/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic.Q5_K_S.gguf) | Q5_K_S | 6.2 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic-GGUF/resolve/main/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic.Q5_K_M.gguf) | Q5_K_M | 6.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic-GGUF/resolve/main/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic.Q6_K.gguf) | Q6_K | 7.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic-GGUF/resolve/main/Mermaid_Yi-9B_Factual_Temps_Full_Synthetic.Q8_0.gguf) | Q8_0 | 9.5 | 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
## 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.
<!-- end -->
|
mradermacher/Capybara-Tess-Yi-34B-200K-i1-GGUF
|
mradermacher
| 2024-05-06T05:16:52Z | 37 | 0 |
transformers
|
[
"transformers",
"gguf",
"merge",
"en",
"base_model:brucethemoose/Capybara-Tess-Yi-34B-200K",
"base_model:quantized:brucethemoose/Capybara-Tess-Yi-34B-200K",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T00:09:20Z |
---
base_model: brucethemoose/Capybara-Tess-Yi-34B-200K
language:
- en
library_name: transformers
license: other
license_link: https://huggingface.co/01-ai/Yi-34B/blob/main/LICENSE
license_name: yi-license
quantized_by: mradermacher
tags:
- merge
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/brucethemoose/Capybara-Tess-Yi-34B-200K
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Capybara-Tess-Yi-34B-200K-GGUF
## 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/Capybara-Tess-Yi-34B-200K-i1-GGUF/resolve/main/Capybara-Tess-Yi-34B-200K.i1-IQ1_S.gguf) | i1-IQ1_S | 7.6 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Capybara-Tess-Yi-34B-200K-i1-GGUF/resolve/main/Capybara-Tess-Yi-34B-200K.i1-IQ1_M.gguf) | i1-IQ1_M | 8.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Capybara-Tess-Yi-34B-200K-i1-GGUF/resolve/main/Capybara-Tess-Yi-34B-200K.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 9.4 | |
| [GGUF](https://huggingface.co/mradermacher/Capybara-Tess-Yi-34B-200K-i1-GGUF/resolve/main/Capybara-Tess-Yi-34B-200K.i1-IQ2_XS.gguf) | i1-IQ2_XS | 10.4 | |
| [GGUF](https://huggingface.co/mradermacher/Capybara-Tess-Yi-34B-200K-i1-GGUF/resolve/main/Capybara-Tess-Yi-34B-200K.i1-IQ2_S.gguf) | i1-IQ2_S | 11.0 | |
| [GGUF](https://huggingface.co/mradermacher/Capybara-Tess-Yi-34B-200K-i1-GGUF/resolve/main/Capybara-Tess-Yi-34B-200K.i1-IQ2_M.gguf) | i1-IQ2_M | 11.9 | |
| [GGUF](https://huggingface.co/mradermacher/Capybara-Tess-Yi-34B-200K-i1-GGUF/resolve/main/Capybara-Tess-Yi-34B-200K.i1-Q2_K.gguf) | i1-Q2_K | 12.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Capybara-Tess-Yi-34B-200K-i1-GGUF/resolve/main/Capybara-Tess-Yi-34B-200K.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 13.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Capybara-Tess-Yi-34B-200K-i1-GGUF/resolve/main/Capybara-Tess-Yi-34B-200K.i1-IQ3_XS.gguf) | i1-IQ3_XS | 14.3 | |
| [GGUF](https://huggingface.co/mradermacher/Capybara-Tess-Yi-34B-200K-i1-GGUF/resolve/main/Capybara-Tess-Yi-34B-200K.i1-Q3_K_S.gguf) | i1-Q3_K_S | 15.1 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Capybara-Tess-Yi-34B-200K-i1-GGUF/resolve/main/Capybara-Tess-Yi-34B-200K.i1-IQ3_S.gguf) | i1-IQ3_S | 15.1 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Capybara-Tess-Yi-34B-200K-i1-GGUF/resolve/main/Capybara-Tess-Yi-34B-200K.i1-IQ3_M.gguf) | i1-IQ3_M | 15.7 | |
| [GGUF](https://huggingface.co/mradermacher/Capybara-Tess-Yi-34B-200K-i1-GGUF/resolve/main/Capybara-Tess-Yi-34B-200K.i1-Q3_K_M.gguf) | i1-Q3_K_M | 16.8 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Capybara-Tess-Yi-34B-200K-i1-GGUF/resolve/main/Capybara-Tess-Yi-34B-200K.i1-Q3_K_L.gguf) | i1-Q3_K_L | 18.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Capybara-Tess-Yi-34B-200K-i1-GGUF/resolve/main/Capybara-Tess-Yi-34B-200K.i1-IQ4_XS.gguf) | i1-IQ4_XS | 18.6 | |
| [GGUF](https://huggingface.co/mradermacher/Capybara-Tess-Yi-34B-200K-i1-GGUF/resolve/main/Capybara-Tess-Yi-34B-200K.i1-Q4_0.gguf) | i1-Q4_0 | 19.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Capybara-Tess-Yi-34B-200K-i1-GGUF/resolve/main/Capybara-Tess-Yi-34B-200K.i1-Q4_K_S.gguf) | i1-Q4_K_S | 19.7 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Capybara-Tess-Yi-34B-200K-i1-GGUF/resolve/main/Capybara-Tess-Yi-34B-200K.i1-Q4_K_M.gguf) | i1-Q4_K_M | 20.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Capybara-Tess-Yi-34B-200K-i1-GGUF/resolve/main/Capybara-Tess-Yi-34B-200K.i1-Q5_K_S.gguf) | i1-Q5_K_S | 23.8 | |
| [GGUF](https://huggingface.co/mradermacher/Capybara-Tess-Yi-34B-200K-i1-GGUF/resolve/main/Capybara-Tess-Yi-34B-200K.i1-Q5_K_M.gguf) | i1-Q5_K_M | 24.4 | |
| [GGUF](https://huggingface.co/mradermacher/Capybara-Tess-Yi-34B-200K-i1-GGUF/resolve/main/Capybara-Tess-Yi-34B-200K.i1-Q6_K.gguf) | i1-Q6_K | 28.3 | 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.
<!-- end -->
|
mradermacher/NeuralNinja-2x-7B-GGUF
|
mradermacher
| 2024-05-06T05:16:45Z | 2 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:Muhammad2003/NeuralNinja-2x-7B",
"base_model:quantized:Muhammad2003/NeuralNinja-2x-7B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-05T00:55:22Z |
---
base_model: Muhammad2003/NeuralNinja-2x-7B
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Muhammad2003/NeuralNinja-2x-7B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/NeuralNinja-2x-7B-GGUF/resolve/main/NeuralNinja-2x-7B.Q2_K.gguf) | Q2_K | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralNinja-2x-7B-GGUF/resolve/main/NeuralNinja-2x-7B.IQ3_XS.gguf) | IQ3_XS | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralNinja-2x-7B-GGUF/resolve/main/NeuralNinja-2x-7B.Q3_K_S.gguf) | Q3_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralNinja-2x-7B-GGUF/resolve/main/NeuralNinja-2x-7B.IQ3_S.gguf) | IQ3_S | 5.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/NeuralNinja-2x-7B-GGUF/resolve/main/NeuralNinja-2x-7B.IQ3_M.gguf) | IQ3_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralNinja-2x-7B-GGUF/resolve/main/NeuralNinja-2x-7B.Q3_K_M.gguf) | Q3_K_M | 6.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/NeuralNinja-2x-7B-GGUF/resolve/main/NeuralNinja-2x-7B.Q3_K_L.gguf) | Q3_K_L | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralNinja-2x-7B-GGUF/resolve/main/NeuralNinja-2x-7B.IQ4_XS.gguf) | IQ4_XS | 7.1 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralNinja-2x-7B-GGUF/resolve/main/NeuralNinja-2x-7B.Q4_K_S.gguf) | Q4_K_S | 7.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/NeuralNinja-2x-7B-GGUF/resolve/main/NeuralNinja-2x-7B.Q4_K_M.gguf) | Q4_K_M | 7.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/NeuralNinja-2x-7B-GGUF/resolve/main/NeuralNinja-2x-7B.Q5_K_S.gguf) | Q5_K_S | 9.0 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralNinja-2x-7B-GGUF/resolve/main/NeuralNinja-2x-7B.Q5_K_M.gguf) | Q5_K_M | 9.2 | |
| [GGUF](https://huggingface.co/mradermacher/NeuralNinja-2x-7B-GGUF/resolve/main/NeuralNinja-2x-7B.Q6_K.gguf) | Q6_K | 10.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/NeuralNinja-2x-7B-GGUF/resolve/main/NeuralNinja-2x-7B.Q8_0.gguf) | Q8_0 | 13.8 | 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
## 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.
<!-- end -->
|
mradermacher/hydra-moe-120b-GGUF
|
mradermacher
| 2024-05-06T05:16:38Z | 19 | 0 |
transformers
|
[
"transformers",
"gguf",
"moe",
"moerge",
"en",
"base_model:ibivibiv/hydra-moe-120b",
"base_model:quantized:ibivibiv/hydra-moe-120b",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-05T01:49:53Z |
---
base_model: ibivibiv/hydra-moe-120b
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- moe
- moerge
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/ibivibiv/hydra-moe-120b
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/hydra-moe-120b-i1-GGUF
## 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/hydra-moe-120b-GGUF/resolve/main/hydra-moe-120b.Q2_K.gguf) | Q2_K | 41.6 | |
| [GGUF](https://huggingface.co/mradermacher/hydra-moe-120b-GGUF/resolve/main/hydra-moe-120b.IQ3_XS.gguf) | IQ3_XS | 46.5 | |
| [GGUF](https://huggingface.co/mradermacher/hydra-moe-120b-GGUF/resolve/main/hydra-moe-120b.Q3_K_S.gguf) | Q3_K_S | 49.1 | |
| [GGUF](https://huggingface.co/mradermacher/hydra-moe-120b-GGUF/resolve/main/hydra-moe-120b.IQ3_S.gguf) | IQ3_S | 49.2 | beats Q3_K* |
| [PART 1](https://huggingface.co/mradermacher/hydra-moe-120b-GGUF/resolve/main/hydra-moe-120b.IQ3_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/hydra-moe-120b-GGUF/resolve/main/hydra-moe-120b.IQ3_M.gguf.part2of2) | IQ3_M | 50.1 | |
| [PART 1](https://huggingface.co/mradermacher/hydra-moe-120b-GGUF/resolve/main/hydra-moe-120b.Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/hydra-moe-120b-GGUF/resolve/main/hydra-moe-120b.Q3_K_M.gguf.part2of2) | Q3_K_M | 54.5 | lower quality |
| [PART 1](https://huggingface.co/mradermacher/hydra-moe-120b-GGUF/resolve/main/hydra-moe-120b.Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/hydra-moe-120b-GGUF/resolve/main/hydra-moe-120b.Q3_K_L.gguf.part2of2) | Q3_K_L | 59.1 | |
| [PART 1](https://huggingface.co/mradermacher/hydra-moe-120b-GGUF/resolve/main/hydra-moe-120b.IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/hydra-moe-120b-GGUF/resolve/main/hydra-moe-120b.IQ4_XS.gguf.part2of2) | IQ4_XS | 61.3 | |
| [PART 1](https://huggingface.co/mradermacher/hydra-moe-120b-GGUF/resolve/main/hydra-moe-120b.Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/hydra-moe-120b-GGUF/resolve/main/hydra-moe-120b.Q4_K_S.gguf.part2of2) | Q4_K_S | 64.7 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/hydra-moe-120b-GGUF/resolve/main/hydra-moe-120b.Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/hydra-moe-120b-GGUF/resolve/main/hydra-moe-120b.Q4_K_M.gguf.part2of2) | Q4_K_M | 68.8 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/hydra-moe-120b-GGUF/resolve/main/hydra-moe-120b.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/hydra-moe-120b-GGUF/resolve/main/hydra-moe-120b.Q5_K_S.gguf.part2of2) | Q5_K_S | 78.3 | |
| [PART 1](https://huggingface.co/mradermacher/hydra-moe-120b-GGUF/resolve/main/hydra-moe-120b.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/hydra-moe-120b-GGUF/resolve/main/hydra-moe-120b.Q5_K_M.gguf.part2of2) | Q5_K_M | 80.7 | |
| [PART 1](https://huggingface.co/mradermacher/hydra-moe-120b-GGUF/resolve/main/hydra-moe-120b.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/hydra-moe-120b-GGUF/resolve/main/hydra-moe-120b.Q6_K.gguf.part2of2) | Q6_K | 93.3 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/hydra-moe-120b-GGUF/resolve/main/hydra-moe-120b.Q8_0.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/hydra-moe-120b-GGUF/resolve/main/hydra-moe-120b.Q8_0.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/hydra-moe-120b-GGUF/resolve/main/hydra-moe-120b.Q8_0.gguf.part3of3) | Q8_0 | 120.9 | 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
## 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.
<!-- end -->
|
mradermacher/WizardLM-30B-V1.0-GGUF
|
mradermacher
| 2024-05-06T05:16:35Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:WizardLM/WizardLM-30B-V1.0",
"base_model:quantized:WizardLM/WizardLM-30B-V1.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T01:57:20Z |
---
base_model: WizardLM/WizardLM-30B-V1.0
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/WizardLM/WizardLM-30B-V1.0
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/WizardLM-30B-V1.0-i1-GGUF
## 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/WizardLM-30B-V1.0-GGUF/resolve/main/WizardLM-30B-V1.0.Q2_K.gguf) | Q2_K | 12.1 | |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-GGUF/resolve/main/WizardLM-30B-V1.0.IQ3_XS.gguf) | IQ3_XS | 13.4 | |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-GGUF/resolve/main/WizardLM-30B-V1.0.IQ3_S.gguf) | IQ3_S | 14.2 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-GGUF/resolve/main/WizardLM-30B-V1.0.Q3_K_S.gguf) | Q3_K_S | 14.2 | |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-GGUF/resolve/main/WizardLM-30B-V1.0.IQ3_M.gguf) | IQ3_M | 15.0 | |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-GGUF/resolve/main/WizardLM-30B-V1.0.Q3_K_M.gguf) | Q3_K_M | 15.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-GGUF/resolve/main/WizardLM-30B-V1.0.Q3_K_L.gguf) | Q3_K_L | 17.4 | |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-GGUF/resolve/main/WizardLM-30B-V1.0.IQ4_XS.gguf) | IQ4_XS | 17.6 | |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-GGUF/resolve/main/WizardLM-30B-V1.0.Q4_K_S.gguf) | Q4_K_S | 18.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-GGUF/resolve/main/WizardLM-30B-V1.0.Q4_K_M.gguf) | Q4_K_M | 19.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-GGUF/resolve/main/WizardLM-30B-V1.0.Q5_K_S.gguf) | Q5_K_S | 22.5 | |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-GGUF/resolve/main/WizardLM-30B-V1.0.Q5_K_M.gguf) | Q5_K_M | 23.1 | |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-GGUF/resolve/main/WizardLM-30B-V1.0.Q6_K.gguf) | Q6_K | 26.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/WizardLM-30B-V1.0-GGUF/resolve/main/WizardLM-30B-V1.0.Q8_0.gguf) | Q8_0 | 34.7 | 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
## 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.
<!-- end -->
|
mradermacher/Irene-RP-v5-7B-GGUF
|
mradermacher
| 2024-05-06T05:16:24Z | 1 | 1 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"mistral",
"roleplay",
"en",
"base_model:Virt-io/Irene-RP-v5-7B",
"base_model:quantized:Virt-io/Irene-RP-v5-7B",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T02:29:13Z |
---
base_model: Virt-io/Irene-RP-v5-7B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
- mistral
- roleplay
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Virt-io/Irene-RP-v5-7B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/Irene-RP-v5-7B-GGUF/resolve/main/Irene-RP-v5-7B.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v5-7B-GGUF/resolve/main/Irene-RP-v5-7B.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v5-7B-GGUF/resolve/main/Irene-RP-v5-7B.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v5-7B-GGUF/resolve/main/Irene-RP-v5-7B.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v5-7B-GGUF/resolve/main/Irene-RP-v5-7B.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v5-7B-GGUF/resolve/main/Irene-RP-v5-7B.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v5-7B-GGUF/resolve/main/Irene-RP-v5-7B.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v5-7B-GGUF/resolve/main/Irene-RP-v5-7B.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v5-7B-GGUF/resolve/main/Irene-RP-v5-7B.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v5-7B-GGUF/resolve/main/Irene-RP-v5-7B.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v5-7B-GGUF/resolve/main/Irene-RP-v5-7B.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v5-7B-GGUF/resolve/main/Irene-RP-v5-7B.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v5-7B-GGUF/resolve/main/Irene-RP-v5-7B.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Irene-RP-v5-7B-GGUF/resolve/main/Irene-RP-v5-7B.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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.
<!-- end -->
|
mradermacher/Aguie-chat_v0.1-GGUF
|
mradermacher
| 2024-05-06T05:16:21Z | 91 | 0 |
transformers
|
[
"transformers",
"gguf",
"ko",
"en",
"base_model:Heoni/Aguie-chat_v0.1",
"base_model:quantized:Heoni/Aguie-chat_v0.1",
"license:cc-by-nc-nd-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T03:21:10Z |
---
base_model: Heoni/Aguie-chat_v0.1
language:
- ko
- en
library_name: transformers
license: cc-by-nc-nd-4.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Heoni/Aguie-chat_v0.1
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/Aguie-chat_v0.1-GGUF/resolve/main/Aguie-chat_v0.1.Q2_K.gguf) | Q2_K | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Aguie-chat_v0.1-GGUF/resolve/main/Aguie-chat_v0.1.IQ3_XS.gguf) | IQ3_XS | 5.6 | |
| [GGUF](https://huggingface.co/mradermacher/Aguie-chat_v0.1-GGUF/resolve/main/Aguie-chat_v0.1.IQ3_S.gguf) | IQ3_S | 5.9 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Aguie-chat_v0.1-GGUF/resolve/main/Aguie-chat_v0.1.Q3_K_S.gguf) | Q3_K_S | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/Aguie-chat_v0.1-GGUF/resolve/main/Aguie-chat_v0.1.IQ3_M.gguf) | IQ3_M | 6.2 | |
| [GGUF](https://huggingface.co/mradermacher/Aguie-chat_v0.1-GGUF/resolve/main/Aguie-chat_v0.1.Q3_K_M.gguf) | Q3_K_M | 6.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Aguie-chat_v0.1-GGUF/resolve/main/Aguie-chat_v0.1.Q3_K_L.gguf) | Q3_K_L | 7.2 | |
| [GGUF](https://huggingface.co/mradermacher/Aguie-chat_v0.1-GGUF/resolve/main/Aguie-chat_v0.1.IQ4_XS.gguf) | IQ4_XS | 7.3 | |
| [GGUF](https://huggingface.co/mradermacher/Aguie-chat_v0.1-GGUF/resolve/main/Aguie-chat_v0.1.Q4_K_S.gguf) | Q4_K_S | 7.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Aguie-chat_v0.1-GGUF/resolve/main/Aguie-chat_v0.1.Q4_K_M.gguf) | Q4_K_M | 8.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Aguie-chat_v0.1-GGUF/resolve/main/Aguie-chat_v0.1.Q5_K_S.gguf) | Q5_K_S | 9.3 | |
| [GGUF](https://huggingface.co/mradermacher/Aguie-chat_v0.1-GGUF/resolve/main/Aguie-chat_v0.1.Q5_K_M.gguf) | Q5_K_M | 9.5 | |
| [GGUF](https://huggingface.co/mradermacher/Aguie-chat_v0.1-GGUF/resolve/main/Aguie-chat_v0.1.Q6_K.gguf) | Q6_K | 11.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Aguie-chat_v0.1-GGUF/resolve/main/Aguie-chat_v0.1.Q8_0.gguf) | Q8_0 | 14.2 | 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
## 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.
<!-- end -->
|
mradermacher/Mistral-7b-V0.3-ReAct-GGUF
|
mradermacher
| 2024-05-06T05:16:16Z | 51 | 2 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T03:43:30Z |
---
base_model: Maverick17/Mistral-7b-V0.3-ReAct
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Maverick17/Mistral-7b-V0.3-ReAct
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/Mistral-7b-V0.3-ReAct-GGUF/resolve/main/Mistral-7b-V0.3-ReAct.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7b-V0.3-ReAct-GGUF/resolve/main/Mistral-7b-V0.3-ReAct.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7b-V0.3-ReAct-GGUF/resolve/main/Mistral-7b-V0.3-ReAct.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7b-V0.3-ReAct-GGUF/resolve/main/Mistral-7b-V0.3-ReAct.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7b-V0.3-ReAct-GGUF/resolve/main/Mistral-7b-V0.3-ReAct.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7b-V0.3-ReAct-GGUF/resolve/main/Mistral-7b-V0.3-ReAct.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7b-V0.3-ReAct-GGUF/resolve/main/Mistral-7b-V0.3-ReAct.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7b-V0.3-ReAct-GGUF/resolve/main/Mistral-7b-V0.3-ReAct.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7b-V0.3-ReAct-GGUF/resolve/main/Mistral-7b-V0.3-ReAct.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7b-V0.3-ReAct-GGUF/resolve/main/Mistral-7b-V0.3-ReAct.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7b-V0.3-ReAct-GGUF/resolve/main/Mistral-7b-V0.3-ReAct.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7b-V0.3-ReAct-GGUF/resolve/main/Mistral-7b-V0.3-ReAct.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7b-V0.3-ReAct-GGUF/resolve/main/Mistral-7b-V0.3-ReAct.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Mistral-7b-V0.3-ReAct-GGUF/resolve/main/Mistral-7b-V0.3-ReAct.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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.
<!-- end -->
|
mradermacher/Aguie_v0.1-GGUF
|
mradermacher
| 2024-05-06T05:16:03Z | 36 | 0 |
transformers
|
[
"transformers",
"gguf",
"ko",
"en",
"base_model:Heoni/Aguie_v0.1",
"base_model:quantized:Heoni/Aguie_v0.1",
"license:cc-by-nc-nd-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T05:01:17Z |
---
base_model: Heoni/Aguie_v0.1
language:
- ko
- en
library_name: transformers
license: cc-by-nc-nd-4.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Heoni/Aguie_v0.1
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/Aguie_v0.1-GGUF/resolve/main/Aguie_v0.1.Q2_K.gguf) | Q2_K | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Aguie_v0.1-GGUF/resolve/main/Aguie_v0.1.IQ3_XS.gguf) | IQ3_XS | 5.6 | |
| [GGUF](https://huggingface.co/mradermacher/Aguie_v0.1-GGUF/resolve/main/Aguie_v0.1.IQ3_S.gguf) | IQ3_S | 5.9 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Aguie_v0.1-GGUF/resolve/main/Aguie_v0.1.Q3_K_S.gguf) | Q3_K_S | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/Aguie_v0.1-GGUF/resolve/main/Aguie_v0.1.IQ3_M.gguf) | IQ3_M | 6.2 | |
| [GGUF](https://huggingface.co/mradermacher/Aguie_v0.1-GGUF/resolve/main/Aguie_v0.1.Q3_K_M.gguf) | Q3_K_M | 6.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Aguie_v0.1-GGUF/resolve/main/Aguie_v0.1.Q3_K_L.gguf) | Q3_K_L | 7.2 | |
| [GGUF](https://huggingface.co/mradermacher/Aguie_v0.1-GGUF/resolve/main/Aguie_v0.1.IQ4_XS.gguf) | IQ4_XS | 7.3 | |
| [GGUF](https://huggingface.co/mradermacher/Aguie_v0.1-GGUF/resolve/main/Aguie_v0.1.Q4_K_S.gguf) | Q4_K_S | 7.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Aguie_v0.1-GGUF/resolve/main/Aguie_v0.1.Q4_K_M.gguf) | Q4_K_M | 8.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Aguie_v0.1-GGUF/resolve/main/Aguie_v0.1.Q5_K_S.gguf) | Q5_K_S | 9.3 | |
| [GGUF](https://huggingface.co/mradermacher/Aguie_v0.1-GGUF/resolve/main/Aguie_v0.1.Q5_K_M.gguf) | Q5_K_M | 9.5 | |
| [GGUF](https://huggingface.co/mradermacher/Aguie_v0.1-GGUF/resolve/main/Aguie_v0.1.Q6_K.gguf) | Q6_K | 11.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Aguie_v0.1-GGUF/resolve/main/Aguie_v0.1.Q8_0.gguf) | Q8_0 | 14.2 | 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
## 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.
<!-- end -->
|
mradermacher/TableLLM-7b-GGUF
|
mradermacher
| 2024-05-06T05:16:01Z | 146 | 0 |
transformers
|
[
"transformers",
"gguf",
"Table",
"QA",
"Code",
"en",
"dataset:RUCKBReasoning/TableLLM-SFT",
"base_model:RUCKBReasoning/TableLLM-7b",
"base_model:quantized:RUCKBReasoning/TableLLM-7b",
"license:llama2",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T05:06:18Z |
---
base_model: RUCKBReasoning/TableLLM-7b
datasets:
- RUCKBReasoning/TableLLM-SFT
language:
- en
library_name: transformers
license: llama2
quantized_by: mradermacher
tags:
- Table
- QA
- Code
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/RUCKBReasoning/TableLLM-7b
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/TableLLM-7b-GGUF/resolve/main/TableLLM-7b.Q2_K.gguf) | Q2_K | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-7b-GGUF/resolve/main/TableLLM-7b.IQ3_XS.gguf) | IQ3_XS | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-7b-GGUF/resolve/main/TableLLM-7b.IQ3_S.gguf) | IQ3_S | 3.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-7b-GGUF/resolve/main/TableLLM-7b.Q3_K_S.gguf) | Q3_K_S | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-7b-GGUF/resolve/main/TableLLM-7b.IQ3_M.gguf) | IQ3_M | 3.2 | |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-7b-GGUF/resolve/main/TableLLM-7b.Q3_K_M.gguf) | Q3_K_M | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-7b-GGUF/resolve/main/TableLLM-7b.Q3_K_L.gguf) | Q3_K_L | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-7b-GGUF/resolve/main/TableLLM-7b.IQ4_XS.gguf) | IQ4_XS | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-7b-GGUF/resolve/main/TableLLM-7b.Q4_K_S.gguf) | Q4_K_S | 4.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-7b-GGUF/resolve/main/TableLLM-7b.Q4_K_M.gguf) | Q4_K_M | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-7b-GGUF/resolve/main/TableLLM-7b.Q5_K_S.gguf) | Q5_K_S | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-7b-GGUF/resolve/main/TableLLM-7b.Q5_K_M.gguf) | Q5_K_M | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-7b-GGUF/resolve/main/TableLLM-7b.Q6_K.gguf) | Q6_K | 5.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-7b-GGUF/resolve/main/TableLLM-7b.Q8_0.gguf) | Q8_0 | 7.3 | 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
## 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.
<!-- end -->
|
mradermacher/bophades-v2-mistral-7B-GGUF
|
mradermacher
| 2024-05-06T05:15:51Z | 40 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:nbeerbower/bophades-v2-mistral-7B",
"base_model:quantized:nbeerbower/bophades-v2-mistral-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T06:26:40Z |
---
base_model: nbeerbower/bophades-v2-mistral-7B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/nbeerbower/bophades-v2-mistral-7B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/bophades-v2-mistral-7B-GGUF/resolve/main/bophades-v2-mistral-7B.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/bophades-v2-mistral-7B-GGUF/resolve/main/bophades-v2-mistral-7B.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/bophades-v2-mistral-7B-GGUF/resolve/main/bophades-v2-mistral-7B.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/bophades-v2-mistral-7B-GGUF/resolve/main/bophades-v2-mistral-7B.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/bophades-v2-mistral-7B-GGUF/resolve/main/bophades-v2-mistral-7B.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/bophades-v2-mistral-7B-GGUF/resolve/main/bophades-v2-mistral-7B.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/bophades-v2-mistral-7B-GGUF/resolve/main/bophades-v2-mistral-7B.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/bophades-v2-mistral-7B-GGUF/resolve/main/bophades-v2-mistral-7B.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/bophades-v2-mistral-7B-GGUF/resolve/main/bophades-v2-mistral-7B.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/bophades-v2-mistral-7B-GGUF/resolve/main/bophades-v2-mistral-7B.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/bophades-v2-mistral-7B-GGUF/resolve/main/bophades-v2-mistral-7B.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/bophades-v2-mistral-7B-GGUF/resolve/main/bophades-v2-mistral-7B.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/bophades-v2-mistral-7B-GGUF/resolve/main/bophades-v2-mistral-7B.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/bophades-v2-mistral-7B-GGUF/resolve/main/bophades-v2-mistral-7B.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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.
<!-- end -->
|
mradermacher/TableLLM-13b-GGUF
|
mradermacher
| 2024-05-06T05:15:43Z | 129 | 0 |
transformers
|
[
"transformers",
"gguf",
"Table",
"QA",
"Code",
"en",
"dataset:RUCKBReasoning/TableLLM-SFT",
"base_model:RUCKBReasoning/TableLLM-13b",
"base_model:quantized:RUCKBReasoning/TableLLM-13b",
"license:llama2",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T06:37:28Z |
---
base_model: RUCKBReasoning/TableLLM-13b
datasets:
- RUCKBReasoning/TableLLM-SFT
language:
- en
library_name: transformers
license: llama2
quantized_by: mradermacher
tags:
- Table
- QA
- Code
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/RUCKBReasoning/TableLLM-13b
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/TableLLM-13b-GGUF/resolve/main/TableLLM-13b.Q2_K.gguf) | Q2_K | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-13b-GGUF/resolve/main/TableLLM-13b.IQ3_XS.gguf) | IQ3_XS | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-13b-GGUF/resolve/main/TableLLM-13b.IQ3_S.gguf) | IQ3_S | 5.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-13b-GGUF/resolve/main/TableLLM-13b.Q3_K_S.gguf) | Q3_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-13b-GGUF/resolve/main/TableLLM-13b.IQ3_M.gguf) | IQ3_M | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-13b-GGUF/resolve/main/TableLLM-13b.Q3_K_M.gguf) | Q3_K_M | 6.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-13b-GGUF/resolve/main/TableLLM-13b.Q3_K_L.gguf) | Q3_K_L | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-13b-GGUF/resolve/main/TableLLM-13b.IQ4_XS.gguf) | IQ4_XS | 7.1 | |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-13b-GGUF/resolve/main/TableLLM-13b.Q4_K_S.gguf) | Q4_K_S | 7.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-13b-GGUF/resolve/main/TableLLM-13b.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-13b-GGUF/resolve/main/TableLLM-13b.Q5_K_S.gguf) | Q5_K_S | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-13b-GGUF/resolve/main/TableLLM-13b.Q5_K_M.gguf) | Q5_K_M | 9.3 | |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-13b-GGUF/resolve/main/TableLLM-13b.Q6_K.gguf) | Q6_K | 10.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/TableLLM-13b-GGUF/resolve/main/TableLLM-13b.Q8_0.gguf) | Q8_0 | 13.9 | 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
## 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.
<!-- end -->
|
mradermacher/Swallow-7b-hf-CodeSkill-GGUF
|
mradermacher
| 2024-05-06T05:15:31Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:HachiML/Swallow-7b-hf-CodeSkill",
"base_model:quantized:HachiML/Swallow-7b-hf-CodeSkill",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T08:12:09Z |
---
base_model: HachiML/Swallow-7b-hf-CodeSkill
language:
- en
library_name: transformers
quantized_by: mradermacher
tags: []
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/HachiML/Swallow-7b-hf-CodeSkill
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/Swallow-7b-hf-CodeSkill-GGUF/resolve/main/Swallow-7b-hf-CodeSkill.Q2_K.gguf) | Q2_K | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-7b-hf-CodeSkill-GGUF/resolve/main/Swallow-7b-hf-CodeSkill.IQ3_XS.gguf) | IQ3_XS | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-7b-hf-CodeSkill-GGUF/resolve/main/Swallow-7b-hf-CodeSkill.IQ3_S.gguf) | IQ3_S | 3.1 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Swallow-7b-hf-CodeSkill-GGUF/resolve/main/Swallow-7b-hf-CodeSkill.Q3_K_S.gguf) | Q3_K_S | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-7b-hf-CodeSkill-GGUF/resolve/main/Swallow-7b-hf-CodeSkill.IQ3_M.gguf) | IQ3_M | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-7b-hf-CodeSkill-GGUF/resolve/main/Swallow-7b-hf-CodeSkill.Q3_K_M.gguf) | Q3_K_M | 3.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Swallow-7b-hf-CodeSkill-GGUF/resolve/main/Swallow-7b-hf-CodeSkill.Q3_K_L.gguf) | Q3_K_L | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-7b-hf-CodeSkill-GGUF/resolve/main/Swallow-7b-hf-CodeSkill.IQ4_XS.gguf) | IQ4_XS | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-7b-hf-CodeSkill-GGUF/resolve/main/Swallow-7b-hf-CodeSkill.Q4_K_S.gguf) | Q4_K_S | 4.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Swallow-7b-hf-CodeSkill-GGUF/resolve/main/Swallow-7b-hf-CodeSkill.Q4_K_M.gguf) | Q4_K_M | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Swallow-7b-hf-CodeSkill-GGUF/resolve/main/Swallow-7b-hf-CodeSkill.Q5_K_S.gguf) | Q5_K_S | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-7b-hf-CodeSkill-GGUF/resolve/main/Swallow-7b-hf-CodeSkill.Q5_K_M.gguf) | Q5_K_M | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-7b-hf-CodeSkill-GGUF/resolve/main/Swallow-7b-hf-CodeSkill.Q6_K.gguf) | Q6_K | 5.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Swallow-7b-hf-CodeSkill-GGUF/resolve/main/Swallow-7b-hf-CodeSkill.Q8_0.gguf) | Q8_0 | 7.4 | 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
## 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.
<!-- end -->
|
mradermacher/Myrrh_solar_10.7b_v0.1-dpo-GGUF
|
mradermacher
| 2024-05-06T05:15:23Z | 2 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-05T09:02:52Z |
---
base_model: ParkTaeEon/Myrrh_solar_10.7b_v0.1-dpo
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/ParkTaeEon/Myrrh_solar_10.7b_v0.1-dpo
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/Myrrh_solar_10.7b_v0.1-dpo-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1-dpo.Q2_K.gguf) | Q2_K | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-dpo-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1-dpo.IQ3_XS.gguf) | IQ3_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-dpo-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1-dpo.Q3_K_S.gguf) | Q3_K_S | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-dpo-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1-dpo.IQ3_S.gguf) | IQ3_S | 4.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-dpo-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1-dpo.IQ3_M.gguf) | IQ3_M | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-dpo-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1-dpo.Q3_K_M.gguf) | Q3_K_M | 5.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-dpo-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1-dpo.Q3_K_L.gguf) | Q3_K_L | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-dpo-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1-dpo.IQ4_XS.gguf) | IQ4_XS | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-dpo-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1-dpo.Q4_K_S.gguf) | Q4_K_S | 6.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-dpo-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1-dpo.Q4_K_M.gguf) | Q4_K_M | 6.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-dpo-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1-dpo.Q5_K_S.gguf) | Q5_K_S | 7.5 | |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-dpo-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1-dpo.Q5_K_M.gguf) | Q5_K_M | 7.7 | |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-dpo-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1-dpo.Q6_K.gguf) | Q6_K | 8.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-dpo-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1-dpo.Q8_0.gguf) | Q8_0 | 11.5 | 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
## 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.
<!-- end -->
|
mradermacher/Myrrh_solar_10.7b_v0.1-GGUF
|
mradermacher
| 2024-05-06T05:14:59Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-05T11:43:30Z |
---
base_model: ParkTaeEon/Myrrh_solar_10.7b_v0.1
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/ParkTaeEon/Myrrh_solar_10.7b_v0.1
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/Myrrh_solar_10.7b_v0.1-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1.Q2_K.gguf) | Q2_K | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1.IQ3_XS.gguf) | IQ3_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1.Q3_K_S.gguf) | Q3_K_S | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1.IQ3_S.gguf) | IQ3_S | 4.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1.IQ3_M.gguf) | IQ3_M | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1.Q3_K_M.gguf) | Q3_K_M | 5.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1.Q3_K_L.gguf) | Q3_K_L | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1.IQ4_XS.gguf) | IQ4_XS | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1.Q4_K_S.gguf) | Q4_K_S | 6.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1.Q4_K_M.gguf) | Q4_K_M | 6.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1.Q5_K_S.gguf) | Q5_K_S | 7.5 | |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1.Q5_K_M.gguf) | Q5_K_M | 7.7 | |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1.Q6_K.gguf) | Q6_K | 8.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Myrrh_solar_10.7b_v0.1-GGUF/resolve/main/Myrrh_solar_10.7b_v0.1.Q8_0.gguf) | Q8_0 | 11.5 | 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
## 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.
<!-- end -->
|
mradermacher/Superkarakuri-lm-chat-70b-v0.1-GGUF
|
mradermacher
| 2024-05-06T05:14:57Z | 4 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"ja",
"license:llama2",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-05T12:00:20Z |
---
base_model: Aratako/Superkarakuri-lm-chat-70b-v0.1
language:
- ja
library_name: transformers
license: llama2
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Aratako/Superkarakuri-lm-chat-70b-v0.1
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/Superkarakuri-lm-chat-70b-v0.1-GGUF/resolve/main/Superkarakuri-lm-chat-70b-v0.1.Q2_K.gguf) | Q2_K | 25.7 | |
| [GGUF](https://huggingface.co/mradermacher/Superkarakuri-lm-chat-70b-v0.1-GGUF/resolve/main/Superkarakuri-lm-chat-70b-v0.1.IQ3_XS.gguf) | IQ3_XS | 28.6 | |
| [GGUF](https://huggingface.co/mradermacher/Superkarakuri-lm-chat-70b-v0.1-GGUF/resolve/main/Superkarakuri-lm-chat-70b-v0.1.IQ3_S.gguf) | IQ3_S | 30.2 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Superkarakuri-lm-chat-70b-v0.1-GGUF/resolve/main/Superkarakuri-lm-chat-70b-v0.1.Q3_K_S.gguf) | Q3_K_S | 30.2 | |
| [GGUF](https://huggingface.co/mradermacher/Superkarakuri-lm-chat-70b-v0.1-GGUF/resolve/main/Superkarakuri-lm-chat-70b-v0.1.IQ3_M.gguf) | IQ3_M | 31.2 | |
| [GGUF](https://huggingface.co/mradermacher/Superkarakuri-lm-chat-70b-v0.1-GGUF/resolve/main/Superkarakuri-lm-chat-70b-v0.1.Q3_K_M.gguf) | Q3_K_M | 33.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Superkarakuri-lm-chat-70b-v0.1-GGUF/resolve/main/Superkarakuri-lm-chat-70b-v0.1.Q3_K_L.gguf) | Q3_K_L | 36.4 | |
| [GGUF](https://huggingface.co/mradermacher/Superkarakuri-lm-chat-70b-v0.1-GGUF/resolve/main/Superkarakuri-lm-chat-70b-v0.1.IQ4_XS.gguf) | IQ4_XS | 37.4 | |
| [GGUF](https://huggingface.co/mradermacher/Superkarakuri-lm-chat-70b-v0.1-GGUF/resolve/main/Superkarakuri-lm-chat-70b-v0.1.Q4_K_S.gguf) | Q4_K_S | 39.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Superkarakuri-lm-chat-70b-v0.1-GGUF/resolve/main/Superkarakuri-lm-chat-70b-v0.1.Q4_K_M.gguf) | Q4_K_M | 41.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Superkarakuri-lm-chat-70b-v0.1-GGUF/resolve/main/Superkarakuri-lm-chat-70b-v0.1.Q5_K_S.gguf) | Q5_K_S | 47.7 | |
| [GGUF](https://huggingface.co/mradermacher/Superkarakuri-lm-chat-70b-v0.1-GGUF/resolve/main/Superkarakuri-lm-chat-70b-v0.1.Q5_K_M.gguf) | Q5_K_M | 49.0 | |
| [PART 1](https://huggingface.co/mradermacher/Superkarakuri-lm-chat-70b-v0.1-GGUF/resolve/main/Superkarakuri-lm-chat-70b-v0.1.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Superkarakuri-lm-chat-70b-v0.1-GGUF/resolve/main/Superkarakuri-lm-chat-70b-v0.1.Q6_K.gguf.part2of2) | Q6_K | 56.9 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Superkarakuri-lm-chat-70b-v0.1-GGUF/resolve/main/Superkarakuri-lm-chat-70b-v0.1.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Superkarakuri-lm-chat-70b-v0.1-GGUF/resolve/main/Superkarakuri-lm-chat-70b-v0.1.Q8_0.gguf.part2of2) | Q8_0 | 73.6 | 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
## 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.
<!-- end -->
|
mradermacher/pandafish-2-7b-32k-GGUF
|
mradermacher
| 2024-05-06T05:14:48Z | 16 | 5 |
transformers
|
[
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"cognitivecomputations/dolphin-2.8-mistral-7b-v02",
"en",
"base_model:ichigoberry/pandafish-2-7b-32k",
"base_model:quantized:ichigoberry/pandafish-2-7b-32k",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T14:05:35Z |
---
base_model: ichigoberry/pandafish-2-7b-32k
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- cognitivecomputations/dolphin-2.8-mistral-7b-v02
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/ichigoberry/pandafish-2-7b-32k
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/pandafish-2-7b-32k-GGUF/resolve/main/pandafish-2-7b-32k.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-2-7b-32k-GGUF/resolve/main/pandafish-2-7b-32k.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-2-7b-32k-GGUF/resolve/main/pandafish-2-7b-32k.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-2-7b-32k-GGUF/resolve/main/pandafish-2-7b-32k.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/pandafish-2-7b-32k-GGUF/resolve/main/pandafish-2-7b-32k.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-2-7b-32k-GGUF/resolve/main/pandafish-2-7b-32k.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/pandafish-2-7b-32k-GGUF/resolve/main/pandafish-2-7b-32k.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-2-7b-32k-GGUF/resolve/main/pandafish-2-7b-32k.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-2-7b-32k-GGUF/resolve/main/pandafish-2-7b-32k.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/pandafish-2-7b-32k-GGUF/resolve/main/pandafish-2-7b-32k.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/pandafish-2-7b-32k-GGUF/resolve/main/pandafish-2-7b-32k.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-2-7b-32k-GGUF/resolve/main/pandafish-2-7b-32k.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-2-7b-32k-GGUF/resolve/main/pandafish-2-7b-32k.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/pandafish-2-7b-32k-GGUF/resolve/main/pandafish-2-7b-32k.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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.
<!-- end -->
|
mradermacher/ClaudeLimaRP-Maid-10.7B-GGUF
|
mradermacher
| 2024-05-06T05:14:46Z | 107 | 1 |
transformers
|
[
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"Undi95/Mistral-ClaudeLimaRP-v3-7B",
"SanjiWatsuki/Silicon-Maid-7B",
"en",
"base_model:akrads/ClaudeLimaRP-Maid-10.7B",
"base_model:quantized:akrads/ClaudeLimaRP-Maid-10.7B",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T14:16:23Z |
---
base_model: akrads/ClaudeLimaRP-Maid-10.7B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- Undi95/Mistral-ClaudeLimaRP-v3-7B
- SanjiWatsuki/Silicon-Maid-7B
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/akrads/ClaudeLimaRP-Maid-10.7B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/ClaudeLimaRP-Maid-10.7B-GGUF/resolve/main/ClaudeLimaRP-Maid-10.7B.Q2_K.gguf) | Q2_K | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/ClaudeLimaRP-Maid-10.7B-GGUF/resolve/main/ClaudeLimaRP-Maid-10.7B.IQ3_XS.gguf) | IQ3_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/ClaudeLimaRP-Maid-10.7B-GGUF/resolve/main/ClaudeLimaRP-Maid-10.7B.Q3_K_S.gguf) | Q3_K_S | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/ClaudeLimaRP-Maid-10.7B-GGUF/resolve/main/ClaudeLimaRP-Maid-10.7B.IQ3_S.gguf) | IQ3_S | 4.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/ClaudeLimaRP-Maid-10.7B-GGUF/resolve/main/ClaudeLimaRP-Maid-10.7B.IQ3_M.gguf) | IQ3_M | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/ClaudeLimaRP-Maid-10.7B-GGUF/resolve/main/ClaudeLimaRP-Maid-10.7B.Q3_K_M.gguf) | Q3_K_M | 5.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/ClaudeLimaRP-Maid-10.7B-GGUF/resolve/main/ClaudeLimaRP-Maid-10.7B.Q3_K_L.gguf) | Q3_K_L | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/ClaudeLimaRP-Maid-10.7B-GGUF/resolve/main/ClaudeLimaRP-Maid-10.7B.IQ4_XS.gguf) | IQ4_XS | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/ClaudeLimaRP-Maid-10.7B-GGUF/resolve/main/ClaudeLimaRP-Maid-10.7B.Q4_K_S.gguf) | Q4_K_S | 6.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ClaudeLimaRP-Maid-10.7B-GGUF/resolve/main/ClaudeLimaRP-Maid-10.7B.Q4_K_M.gguf) | Q4_K_M | 6.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ClaudeLimaRP-Maid-10.7B-GGUF/resolve/main/ClaudeLimaRP-Maid-10.7B.Q5_K_S.gguf) | Q5_K_S | 7.5 | |
| [GGUF](https://huggingface.co/mradermacher/ClaudeLimaRP-Maid-10.7B-GGUF/resolve/main/ClaudeLimaRP-Maid-10.7B.Q5_K_M.gguf) | Q5_K_M | 7.7 | |
| [GGUF](https://huggingface.co/mradermacher/ClaudeLimaRP-Maid-10.7B-GGUF/resolve/main/ClaudeLimaRP-Maid-10.7B.Q6_K.gguf) | Q6_K | 8.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/ClaudeLimaRP-Maid-10.7B-GGUF/resolve/main/ClaudeLimaRP-Maid-10.7B.Q8_0.gguf) | Q8_0 | 11.5 | 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
## 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.
<!-- end -->
|
mradermacher/Swallow-70b-NVE-RP-i1-GGUF
|
mradermacher
| 2024-05-06T05:14:43Z | 99 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"ja",
"base_model:nitky/Swallow-70b-NVE-RP",
"base_model:quantized:nitky/Swallow-70b-NVE-RP",
"license:llama2",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T14:20:52Z |
---
base_model: nitky/Swallow-70b-NVE-RP
language:
- en
- ja
library_name: transformers
license: llama2
model_type: llama
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/nitky/Swallow-70b-NVE-RP
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-GGUF
## 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/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-IQ1_S.gguf) | i1-IQ1_S | 14.6 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-IQ1_M.gguf) | i1-IQ1_M | 16.0 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 18.4 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-IQ2_XS.gguf) | i1-IQ2_XS | 20.4 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-IQ2_S.gguf) | i1-IQ2_S | 21.5 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-IQ2_M.gguf) | i1-IQ2_M | 23.3 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-Q2_K.gguf) | i1-Q2_K | 25.6 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 26.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-IQ3_XS.gguf) | i1-IQ3_XS | 28.4 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-IQ3_S.gguf) | i1-IQ3_S | 30.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-Q3_K_S.gguf) | i1-Q3_K_S | 30.0 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-IQ3_M.gguf) | i1-IQ3_M | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-Q3_K_M.gguf) | i1-Q3_K_M | 33.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-Q3_K_L.gguf) | i1-Q3_K_L | 36.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-IQ4_XS.gguf) | i1-IQ4_XS | 36.9 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-Q4_0.gguf) | i1-Q4_0 | 39.1 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-Q4_K_S.gguf) | i1-Q4_K_S | 39.3 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-Q4_K_M.gguf) | i1-Q4_K_M | 41.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-Q5_K_S.gguf) | i1-Q5_K_S | 47.6 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-Q5_K_M.gguf) | i1-Q5_K_M | 48.9 | |
| [PART 1](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Swallow-70b-NVE-RP-i1-GGUF/resolve/main/Swallow-70b-NVE-RP.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 56.7 | 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.
<!-- end -->
|
mradermacher/anarchy-solar-10B-v1-GGUF
|
mradermacher
| 2024-05-06T05:14:37Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"ko",
"base_model:moondriller/anarchy-solar-10B-v1",
"base_model:quantized:moondriller/anarchy-solar-10B-v1",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T15:34:15Z |
---
base_model: moondriller/anarchy-solar-10B-v1
language:
- ko
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/moondriller/anarchy-solar-10B-v1
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/anarchy-solar-10B-v1-GGUF/resolve/main/anarchy-solar-10B-v1.Q2_K.gguf) | Q2_K | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/anarchy-solar-10B-v1-GGUF/resolve/main/anarchy-solar-10B-v1.IQ3_XS.gguf) | IQ3_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/anarchy-solar-10B-v1-GGUF/resolve/main/anarchy-solar-10B-v1.Q3_K_S.gguf) | Q3_K_S | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/anarchy-solar-10B-v1-GGUF/resolve/main/anarchy-solar-10B-v1.IQ3_S.gguf) | IQ3_S | 4.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/anarchy-solar-10B-v1-GGUF/resolve/main/anarchy-solar-10B-v1.IQ3_M.gguf) | IQ3_M | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/anarchy-solar-10B-v1-GGUF/resolve/main/anarchy-solar-10B-v1.Q3_K_M.gguf) | Q3_K_M | 5.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/anarchy-solar-10B-v1-GGUF/resolve/main/anarchy-solar-10B-v1.Q3_K_L.gguf) | Q3_K_L | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/anarchy-solar-10B-v1-GGUF/resolve/main/anarchy-solar-10B-v1.IQ4_XS.gguf) | IQ4_XS | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/anarchy-solar-10B-v1-GGUF/resolve/main/anarchy-solar-10B-v1.Q4_K_S.gguf) | Q4_K_S | 6.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/anarchy-solar-10B-v1-GGUF/resolve/main/anarchy-solar-10B-v1.Q4_K_M.gguf) | Q4_K_M | 6.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/anarchy-solar-10B-v1-GGUF/resolve/main/anarchy-solar-10B-v1.Q5_K_S.gguf) | Q5_K_S | 7.5 | |
| [GGUF](https://huggingface.co/mradermacher/anarchy-solar-10B-v1-GGUF/resolve/main/anarchy-solar-10B-v1.Q5_K_M.gguf) | Q5_K_M | 7.7 | |
| [GGUF](https://huggingface.co/mradermacher/anarchy-solar-10B-v1-GGUF/resolve/main/anarchy-solar-10B-v1.Q6_K.gguf) | Q6_K | 8.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/anarchy-solar-10B-v1-GGUF/resolve/main/anarchy-solar-10B-v1.Q8_0.gguf) | Q8_0 | 11.5 | 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
## 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.
<!-- end -->
|
mradermacher/anarchy-llama2-13B-v2-GGUF
|
mradermacher
| 2024-05-06T05:14:30Z | 3 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:moondriller/anarchy-llama2-13B-v2",
"base_model:quantized:moondriller/anarchy-llama2-13B-v2",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T16:58:16Z |
---
base_model: moondriller/anarchy-llama2-13B-v2
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/moondriller/anarchy-llama2-13B-v2
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/anarchy-llama2-13B-v2-GGUF/resolve/main/anarchy-llama2-13B-v2.Q2_K.gguf) | Q2_K | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/anarchy-llama2-13B-v2-GGUF/resolve/main/anarchy-llama2-13B-v2.IQ3_XS.gguf) | IQ3_XS | 5.6 | |
| [GGUF](https://huggingface.co/mradermacher/anarchy-llama2-13B-v2-GGUF/resolve/main/anarchy-llama2-13B-v2.IQ3_S.gguf) | IQ3_S | 5.9 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/anarchy-llama2-13B-v2-GGUF/resolve/main/anarchy-llama2-13B-v2.Q3_K_S.gguf) | Q3_K_S | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/anarchy-llama2-13B-v2-GGUF/resolve/main/anarchy-llama2-13B-v2.IQ3_M.gguf) | IQ3_M | 6.2 | |
| [GGUF](https://huggingface.co/mradermacher/anarchy-llama2-13B-v2-GGUF/resolve/main/anarchy-llama2-13B-v2.Q3_K_M.gguf) | Q3_K_M | 6.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/anarchy-llama2-13B-v2-GGUF/resolve/main/anarchy-llama2-13B-v2.Q3_K_L.gguf) | Q3_K_L | 7.1 | |
| [GGUF](https://huggingface.co/mradermacher/anarchy-llama2-13B-v2-GGUF/resolve/main/anarchy-llama2-13B-v2.IQ4_XS.gguf) | IQ4_XS | 7.2 | |
| [GGUF](https://huggingface.co/mradermacher/anarchy-llama2-13B-v2-GGUF/resolve/main/anarchy-llama2-13B-v2.Q4_K_S.gguf) | Q4_K_S | 7.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/anarchy-llama2-13B-v2-GGUF/resolve/main/anarchy-llama2-13B-v2.Q4_K_M.gguf) | Q4_K_M | 8.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/anarchy-llama2-13B-v2-GGUF/resolve/main/anarchy-llama2-13B-v2.Q5_K_S.gguf) | Q5_K_S | 9.2 | |
| [GGUF](https://huggingface.co/mradermacher/anarchy-llama2-13B-v2-GGUF/resolve/main/anarchy-llama2-13B-v2.Q5_K_M.gguf) | Q5_K_M | 9.4 | |
| [GGUF](https://huggingface.co/mradermacher/anarchy-llama2-13B-v2-GGUF/resolve/main/anarchy-llama2-13B-v2.Q6_K.gguf) | Q6_K | 10.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/anarchy-llama2-13B-v2-GGUF/resolve/main/anarchy-llama2-13B-v2.Q8_0.gguf) | Q8_0 | 14.1 | 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
## 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.
<!-- end -->
|
mradermacher/Mergerix-7b-v0.5-GGUF
|
mradermacher
| 2024-05-06T05:14:27Z | 5 | 0 |
transformers
|
[
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"automerger/YamshadowExperiment28-7B",
"automerger/PasticheInex12-7B",
"en",
"base_model:MiniMoog/Mergerix-7b-v0.5",
"base_model:quantized:MiniMoog/Mergerix-7b-v0.5",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T18:32:48Z |
---
base_model: MiniMoog/Mergerix-7b-v0.5
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- automerger/YamshadowExperiment28-7B
- automerger/PasticheInex12-7B
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/MiniMoog/Mergerix-7b-v0.5
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/Mergerix-7b-v0.5-GGUF/resolve/main/Mergerix-7b-v0.5.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Mergerix-7b-v0.5-GGUF/resolve/main/Mergerix-7b-v0.5.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mergerix-7b-v0.5-GGUF/resolve/main/Mergerix-7b-v0.5.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mergerix-7b-v0.5-GGUF/resolve/main/Mergerix-7b-v0.5.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Mergerix-7b-v0.5-GGUF/resolve/main/Mergerix-7b-v0.5.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mergerix-7b-v0.5-GGUF/resolve/main/Mergerix-7b-v0.5.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mergerix-7b-v0.5-GGUF/resolve/main/Mergerix-7b-v0.5.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Mergerix-7b-v0.5-GGUF/resolve/main/Mergerix-7b-v0.5.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mergerix-7b-v0.5-GGUF/resolve/main/Mergerix-7b-v0.5.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mergerix-7b-v0.5-GGUF/resolve/main/Mergerix-7b-v0.5.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mergerix-7b-v0.5-GGUF/resolve/main/Mergerix-7b-v0.5.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mergerix-7b-v0.5-GGUF/resolve/main/Mergerix-7b-v0.5.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Mergerix-7b-v0.5-GGUF/resolve/main/Mergerix-7b-v0.5.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Mergerix-7b-v0.5-GGUF/resolve/main/Mergerix-7b-v0.5.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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.
<!-- end -->
|
mradermacher/H4na-7B-v0.1-GGUF
|
mradermacher
| 2024-05-06T05:14:20Z | 23 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"sft",
"en",
"base_model:Smuggling1710/H4na-7B-v0.1",
"base_model:quantized:Smuggling1710/H4na-7B-v0.1",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T19:53:00Z |
---
base_model: Smuggling1710/H4na-7B-v0.1
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
- sft
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Smuggling1710/H4na-7B-v0.1
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/H4na-7B-v0.1-GGUF/resolve/main/H4na-7B-v0.1.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/H4na-7B-v0.1-GGUF/resolve/main/H4na-7B-v0.1.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/H4na-7B-v0.1-GGUF/resolve/main/H4na-7B-v0.1.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/H4na-7B-v0.1-GGUF/resolve/main/H4na-7B-v0.1.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/H4na-7B-v0.1-GGUF/resolve/main/H4na-7B-v0.1.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/H4na-7B-v0.1-GGUF/resolve/main/H4na-7B-v0.1.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/H4na-7B-v0.1-GGUF/resolve/main/H4na-7B-v0.1.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/H4na-7B-v0.1-GGUF/resolve/main/H4na-7B-v0.1.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/H4na-7B-v0.1-GGUF/resolve/main/H4na-7B-v0.1.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/H4na-7B-v0.1-GGUF/resolve/main/H4na-7B-v0.1.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/H4na-7B-v0.1-GGUF/resolve/main/H4na-7B-v0.1.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/H4na-7B-v0.1-GGUF/resolve/main/H4na-7B-v0.1.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/H4na-7B-v0.1-GGUF/resolve/main/H4na-7B-v0.1.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/H4na-7B-v0.1-GGUF/resolve/main/H4na-7B-v0.1.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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.
<!-- end -->
|
mradermacher/13B-HyperMantis-GGUF
|
mradermacher
| 2024-05-06T05:14:16Z | 97 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"alpaca",
"vicuna",
"mix",
"merge",
"model merge",
"roleplay",
"chat",
"instruct",
"en",
"base_model:digitous/13B-HyperMantis",
"base_model:quantized:digitous/13B-HyperMantis",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T20:04:53Z |
---
base_model: digitous/13B-HyperMantis
language:
- en
library_name: transformers
license: other
quantized_by: mradermacher
tags:
- llama
- alpaca
- vicuna
- mix
- merge
- model merge
- roleplay
- chat
- instruct
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/digitous/13B-HyperMantis
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/13B-HyperMantis-GGUF/resolve/main/13B-HyperMantis.Q2_K.gguf) | Q2_K | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/13B-HyperMantis-GGUF/resolve/main/13B-HyperMantis.IQ3_XS.gguf) | IQ3_XS | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/13B-HyperMantis-GGUF/resolve/main/13B-HyperMantis.IQ3_S.gguf) | IQ3_S | 5.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/13B-HyperMantis-GGUF/resolve/main/13B-HyperMantis.Q3_K_S.gguf) | Q3_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/13B-HyperMantis-GGUF/resolve/main/13B-HyperMantis.IQ3_M.gguf) | IQ3_M | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/13B-HyperMantis-GGUF/resolve/main/13B-HyperMantis.Q3_K_M.gguf) | Q3_K_M | 6.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/13B-HyperMantis-GGUF/resolve/main/13B-HyperMantis.Q3_K_L.gguf) | Q3_K_L | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/13B-HyperMantis-GGUF/resolve/main/13B-HyperMantis.IQ4_XS.gguf) | IQ4_XS | 7.1 | |
| [GGUF](https://huggingface.co/mradermacher/13B-HyperMantis-GGUF/resolve/main/13B-HyperMantis.Q4_K_S.gguf) | Q4_K_S | 7.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/13B-HyperMantis-GGUF/resolve/main/13B-HyperMantis.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/13B-HyperMantis-GGUF/resolve/main/13B-HyperMantis.Q5_K_S.gguf) | Q5_K_S | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/13B-HyperMantis-GGUF/resolve/main/13B-HyperMantis.Q5_K_M.gguf) | Q5_K_M | 9.3 | |
| [GGUF](https://huggingface.co/mradermacher/13B-HyperMantis-GGUF/resolve/main/13B-HyperMantis.Q6_K.gguf) | Q6_K | 10.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/13B-HyperMantis-GGUF/resolve/main/13B-HyperMantis.Q8_0.gguf) | Q8_0 | 13.9 | 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
## 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.
<!-- end -->
|
mradermacher/Swallow-70b-RP-i1-GGUF
|
mradermacher
| 2024-05-06T05:13:54Z | 16 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"ja",
"base_model:nitky/Swallow-70b-RP",
"base_model:quantized:nitky/Swallow-70b-RP",
"license:llama2",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T21:40:58Z |
---
base_model: nitky/Swallow-70b-RP
language:
- en
- ja
library_name: transformers
license: llama2
model_type: llama
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/nitky/Swallow-70b-RP
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Swallow-70b-RP-GGUF
## 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/Swallow-70b-RP-i1-GGUF/resolve/main/Swallow-70b-RP.i1-IQ1_S.gguf) | i1-IQ1_S | 14.7 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-RP-i1-GGUF/resolve/main/Swallow-70b-RP.i1-IQ1_M.gguf) | i1-IQ1_M | 16.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-RP-i1-GGUF/resolve/main/Swallow-70b-RP.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 18.5 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-RP-i1-GGUF/resolve/main/Swallow-70b-RP.i1-IQ2_XS.gguf) | i1-IQ2_XS | 20.5 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-RP-i1-GGUF/resolve/main/Swallow-70b-RP.i1-IQ2_S.gguf) | i1-IQ2_S | 21.6 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-RP-i1-GGUF/resolve/main/Swallow-70b-RP.i1-IQ2_M.gguf) | i1-IQ2_M | 23.4 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-RP-i1-GGUF/resolve/main/Swallow-70b-RP.i1-Q2_K.gguf) | i1-Q2_K | 25.7 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-RP-i1-GGUF/resolve/main/Swallow-70b-RP.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 26.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-RP-i1-GGUF/resolve/main/Swallow-70b-RP.i1-IQ3_XS.gguf) | i1-IQ3_XS | 28.5 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-RP-i1-GGUF/resolve/main/Swallow-70b-RP.i1-IQ3_S.gguf) | i1-IQ3_S | 30.1 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-RP-i1-GGUF/resolve/main/Swallow-70b-RP.i1-Q3_K_S.gguf) | i1-Q3_K_S | 30.1 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-RP-i1-GGUF/resolve/main/Swallow-70b-RP.i1-IQ3_M.gguf) | i1-IQ3_M | 31.2 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-RP-i1-GGUF/resolve/main/Swallow-70b-RP.i1-Q3_K_M.gguf) | i1-Q3_K_M | 33.5 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-RP-i1-GGUF/resolve/main/Swallow-70b-RP.i1-Q3_K_L.gguf) | i1-Q3_K_L | 36.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-RP-i1-GGUF/resolve/main/Swallow-70b-RP.i1-IQ4_XS.gguf) | i1-IQ4_XS | 37.1 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-RP-i1-GGUF/resolve/main/Swallow-70b-RP.i1-Q4_0.gguf) | i1-Q4_0 | 39.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-RP-i1-GGUF/resolve/main/Swallow-70b-RP.i1-Q4_K_S.gguf) | i1-Q4_K_S | 39.5 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-RP-i1-GGUF/resolve/main/Swallow-70b-RP.i1-Q4_K_M.gguf) | i1-Q4_K_M | 41.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-RP-i1-GGUF/resolve/main/Swallow-70b-RP.i1-Q5_K_S.gguf) | i1-Q5_K_S | 47.7 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-70b-RP-i1-GGUF/resolve/main/Swallow-70b-RP.i1-Q5_K_M.gguf) | i1-Q5_K_M | 49.0 | |
| [PART 1](https://huggingface.co/mradermacher/Swallow-70b-RP-i1-GGUF/resolve/main/Swallow-70b-RP.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Swallow-70b-RP-i1-GGUF/resolve/main/Swallow-70b-RP.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 56.8 | 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.
<!-- end -->
|
mradermacher/13B-Chimera-GGUF
|
mradermacher
| 2024-05-06T05:13:49Z | 34 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"cot",
"vicuna",
"uncensored",
"merge",
"mix",
"gptq",
"en",
"base_model:digitous/13B-Chimera",
"base_model:quantized:digitous/13B-Chimera",
"endpoints_compatible",
"region:us"
] | null | 2024-04-05T23:11:33Z |
---
base_model: digitous/13B-Chimera
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- llama
- cot
- vicuna
- uncensored
- merge
- mix
- gptq
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/digitous/13B-Chimera
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/13B-Chimera-GGUF/resolve/main/13B-Chimera.Q2_K.gguf) | Q2_K | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/13B-Chimera-GGUF/resolve/main/13B-Chimera.IQ3_XS.gguf) | IQ3_XS | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/13B-Chimera-GGUF/resolve/main/13B-Chimera.IQ3_S.gguf) | IQ3_S | 5.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/13B-Chimera-GGUF/resolve/main/13B-Chimera.Q3_K_S.gguf) | Q3_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/13B-Chimera-GGUF/resolve/main/13B-Chimera.IQ3_M.gguf) | IQ3_M | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/13B-Chimera-GGUF/resolve/main/13B-Chimera.Q3_K_M.gguf) | Q3_K_M | 6.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/13B-Chimera-GGUF/resolve/main/13B-Chimera.Q3_K_L.gguf) | Q3_K_L | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/13B-Chimera-GGUF/resolve/main/13B-Chimera.IQ4_XS.gguf) | IQ4_XS | 7.1 | |
| [GGUF](https://huggingface.co/mradermacher/13B-Chimera-GGUF/resolve/main/13B-Chimera.Q4_K_S.gguf) | Q4_K_S | 7.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/13B-Chimera-GGUF/resolve/main/13B-Chimera.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/13B-Chimera-GGUF/resolve/main/13B-Chimera.Q5_K_S.gguf) | Q5_K_S | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/13B-Chimera-GGUF/resolve/main/13B-Chimera.Q5_K_M.gguf) | Q5_K_M | 9.3 | |
| [GGUF](https://huggingface.co/mradermacher/13B-Chimera-GGUF/resolve/main/13B-Chimera.Q6_K.gguf) | Q6_K | 10.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/13B-Chimera-GGUF/resolve/main/13B-Chimera.Q8_0.gguf) | Q8_0 | 13.9 | 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
## 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.
<!-- end -->
|
mradermacher/LemonadeRP-4.5.3-11B-GGUF
|
mradermacher
| 2024-05-06T05:13:46Z | 6 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:mpasila/LemonadeRP-4.5.3-11B",
"base_model:quantized:mpasila/LemonadeRP-4.5.3-11B",
"endpoints_compatible",
"region:us"
] | null | 2024-04-06T00:30:17Z |
---
base_model: mpasila/LemonadeRP-4.5.3-11B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/mpasila/LemonadeRP-4.5.3-11B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/LemonadeRP-4.5.3-11B-GGUF/resolve/main/LemonadeRP-4.5.3-11B.Q2_K.gguf) | Q2_K | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/LemonadeRP-4.5.3-11B-GGUF/resolve/main/LemonadeRP-4.5.3-11B.IQ3_XS.gguf) | IQ3_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/LemonadeRP-4.5.3-11B-GGUF/resolve/main/LemonadeRP-4.5.3-11B.Q3_K_S.gguf) | Q3_K_S | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/LemonadeRP-4.5.3-11B-GGUF/resolve/main/LemonadeRP-4.5.3-11B.IQ3_S.gguf) | IQ3_S | 4.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/LemonadeRP-4.5.3-11B-GGUF/resolve/main/LemonadeRP-4.5.3-11B.IQ3_M.gguf) | IQ3_M | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/LemonadeRP-4.5.3-11B-GGUF/resolve/main/LemonadeRP-4.5.3-11B.Q3_K_M.gguf) | Q3_K_M | 5.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/LemonadeRP-4.5.3-11B-GGUF/resolve/main/LemonadeRP-4.5.3-11B.Q3_K_L.gguf) | Q3_K_L | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/LemonadeRP-4.5.3-11B-GGUF/resolve/main/LemonadeRP-4.5.3-11B.IQ4_XS.gguf) | IQ4_XS | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/LemonadeRP-4.5.3-11B-GGUF/resolve/main/LemonadeRP-4.5.3-11B.Q4_K_S.gguf) | Q4_K_S | 6.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/LemonadeRP-4.5.3-11B-GGUF/resolve/main/LemonadeRP-4.5.3-11B.Q4_K_M.gguf) | Q4_K_M | 6.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/LemonadeRP-4.5.3-11B-GGUF/resolve/main/LemonadeRP-4.5.3-11B.Q5_K_S.gguf) | Q5_K_S | 7.5 | |
| [GGUF](https://huggingface.co/mradermacher/LemonadeRP-4.5.3-11B-GGUF/resolve/main/LemonadeRP-4.5.3-11B.Q5_K_M.gguf) | Q5_K_M | 7.7 | |
| [GGUF](https://huggingface.co/mradermacher/LemonadeRP-4.5.3-11B-GGUF/resolve/main/LemonadeRP-4.5.3-11B.Q6_K.gguf) | Q6_K | 8.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/LemonadeRP-4.5.3-11B-GGUF/resolve/main/LemonadeRP-4.5.3-11B.Q8_0.gguf) | Q8_0 | 11.5 | 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
## 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.
<!-- end -->
|
mradermacher/mixtral-8x1.5B-GGUF
|
mradermacher
| 2024-05-06T05:13:34Z | 96 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:sanchit-gandhi/mixtral-8x1.5B",
"base_model:quantized:sanchit-gandhi/mixtral-8x1.5B",
"endpoints_compatible",
"region:us"
] | null | 2024-04-06T04:02:35Z |
---
base_model: sanchit-gandhi/mixtral-8x1.5B
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/sanchit-gandhi/mixtral-8x1.5B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/mixtral-8x1.5B-GGUF/resolve/main/mixtral-8x1.5B.Q2_K.gguf) | Q2_K | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/mixtral-8x1.5B-GGUF/resolve/main/mixtral-8x1.5B.IQ3_XS.gguf) | IQ3_XS | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/mixtral-8x1.5B-GGUF/resolve/main/mixtral-8x1.5B.IQ3_S.gguf) | IQ3_S | 4.1 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/mixtral-8x1.5B-GGUF/resolve/main/mixtral-8x1.5B.Q3_K_S.gguf) | Q3_K_S | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/mixtral-8x1.5B-GGUF/resolve/main/mixtral-8x1.5B.IQ3_M.gguf) | IQ3_M | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/mixtral-8x1.5B-GGUF/resolve/main/mixtral-8x1.5B.Q3_K_M.gguf) | Q3_K_M | 4.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/mixtral-8x1.5B-GGUF/resolve/main/mixtral-8x1.5B.Q3_K_L.gguf) | Q3_K_L | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/mixtral-8x1.5B-GGUF/resolve/main/mixtral-8x1.5B.IQ4_XS.gguf) | IQ4_XS | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/mixtral-8x1.5B-GGUF/resolve/main/mixtral-8x1.5B.Q4_K_S.gguf) | Q4_K_S | 5.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/mixtral-8x1.5B-GGUF/resolve/main/mixtral-8x1.5B.Q4_K_M.gguf) | Q4_K_M | 5.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/mixtral-8x1.5B-GGUF/resolve/main/mixtral-8x1.5B.Q5_K_S.gguf) | Q5_K_S | 6.3 | |
| [GGUF](https://huggingface.co/mradermacher/mixtral-8x1.5B-GGUF/resolve/main/mixtral-8x1.5B.Q5_K_M.gguf) | Q5_K_M | 6.4 | |
| [GGUF](https://huggingface.co/mradermacher/mixtral-8x1.5B-GGUF/resolve/main/mixtral-8x1.5B.Q6_K.gguf) | Q6_K | 7.5 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/mixtral-8x1.5B-GGUF/resolve/main/mixtral-8x1.5B.Q8_0.gguf) | Q8_0 | 9.6 | 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
## 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.
<!-- end -->
|
mradermacher/prometheus-8x7b-v2.0-1-pp-GGUF
|
mradermacher
| 2024-05-06T05:13:24Z | 4 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:kaist-ai/prometheus-8x7b-v2.0-1-pp",
"base_model:quantized:kaist-ai/prometheus-8x7b-v2.0-1-pp",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-06T05:27:51Z |
---
base_model: kaist-ai/prometheus-8x7b-v2.0-1-pp
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/kaist-ai/prometheus-8x7b-v2.0-1-pp
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/prometheus-8x7b-v2.0-1-pp-GGUF/resolve/main/prometheus-8x7b-v2.0-1-pp.Q2_K.gguf) | Q2_K | 17.4 | |
| [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-1-pp-GGUF/resolve/main/prometheus-8x7b-v2.0-1-pp.IQ3_XS.gguf) | IQ3_XS | 19.4 | |
| [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-1-pp-GGUF/resolve/main/prometheus-8x7b-v2.0-1-pp.IQ3_S.gguf) | IQ3_S | 20.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-1-pp-GGUF/resolve/main/prometheus-8x7b-v2.0-1-pp.Q3_K_S.gguf) | Q3_K_S | 20.5 | |
| [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-1-pp-GGUF/resolve/main/prometheus-8x7b-v2.0-1-pp.IQ3_M.gguf) | IQ3_M | 21.5 | |
| [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-1-pp-GGUF/resolve/main/prometheus-8x7b-v2.0-1-pp.Q3_K_M.gguf) | Q3_K_M | 22.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-1-pp-GGUF/resolve/main/prometheus-8x7b-v2.0-1-pp.Q3_K_L.gguf) | Q3_K_L | 24.3 | |
| [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-1-pp-GGUF/resolve/main/prometheus-8x7b-v2.0-1-pp.IQ4_XS.gguf) | IQ4_XS | 25.5 | |
| [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-1-pp-GGUF/resolve/main/prometheus-8x7b-v2.0-1-pp.Q4_K_S.gguf) | Q4_K_S | 26.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-1-pp-GGUF/resolve/main/prometheus-8x7b-v2.0-1-pp.Q4_K_M.gguf) | Q4_K_M | 28.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-1-pp-GGUF/resolve/main/prometheus-8x7b-v2.0-1-pp.Q5_K_S.gguf) | Q5_K_S | 32.3 | |
| [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-1-pp-GGUF/resolve/main/prometheus-8x7b-v2.0-1-pp.Q5_K_M.gguf) | Q5_K_M | 33.3 | |
| [GGUF](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-1-pp-GGUF/resolve/main/prometheus-8x7b-v2.0-1-pp.Q6_K.gguf) | Q6_K | 38.5 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-1-pp-GGUF/resolve/main/prometheus-8x7b-v2.0-1-pp.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/prometheus-8x7b-v2.0-1-pp-GGUF/resolve/main/prometheus-8x7b-v2.0-1-pp.Q8_0.gguf.part2of2) | Q8_0 | 49.7 | 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
## 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.
<!-- end -->
|
mradermacher/pandafish-3-7B-32k-GGUF
|
mradermacher
| 2024-05-06T05:13:16Z | 1 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:ichigoberry/pandafish-3-7B-32k",
"base_model:quantized:ichigoberry/pandafish-3-7B-32k",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-06T06:14:55Z |
---
base_model: ichigoberry/pandafish-3-7B-32k
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/ichigoberry/pandafish-3-7B-32k
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/pandafish-3-7B-32k-GGUF/resolve/main/pandafish-3-7B-32k.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-3-7B-32k-GGUF/resolve/main/pandafish-3-7B-32k.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-3-7B-32k-GGUF/resolve/main/pandafish-3-7B-32k.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-3-7B-32k-GGUF/resolve/main/pandafish-3-7B-32k.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/pandafish-3-7B-32k-GGUF/resolve/main/pandafish-3-7B-32k.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-3-7B-32k-GGUF/resolve/main/pandafish-3-7B-32k.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/pandafish-3-7B-32k-GGUF/resolve/main/pandafish-3-7B-32k.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-3-7B-32k-GGUF/resolve/main/pandafish-3-7B-32k.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-3-7B-32k-GGUF/resolve/main/pandafish-3-7B-32k.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/pandafish-3-7B-32k-GGUF/resolve/main/pandafish-3-7B-32k.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/pandafish-3-7B-32k-GGUF/resolve/main/pandafish-3-7B-32k.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-3-7B-32k-GGUF/resolve/main/pandafish-3-7B-32k.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/pandafish-3-7B-32k-GGUF/resolve/main/pandafish-3-7B-32k.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/pandafish-3-7B-32k-GGUF/resolve/main/pandafish-3-7B-32k.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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.
<!-- end -->
|
mradermacher/bophades-mistral-truthy-DPO-7B-GGUF
|
mradermacher
| 2024-05-06T05:13:05Z | 16 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"dataset:jondurbin/truthy-dpo-v0.1",
"base_model:nbeerbower/bophades-mistral-truthy-DPO-7B",
"base_model:quantized:nbeerbower/bophades-mistral-truthy-DPO-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-06T08:25:50Z |
---
base_model: nbeerbower/bophades-mistral-truthy-DPO-7B
datasets:
- jondurbin/truthy-dpo-v0.1
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/nbeerbower/bophades-mistral-truthy-DPO-7B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/bophades-mistral-truthy-DPO-7B-GGUF/resolve/main/bophades-mistral-truthy-DPO-7B.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/bophades-mistral-truthy-DPO-7B-GGUF/resolve/main/bophades-mistral-truthy-DPO-7B.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/bophades-mistral-truthy-DPO-7B-GGUF/resolve/main/bophades-mistral-truthy-DPO-7B.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/bophades-mistral-truthy-DPO-7B-GGUF/resolve/main/bophades-mistral-truthy-DPO-7B.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/bophades-mistral-truthy-DPO-7B-GGUF/resolve/main/bophades-mistral-truthy-DPO-7B.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/bophades-mistral-truthy-DPO-7B-GGUF/resolve/main/bophades-mistral-truthy-DPO-7B.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/bophades-mistral-truthy-DPO-7B-GGUF/resolve/main/bophades-mistral-truthy-DPO-7B.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/bophades-mistral-truthy-DPO-7B-GGUF/resolve/main/bophades-mistral-truthy-DPO-7B.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/bophades-mistral-truthy-DPO-7B-GGUF/resolve/main/bophades-mistral-truthy-DPO-7B.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/bophades-mistral-truthy-DPO-7B-GGUF/resolve/main/bophades-mistral-truthy-DPO-7B.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/bophades-mistral-truthy-DPO-7B-GGUF/resolve/main/bophades-mistral-truthy-DPO-7B.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/bophades-mistral-truthy-DPO-7B-GGUF/resolve/main/bophades-mistral-truthy-DPO-7B.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/bophades-mistral-truthy-DPO-7B-GGUF/resolve/main/bophades-mistral-truthy-DPO-7B.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/bophades-mistral-truthy-DPO-7B-GGUF/resolve/main/bophades-mistral-truthy-DPO-7B.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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.
<!-- end -->
|
mradermacher/Pioneer-2x7B-GGUF
|
mradermacher
| 2024-05-06T05:12:46Z | 78 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:hibana2077/Pioneer-2x7B",
"base_model:quantized:hibana2077/Pioneer-2x7B",
"endpoints_compatible",
"region:us"
] | null | 2024-04-06T10:24:47Z |
---
base_model: hibana2077/Pioneer-2x7B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/hibana2077/Pioneer-2x7B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/Pioneer-2x7B-GGUF/resolve/main/Pioneer-2x7B.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Pioneer-2x7B-GGUF/resolve/main/Pioneer-2x7B.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Pioneer-2x7B-GGUF/resolve/main/Pioneer-2x7B.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Pioneer-2x7B-GGUF/resolve/main/Pioneer-2x7B.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Pioneer-2x7B-GGUF/resolve/main/Pioneer-2x7B.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Pioneer-2x7B-GGUF/resolve/main/Pioneer-2x7B.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Pioneer-2x7B-GGUF/resolve/main/Pioneer-2x7B.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Pioneer-2x7B-GGUF/resolve/main/Pioneer-2x7B.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Pioneer-2x7B-GGUF/resolve/main/Pioneer-2x7B.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Pioneer-2x7B-GGUF/resolve/main/Pioneer-2x7B.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Pioneer-2x7B-GGUF/resolve/main/Pioneer-2x7B.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Pioneer-2x7B-GGUF/resolve/main/Pioneer-2x7B.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Pioneer-2x7B-GGUF/resolve/main/Pioneer-2x7B.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Pioneer-2x7B-GGUF/resolve/main/Pioneer-2x7B.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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.
<!-- end -->
|
mradermacher/Wittgenbot-7B-GGUF
|
mradermacher
| 2024-05-06T05:12:38Z | 6 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:descartesevildemon/Wittgenbot-7B",
"base_model:quantized:descartesevildemon/Wittgenbot-7B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-06T10:54:34Z |
---
base_model: descartesevildemon/Wittgenbot-7B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags: []
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/descartesevildemon/Wittgenbot-7B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/Wittgenbot-7B-GGUF/resolve/main/Wittgenbot-7B.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Wittgenbot-7B-GGUF/resolve/main/Wittgenbot-7B.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Wittgenbot-7B-GGUF/resolve/main/Wittgenbot-7B.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Wittgenbot-7B-GGUF/resolve/main/Wittgenbot-7B.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Wittgenbot-7B-GGUF/resolve/main/Wittgenbot-7B.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Wittgenbot-7B-GGUF/resolve/main/Wittgenbot-7B.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Wittgenbot-7B-GGUF/resolve/main/Wittgenbot-7B.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Wittgenbot-7B-GGUF/resolve/main/Wittgenbot-7B.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Wittgenbot-7B-GGUF/resolve/main/Wittgenbot-7B.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Wittgenbot-7B-GGUF/resolve/main/Wittgenbot-7B.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Wittgenbot-7B-GGUF/resolve/main/Wittgenbot-7B.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Wittgenbot-7B-GGUF/resolve/main/Wittgenbot-7B.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Wittgenbot-7B-GGUF/resolve/main/Wittgenbot-7B.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Wittgenbot-7B-GGUF/resolve/main/Wittgenbot-7B.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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.
<!-- end -->
|
mradermacher/BuRPInfinWestLakev2-IreneRP-Neural-7B-slerp-GGUF
|
mradermacher
| 2024-05-06T05:12:36Z | 5 | 1 |
transformers
|
[
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"Smuggling1710/WestLakev2-IreneRP-Neural-7B-slerp",
"jeiku/BuRPInfinity_9B",
"en",
"base_model:Smuggling1710/BuRPInfinWestLakev2-IreneRP-Neural-7B-slerp",
"base_model:quantized:Smuggling1710/BuRPInfinWestLakev2-IreneRP-Neural-7B-slerp",
"endpoints_compatible",
"region:us"
] | null | 2024-04-06T11:14:58Z |
---
base_model: Smuggling1710/BuRPInfinWestLakev2-IreneRP-Neural-7B-slerp
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- Smuggling1710/WestLakev2-IreneRP-Neural-7B-slerp
- jeiku/BuRPInfinity_9B
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Smuggling1710/BuRPInfinWestLakev2-IreneRP-Neural-7B-slerp
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/BuRPInfinWestLakev2-IreneRP-Neural-7B-slerp-GGUF/resolve/main/BuRPInfinWestLakev2-IreneRP-Neural-7B-slerp.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/BuRPInfinWestLakev2-IreneRP-Neural-7B-slerp-GGUF/resolve/main/BuRPInfinWestLakev2-IreneRP-Neural-7B-slerp.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/BuRPInfinWestLakev2-IreneRP-Neural-7B-slerp-GGUF/resolve/main/BuRPInfinWestLakev2-IreneRP-Neural-7B-slerp.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/BuRPInfinWestLakev2-IreneRP-Neural-7B-slerp-GGUF/resolve/main/BuRPInfinWestLakev2-IreneRP-Neural-7B-slerp.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/BuRPInfinWestLakev2-IreneRP-Neural-7B-slerp-GGUF/resolve/main/BuRPInfinWestLakev2-IreneRP-Neural-7B-slerp.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/BuRPInfinWestLakev2-IreneRP-Neural-7B-slerp-GGUF/resolve/main/BuRPInfinWestLakev2-IreneRP-Neural-7B-slerp.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/BuRPInfinWestLakev2-IreneRP-Neural-7B-slerp-GGUF/resolve/main/BuRPInfinWestLakev2-IreneRP-Neural-7B-slerp.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/BuRPInfinWestLakev2-IreneRP-Neural-7B-slerp-GGUF/resolve/main/BuRPInfinWestLakev2-IreneRP-Neural-7B-slerp.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/BuRPInfinWestLakev2-IreneRP-Neural-7B-slerp-GGUF/resolve/main/BuRPInfinWestLakev2-IreneRP-Neural-7B-slerp.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/BuRPInfinWestLakev2-IreneRP-Neural-7B-slerp-GGUF/resolve/main/BuRPInfinWestLakev2-IreneRP-Neural-7B-slerp.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/BuRPInfinWestLakev2-IreneRP-Neural-7B-slerp-GGUF/resolve/main/BuRPInfinWestLakev2-IreneRP-Neural-7B-slerp.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/BuRPInfinWestLakev2-IreneRP-Neural-7B-slerp-GGUF/resolve/main/BuRPInfinWestLakev2-IreneRP-Neural-7B-slerp.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/BuRPInfinWestLakev2-IreneRP-Neural-7B-slerp-GGUF/resolve/main/BuRPInfinWestLakev2-IreneRP-Neural-7B-slerp.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/BuRPInfinWestLakev2-IreneRP-Neural-7B-slerp-GGUF/resolve/main/BuRPInfinWestLakev2-IreneRP-Neural-7B-slerp.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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.
<!-- end -->
|
mradermacher/Swallow-MS-7b-v0.1-ChatMathSkill-GGUF
|
mradermacher
| 2024-05-06T05:12:26Z | 1 | 0 |
transformers
|
[
"transformers",
"gguf",
"SkillEnhanced",
"mistral",
"en",
"base_model:HachiML/Swallow-MS-7b-v0.1-ChatMathSkill",
"base_model:quantized:HachiML/Swallow-MS-7b-v0.1-ChatMathSkill",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-06T11:24:30Z |
---
base_model: HachiML/Swallow-MS-7b-v0.1-ChatMathSkill
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- SkillEnhanced
- mistral
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/HachiML/Swallow-MS-7b-v0.1-ChatMathSkill
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/Swallow-MS-7b-v0.1-ChatMathSkill-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatMathSkill.Q2_K.gguf) | Q2_K | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatMathSkill-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatMathSkill.IQ3_XS.gguf) | IQ3_XS | 3.2 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatMathSkill-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatMathSkill.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatMathSkill-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatMathSkill.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatMathSkill-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatMathSkill.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatMathSkill-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatMathSkill.Q3_K_M.gguf) | Q3_K_M | 3.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatMathSkill-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatMathSkill.Q3_K_L.gguf) | Q3_K_L | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatMathSkill-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatMathSkill.IQ4_XS.gguf) | IQ4_XS | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatMathSkill-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatMathSkill.Q4_K_S.gguf) | Q4_K_S | 4.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatMathSkill-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatMathSkill.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatMathSkill-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatMathSkill.Q5_K_S.gguf) | Q5_K_S | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatMathSkill-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatMathSkill.Q5_K_M.gguf) | Q5_K_M | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatMathSkill-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatMathSkill.Q6_K.gguf) | Q6_K | 6.1 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Swallow-MS-7b-v0.1-ChatMathSkill-GGUF/resolve/main/Swallow-MS-7b-v0.1-ChatMathSkill.Q8_0.gguf) | Q8_0 | 7.9 | 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
## 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.
<!-- end -->
|
mradermacher/Alpacino30b-GGUF
|
mradermacher
| 2024-05-06T05:12:21Z | 71 | 0 |
transformers
|
[
"transformers",
"gguf",
"alpaca",
"en",
"base_model:digitous/Alpacino30b",
"base_model:quantized:digitous/Alpacino30b",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-04-06T11:56:58Z |
---
base_model: digitous/Alpacino30b
language:
- en
library_name: transformers
license: other
quantized_by: mradermacher
tags:
- alpaca
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/digitous/Alpacino30b
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Alpacino30b-i1-GGUF
## 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/Alpacino30b-GGUF/resolve/main/Alpacino30b.Q2_K.gguf) | Q2_K | 12.1 | |
| [GGUF](https://huggingface.co/mradermacher/Alpacino30b-GGUF/resolve/main/Alpacino30b.IQ3_XS.gguf) | IQ3_XS | 13.4 | |
| [GGUF](https://huggingface.co/mradermacher/Alpacino30b-GGUF/resolve/main/Alpacino30b.IQ3_S.gguf) | IQ3_S | 14.2 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Alpacino30b-GGUF/resolve/main/Alpacino30b.Q3_K_S.gguf) | Q3_K_S | 14.2 | |
| [GGUF](https://huggingface.co/mradermacher/Alpacino30b-GGUF/resolve/main/Alpacino30b.IQ3_M.gguf) | IQ3_M | 15.0 | |
| [GGUF](https://huggingface.co/mradermacher/Alpacino30b-GGUF/resolve/main/Alpacino30b.Q3_K_M.gguf) | Q3_K_M | 15.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Alpacino30b-GGUF/resolve/main/Alpacino30b.Q3_K_L.gguf) | Q3_K_L | 17.4 | |
| [GGUF](https://huggingface.co/mradermacher/Alpacino30b-GGUF/resolve/main/Alpacino30b.IQ4_XS.gguf) | IQ4_XS | 17.6 | |
| [GGUF](https://huggingface.co/mradermacher/Alpacino30b-GGUF/resolve/main/Alpacino30b.Q4_K_S.gguf) | Q4_K_S | 18.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Alpacino30b-GGUF/resolve/main/Alpacino30b.Q4_K_M.gguf) | Q4_K_M | 19.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Alpacino30b-GGUF/resolve/main/Alpacino30b.Q5_K_S.gguf) | Q5_K_S | 22.5 | |
| [GGUF](https://huggingface.co/mradermacher/Alpacino30b-GGUF/resolve/main/Alpacino30b.Q5_K_M.gguf) | Q5_K_M | 23.1 | |
| [GGUF](https://huggingface.co/mradermacher/Alpacino30b-GGUF/resolve/main/Alpacino30b.Q6_K.gguf) | Q6_K | 26.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Alpacino30b-GGUF/resolve/main/Alpacino30b.Q8_0.gguf) | Q8_0 | 34.7 | 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
## 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.
<!-- end -->
|
mradermacher/GPT4-X-Alpasta-30b-GGUF
|
mradermacher
| 2024-05-06T05:12:15Z | 39 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:MetaIX/GPT4-X-Alpasta-30b",
"base_model:quantized:MetaIX/GPT4-X-Alpasta-30b",
"endpoints_compatible",
"region:us"
] | null | 2024-04-06T14:12:48Z |
---
base_model: MetaIX/GPT4-X-Alpasta-30b
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/MetaIX/GPT4-X-Alpasta-30b
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/GPT4-X-Alpasta-30b-i1-GGUF
## 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/GPT4-X-Alpasta-30b-GGUF/resolve/main/GPT4-X-Alpasta-30b.Q2_K.gguf) | Q2_K | 12.1 | |
| [GGUF](https://huggingface.co/mradermacher/GPT4-X-Alpasta-30b-GGUF/resolve/main/GPT4-X-Alpasta-30b.IQ3_XS.gguf) | IQ3_XS | 13.4 | |
| [GGUF](https://huggingface.co/mradermacher/GPT4-X-Alpasta-30b-GGUF/resolve/main/GPT4-X-Alpasta-30b.IQ3_S.gguf) | IQ3_S | 14.2 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/GPT4-X-Alpasta-30b-GGUF/resolve/main/GPT4-X-Alpasta-30b.Q3_K_S.gguf) | Q3_K_S | 14.2 | |
| [GGUF](https://huggingface.co/mradermacher/GPT4-X-Alpasta-30b-GGUF/resolve/main/GPT4-X-Alpasta-30b.IQ3_M.gguf) | IQ3_M | 15.0 | |
| [GGUF](https://huggingface.co/mradermacher/GPT4-X-Alpasta-30b-GGUF/resolve/main/GPT4-X-Alpasta-30b.Q3_K_M.gguf) | Q3_K_M | 15.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/GPT4-X-Alpasta-30b-GGUF/resolve/main/GPT4-X-Alpasta-30b.Q3_K_L.gguf) | Q3_K_L | 17.4 | |
| [GGUF](https://huggingface.co/mradermacher/GPT4-X-Alpasta-30b-GGUF/resolve/main/GPT4-X-Alpasta-30b.IQ4_XS.gguf) | IQ4_XS | 17.6 | |
| [GGUF](https://huggingface.co/mradermacher/GPT4-X-Alpasta-30b-GGUF/resolve/main/GPT4-X-Alpasta-30b.Q4_K_S.gguf) | Q4_K_S | 18.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/GPT4-X-Alpasta-30b-GGUF/resolve/main/GPT4-X-Alpasta-30b.Q4_K_M.gguf) | Q4_K_M | 19.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/GPT4-X-Alpasta-30b-GGUF/resolve/main/GPT4-X-Alpasta-30b.Q5_K_S.gguf) | Q5_K_S | 22.5 | |
| [GGUF](https://huggingface.co/mradermacher/GPT4-X-Alpasta-30b-GGUF/resolve/main/GPT4-X-Alpasta-30b.Q5_K_M.gguf) | Q5_K_M | 23.1 | |
| [GGUF](https://huggingface.co/mradermacher/GPT4-X-Alpasta-30b-GGUF/resolve/main/GPT4-X-Alpasta-30b.Q6_K.gguf) | Q6_K | 26.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/GPT4-X-Alpasta-30b-GGUF/resolve/main/GPT4-X-Alpasta-30b.Q8_0.gguf) | Q8_0 | 34.7 | 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
## 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.
<!-- end -->
|
mradermacher/Enterredaas-33b-GGUF
|
mradermacher
| 2024-05-06T05:12:01Z | 15 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:Aeala/Enterredaas-33b",
"base_model:quantized:Aeala/Enterredaas-33b",
"endpoints_compatible",
"region:us"
] | null | 2024-04-06T16:27:16Z |
---
base_model: Aeala/Enterredaas-33b
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Aeala/Enterredaas-33b
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Enterredaas-33b-i1-GGUF
## 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/Enterredaas-33b-GGUF/resolve/main/Enterredaas-33b.Q2_K.gguf) | Q2_K | 12.1 | |
| [GGUF](https://huggingface.co/mradermacher/Enterredaas-33b-GGUF/resolve/main/Enterredaas-33b.IQ3_XS.gguf) | IQ3_XS | 13.4 | |
| [GGUF](https://huggingface.co/mradermacher/Enterredaas-33b-GGUF/resolve/main/Enterredaas-33b.IQ3_S.gguf) | IQ3_S | 14.2 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Enterredaas-33b-GGUF/resolve/main/Enterredaas-33b.Q3_K_S.gguf) | Q3_K_S | 14.2 | |
| [GGUF](https://huggingface.co/mradermacher/Enterredaas-33b-GGUF/resolve/main/Enterredaas-33b.IQ3_M.gguf) | IQ3_M | 15.0 | |
| [GGUF](https://huggingface.co/mradermacher/Enterredaas-33b-GGUF/resolve/main/Enterredaas-33b.Q3_K_M.gguf) | Q3_K_M | 15.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Enterredaas-33b-GGUF/resolve/main/Enterredaas-33b.Q3_K_L.gguf) | Q3_K_L | 17.4 | |
| [GGUF](https://huggingface.co/mradermacher/Enterredaas-33b-GGUF/resolve/main/Enterredaas-33b.IQ4_XS.gguf) | IQ4_XS | 17.6 | |
| [GGUF](https://huggingface.co/mradermacher/Enterredaas-33b-GGUF/resolve/main/Enterredaas-33b.Q4_K_S.gguf) | Q4_K_S | 18.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Enterredaas-33b-GGUF/resolve/main/Enterredaas-33b.Q4_K_M.gguf) | Q4_K_M | 19.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Enterredaas-33b-GGUF/resolve/main/Enterredaas-33b.Q5_K_S.gguf) | Q5_K_S | 22.5 | |
| [GGUF](https://huggingface.co/mradermacher/Enterredaas-33b-GGUF/resolve/main/Enterredaas-33b.Q5_K_M.gguf) | Q5_K_M | 23.1 | |
| [GGUF](https://huggingface.co/mradermacher/Enterredaas-33b-GGUF/resolve/main/Enterredaas-33b.Q6_K.gguf) | Q6_K | 26.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Enterredaas-33b-GGUF/resolve/main/Enterredaas-33b.Q8_0.gguf) | Q8_0 | 34.7 | 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
## 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.
<!-- end -->
|
mradermacher/sixtyoneeighty-4x7B-v1-GGUF
|
mradermacher
| 2024-05-06T05:11:55Z | 14 | 0 |
transformers
|
[
"transformers",
"gguf",
"moe",
"frankenmoe",
"merge",
"mergekit",
"lazymergekit",
"jambroz/sixtyoneeighty-7b-chat",
"en",
"base_model:jambroz/sixtyoneeighty-4x7B-v1",
"base_model:quantized:jambroz/sixtyoneeighty-4x7B-v1",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-06T17:17:17Z |
---
base_model: jambroz/sixtyoneeighty-4x7B-v1
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- moe
- frankenmoe
- merge
- mergekit
- lazymergekit
- jambroz/sixtyoneeighty-7b-chat
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/jambroz/sixtyoneeighty-4x7B-v1
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/sixtyoneeighty-4x7B-v1-GGUF/resolve/main/sixtyoneeighty-4x7B-v1.Q2_K.gguf) | Q2_K | 8.9 | |
| [GGUF](https://huggingface.co/mradermacher/sixtyoneeighty-4x7B-v1-GGUF/resolve/main/sixtyoneeighty-4x7B-v1.IQ3_XS.gguf) | IQ3_XS | 10.0 | |
| [GGUF](https://huggingface.co/mradermacher/sixtyoneeighty-4x7B-v1-GGUF/resolve/main/sixtyoneeighty-4x7B-v1.Q3_K_S.gguf) | Q3_K_S | 10.5 | |
| [GGUF](https://huggingface.co/mradermacher/sixtyoneeighty-4x7B-v1-GGUF/resolve/main/sixtyoneeighty-4x7B-v1.IQ3_S.gguf) | IQ3_S | 10.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/sixtyoneeighty-4x7B-v1-GGUF/resolve/main/sixtyoneeighty-4x7B-v1.IQ3_M.gguf) | IQ3_M | 10.7 | |
| [GGUF](https://huggingface.co/mradermacher/sixtyoneeighty-4x7B-v1-GGUF/resolve/main/sixtyoneeighty-4x7B-v1.Q3_K_M.gguf) | Q3_K_M | 11.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/sixtyoneeighty-4x7B-v1-GGUF/resolve/main/sixtyoneeighty-4x7B-v1.Q3_K_L.gguf) | Q3_K_L | 12.6 | |
| [GGUF](https://huggingface.co/mradermacher/sixtyoneeighty-4x7B-v1-GGUF/resolve/main/sixtyoneeighty-4x7B-v1.IQ4_XS.gguf) | IQ4_XS | 13.1 | |
| [GGUF](https://huggingface.co/mradermacher/sixtyoneeighty-4x7B-v1-GGUF/resolve/main/sixtyoneeighty-4x7B-v1.Q4_K_S.gguf) | Q4_K_S | 13.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/sixtyoneeighty-4x7B-v1-GGUF/resolve/main/sixtyoneeighty-4x7B-v1.Q4_K_M.gguf) | Q4_K_M | 14.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/sixtyoneeighty-4x7B-v1-GGUF/resolve/main/sixtyoneeighty-4x7B-v1.Q5_K_S.gguf) | Q5_K_S | 16.7 | |
| [GGUF](https://huggingface.co/mradermacher/sixtyoneeighty-4x7B-v1-GGUF/resolve/main/sixtyoneeighty-4x7B-v1.Q5_K_M.gguf) | Q5_K_M | 17.2 | |
| [GGUF](https://huggingface.co/mradermacher/sixtyoneeighty-4x7B-v1-GGUF/resolve/main/sixtyoneeighty-4x7B-v1.Q6_K.gguf) | Q6_K | 19.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/sixtyoneeighty-4x7B-v1-GGUF/resolve/main/sixtyoneeighty-4x7B-v1.Q8_0.gguf) | Q8_0 | 25.8 | 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
## 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.
<!-- end -->
|
mradermacher/MermaidMixtral-3x7b-GGUF
|
mradermacher
| 2024-05-06T05:11:50Z | 45 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:TroyDoesAI/MermaidMixtral-3x7b",
"base_model:quantized:TroyDoesAI/MermaidMixtral-3x7b",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-06T17:23:40Z |
---
base_model: TroyDoesAI/MermaidMixtral-3x7b
language:
- en
library_name: transformers
license: cc-by-4.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/TroyDoesAI/MermaidMixtral-3x7b
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/MermaidMixtral-3x7b-GGUF/resolve/main/MermaidMixtral-3x7b.Q2_K.gguf) | Q2_K | 6.9 | |
| [GGUF](https://huggingface.co/mradermacher/MermaidMixtral-3x7b-GGUF/resolve/main/MermaidMixtral-3x7b.IQ3_XS.gguf) | IQ3_XS | 7.7 | |
| [GGUF](https://huggingface.co/mradermacher/MermaidMixtral-3x7b-GGUF/resolve/main/MermaidMixtral-3x7b.Q3_K_S.gguf) | Q3_K_S | 8.1 | |
| [GGUF](https://huggingface.co/mradermacher/MermaidMixtral-3x7b-GGUF/resolve/main/MermaidMixtral-3x7b.IQ3_S.gguf) | IQ3_S | 8.1 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/MermaidMixtral-3x7b-GGUF/resolve/main/MermaidMixtral-3x7b.IQ3_M.gguf) | IQ3_M | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/MermaidMixtral-3x7b-GGUF/resolve/main/MermaidMixtral-3x7b.Q3_K_M.gguf) | Q3_K_M | 9.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/MermaidMixtral-3x7b-GGUF/resolve/main/MermaidMixtral-3x7b.Q3_K_L.gguf) | Q3_K_L | 9.7 | |
| [GGUF](https://huggingface.co/mradermacher/MermaidMixtral-3x7b-GGUF/resolve/main/MermaidMixtral-3x7b.IQ4_XS.gguf) | IQ4_XS | 10.1 | |
| [GGUF](https://huggingface.co/mradermacher/MermaidMixtral-3x7b-GGUF/resolve/main/MermaidMixtral-3x7b.Q4_K_S.gguf) | Q4_K_S | 10.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MermaidMixtral-3x7b-GGUF/resolve/main/MermaidMixtral-3x7b.Q4_K_M.gguf) | Q4_K_M | 11.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MermaidMixtral-3x7b-GGUF/resolve/main/MermaidMixtral-3x7b.Q5_K_S.gguf) | Q5_K_S | 12.8 | |
| [GGUF](https://huggingface.co/mradermacher/MermaidMixtral-3x7b-GGUF/resolve/main/MermaidMixtral-3x7b.Q5_K_M.gguf) | Q5_K_M | 13.2 | |
| [GGUF](https://huggingface.co/mradermacher/MermaidMixtral-3x7b-GGUF/resolve/main/MermaidMixtral-3x7b.Q6_K.gguf) | Q6_K | 15.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/MermaidMixtral-3x7b-GGUF/resolve/main/MermaidMixtral-3x7b.Q8_0.gguf) | Q8_0 | 19.8 | 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
## 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.
<!-- end -->
|
mradermacher/GPT4-x-AlpacaDente2-30b-GGUF
|
mradermacher
| 2024-05-06T05:11:47Z | 88 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:Aeala/GPT4-x-AlpacaDente2-30b",
"base_model:quantized:Aeala/GPT4-x-AlpacaDente2-30b",
"endpoints_compatible",
"region:us"
] | null | 2024-04-06T18:41:58Z |
---
base_model: Aeala/GPT4-x-AlpacaDente2-30b
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Aeala/GPT4-x-AlpacaDente2-30b
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/GPT4-x-AlpacaDente2-30b-i1-GGUF
## 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/GPT4-x-AlpacaDente2-30b-GGUF/resolve/main/GPT4-x-AlpacaDente2-30b.Q2_K.gguf) | Q2_K | 12.1 | |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente2-30b-GGUF/resolve/main/GPT4-x-AlpacaDente2-30b.IQ3_XS.gguf) | IQ3_XS | 13.4 | |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente2-30b-GGUF/resolve/main/GPT4-x-AlpacaDente2-30b.IQ3_S.gguf) | IQ3_S | 14.2 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente2-30b-GGUF/resolve/main/GPT4-x-AlpacaDente2-30b.Q3_K_S.gguf) | Q3_K_S | 14.2 | |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente2-30b-GGUF/resolve/main/GPT4-x-AlpacaDente2-30b.IQ3_M.gguf) | IQ3_M | 15.0 | |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente2-30b-GGUF/resolve/main/GPT4-x-AlpacaDente2-30b.Q3_K_M.gguf) | Q3_K_M | 15.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente2-30b-GGUF/resolve/main/GPT4-x-AlpacaDente2-30b.Q3_K_L.gguf) | Q3_K_L | 17.4 | |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente2-30b-GGUF/resolve/main/GPT4-x-AlpacaDente2-30b.IQ4_XS.gguf) | IQ4_XS | 17.6 | |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente2-30b-GGUF/resolve/main/GPT4-x-AlpacaDente2-30b.Q4_K_S.gguf) | Q4_K_S | 18.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente2-30b-GGUF/resolve/main/GPT4-x-AlpacaDente2-30b.Q4_K_M.gguf) | Q4_K_M | 19.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente2-30b-GGUF/resolve/main/GPT4-x-AlpacaDente2-30b.Q5_K_S.gguf) | Q5_K_S | 22.5 | |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente2-30b-GGUF/resolve/main/GPT4-x-AlpacaDente2-30b.Q5_K_M.gguf) | Q5_K_M | 23.1 | |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente2-30b-GGUF/resolve/main/GPT4-x-AlpacaDente2-30b.Q6_K.gguf) | Q6_K | 26.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/GPT4-x-AlpacaDente2-30b-GGUF/resolve/main/GPT4-x-AlpacaDente2-30b.Q8_0.gguf) | Q8_0 | 34.7 | 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
## 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.
<!-- end -->
|
mradermacher/Alpaca-elina-65b-GGUF
|
mradermacher
| 2024-05-06T05:11:45Z | 5 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:Aeala/Alpaca-elina-65b",
"base_model:quantized:Aeala/Alpaca-elina-65b",
"endpoints_compatible",
"region:us"
] | null | 2024-04-06T19:05:16Z |
---
base_model: Aeala/Alpaca-elina-65b
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Aeala/Alpaca-elina-65b
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Alpaca-elina-65b-i1-GGUF
## 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/Alpaca-elina-65b-GGUF/resolve/main/Alpaca-elina-65b.Q2_K.gguf) | Q2_K | 24.2 | |
| [GGUF](https://huggingface.co/mradermacher/Alpaca-elina-65b-GGUF/resolve/main/Alpaca-elina-65b.IQ3_XS.gguf) | IQ3_XS | 26.7 | |
| [GGUF](https://huggingface.co/mradermacher/Alpaca-elina-65b-GGUF/resolve/main/Alpaca-elina-65b.IQ3_S.gguf) | IQ3_S | 28.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Alpaca-elina-65b-GGUF/resolve/main/Alpaca-elina-65b.Q3_K_S.gguf) | Q3_K_S | 28.3 | |
| [GGUF](https://huggingface.co/mradermacher/Alpaca-elina-65b-GGUF/resolve/main/Alpaca-elina-65b.IQ3_M.gguf) | IQ3_M | 29.9 | |
| [GGUF](https://huggingface.co/mradermacher/Alpaca-elina-65b-GGUF/resolve/main/Alpaca-elina-65b.Q3_K_M.gguf) | Q3_K_M | 31.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Alpaca-elina-65b-GGUF/resolve/main/Alpaca-elina-65b.Q3_K_L.gguf) | Q3_K_L | 34.7 | |
| [GGUF](https://huggingface.co/mradermacher/Alpaca-elina-65b-GGUF/resolve/main/Alpaca-elina-65b.IQ4_XS.gguf) | IQ4_XS | 35.1 | |
| [GGUF](https://huggingface.co/mradermacher/Alpaca-elina-65b-GGUF/resolve/main/Alpaca-elina-65b.Q4_K_S.gguf) | Q4_K_S | 37.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Alpaca-elina-65b-GGUF/resolve/main/Alpaca-elina-65b.Q4_K_M.gguf) | Q4_K_M | 39.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Alpaca-elina-65b-GGUF/resolve/main/Alpaca-elina-65b.Q5_K_S.gguf) | Q5_K_S | 45.0 | |
| [GGUF](https://huggingface.co/mradermacher/Alpaca-elina-65b-GGUF/resolve/main/Alpaca-elina-65b.Q5_K_M.gguf) | Q5_K_M | 46.3 | |
| [PART 1](https://huggingface.co/mradermacher/Alpaca-elina-65b-GGUF/resolve/main/Alpaca-elina-65b.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Alpaca-elina-65b-GGUF/resolve/main/Alpaca-elina-65b.Q6_K.gguf.part2of2) | Q6_K | 53.7 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Alpaca-elina-65b-GGUF/resolve/main/Alpaca-elina-65b.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Alpaca-elina-65b-GGUF/resolve/main/Alpaca-elina-65b.Q8_0.gguf.part2of2) | Q8_0 | 69.5 | 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
## 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.
<!-- end -->
|
mradermacher/WinterGoddess-1.4x-70B-L2-GGUF
|
mradermacher
| 2024-05-06T05:11:42Z | 2 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:Sao10K/WinterGoddess-1.4x-70B-L2",
"base_model:quantized:Sao10K/WinterGoddess-1.4x-70B-L2",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-06T19:40:17Z |
---
base_model: Sao10K/WinterGoddess-1.4x-70B-L2
language:
- en
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Sao10K/WinterGoddess-1.4x-70B-L2
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-i1-GGUF
## 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/WinterGoddess-1.4x-70B-L2-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.Q2_K.gguf) | Q2_K | 25.6 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.IQ3_XS.gguf) | IQ3_XS | 28.4 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.IQ3_S.gguf) | IQ3_S | 30.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.Q3_K_S.gguf) | Q3_K_S | 30.0 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.IQ3_M.gguf) | IQ3_M | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.Q3_K_M.gguf) | Q3_K_M | 33.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.Q3_K_L.gguf) | Q3_K_L | 36.2 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.IQ4_XS.gguf) | IQ4_XS | 37.3 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.Q4_K_S.gguf) | Q4_K_S | 39.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.Q4_K_M.gguf) | Q4_K_M | 41.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.Q5_K_S.gguf) | Q5_K_S | 47.6 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.Q5_K_M.gguf) | Q5_K_M | 48.9 | |
| [PART 1](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.Q6_K.gguf.part2of2) | Q6_K | 56.7 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70B-L2-GGUF/resolve/main/WinterGoddess-1.4x-70B-L2.Q8_0.gguf.part2of2) | Q8_0 | 73.4 | 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
## 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.
<!-- end -->
|
mradermacher/Alpacino30b-i1-GGUF
|
mradermacher
| 2024-05-06T05:11:37Z | 215 | 0 |
transformers
|
[
"transformers",
"gguf",
"alpaca",
"en",
"base_model:digitous/Alpacino30b",
"base_model:quantized:digitous/Alpacino30b",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-04-06T21:07:29Z |
---
base_model: digitous/Alpacino30b
language:
- en
library_name: transformers
license: other
quantized_by: mradermacher
tags:
- alpaca
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/digitous/Alpacino30b
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Alpacino30b-GGUF
## 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/Alpacino30b-i1-GGUF/resolve/main/Alpacino30b.i1-IQ1_S.gguf) | i1-IQ1_S | 7.2 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Alpacino30b-i1-GGUF/resolve/main/Alpacino30b.i1-IQ1_M.gguf) | i1-IQ1_M | 7.8 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Alpacino30b-i1-GGUF/resolve/main/Alpacino30b.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/Alpacino30b-i1-GGUF/resolve/main/Alpacino30b.i1-IQ2_XS.gguf) | i1-IQ2_XS | 9.7 | |
| [GGUF](https://huggingface.co/mradermacher/Alpacino30b-i1-GGUF/resolve/main/Alpacino30b.i1-IQ2_S.gguf) | i1-IQ2_S | 10.5 | |
| [GGUF](https://huggingface.co/mradermacher/Alpacino30b-i1-GGUF/resolve/main/Alpacino30b.i1-IQ2_M.gguf) | i1-IQ2_M | 11.3 | |
| [GGUF](https://huggingface.co/mradermacher/Alpacino30b-i1-GGUF/resolve/main/Alpacino30b.i1-Q2_K.gguf) | i1-Q2_K | 12.1 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Alpacino30b-i1-GGUF/resolve/main/Alpacino30b.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 12.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Alpacino30b-i1-GGUF/resolve/main/Alpacino30b.i1-IQ3_XS.gguf) | i1-IQ3_XS | 13.4 | |
| [GGUF](https://huggingface.co/mradermacher/Alpacino30b-i1-GGUF/resolve/main/Alpacino30b.i1-IQ3_S.gguf) | i1-IQ3_S | 14.2 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Alpacino30b-i1-GGUF/resolve/main/Alpacino30b.i1-Q3_K_S.gguf) | i1-Q3_K_S | 14.2 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Alpacino30b-i1-GGUF/resolve/main/Alpacino30b.i1-IQ3_M.gguf) | i1-IQ3_M | 15.0 | |
| [GGUF](https://huggingface.co/mradermacher/Alpacino30b-i1-GGUF/resolve/main/Alpacino30b.i1-Q3_K_M.gguf) | i1-Q3_K_M | 15.9 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Alpacino30b-i1-GGUF/resolve/main/Alpacino30b.i1-Q3_K_L.gguf) | i1-Q3_K_L | 17.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Alpacino30b-i1-GGUF/resolve/main/Alpacino30b.i1-IQ4_XS.gguf) | i1-IQ4_XS | 17.4 | |
| [GGUF](https://huggingface.co/mradermacher/Alpacino30b-i1-GGUF/resolve/main/Alpacino30b.i1-Q4_0.gguf) | i1-Q4_0 | 18.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Alpacino30b-i1-GGUF/resolve/main/Alpacino30b.i1-Q4_K_S.gguf) | i1-Q4_K_S | 18.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Alpacino30b-i1-GGUF/resolve/main/Alpacino30b.i1-Q4_K_M.gguf) | i1-Q4_K_M | 19.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Alpacino30b-i1-GGUF/resolve/main/Alpacino30b.i1-Q5_K_S.gguf) | i1-Q5_K_S | 22.5 | |
| [GGUF](https://huggingface.co/mradermacher/Alpacino30b-i1-GGUF/resolve/main/Alpacino30b.i1-Q5_K_M.gguf) | i1-Q5_K_M | 23.1 | |
| [GGUF](https://huggingface.co/mradermacher/Alpacino30b-i1-GGUF/resolve/main/Alpacino30b.i1-Q6_K.gguf) | i1-Q6_K | 26.8 | 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.
<!-- end -->
|
mradermacher/PandafishHeatherReReloaded-GGUF
|
mradermacher
| 2024-05-06T05:11:22Z | 131 | 0 |
transformers
|
[
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"MysticFoxMagic/HeatherSpell-7b",
"ichigoberry/pandafish-2-7b-32k",
"en",
"base_model:MysticFoxMagic/PandafishHeatherReReloaded",
"base_model:quantized:MysticFoxMagic/PandafishHeatherReReloaded",
"endpoints_compatible",
"region:us"
] | null | 2024-04-07T00:10:05Z |
---
base_model: MysticFoxMagic/PandafishHeatherReReloaded
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- MysticFoxMagic/HeatherSpell-7b
- ichigoberry/pandafish-2-7b-32k
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/MysticFoxMagic/PandafishHeatherReReloaded
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/PandafishHeatherReReloaded-GGUF/resolve/main/PandafishHeatherReReloaded.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReReloaded-GGUF/resolve/main/PandafishHeatherReReloaded.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReReloaded-GGUF/resolve/main/PandafishHeatherReReloaded.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReReloaded-GGUF/resolve/main/PandafishHeatherReReloaded.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReReloaded-GGUF/resolve/main/PandafishHeatherReReloaded.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReReloaded-GGUF/resolve/main/PandafishHeatherReReloaded.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReReloaded-GGUF/resolve/main/PandafishHeatherReReloaded.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReReloaded-GGUF/resolve/main/PandafishHeatherReReloaded.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReReloaded-GGUF/resolve/main/PandafishHeatherReReloaded.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReReloaded-GGUF/resolve/main/PandafishHeatherReReloaded.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReReloaded-GGUF/resolve/main/PandafishHeatherReReloaded.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReReloaded-GGUF/resolve/main/PandafishHeatherReReloaded.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReReloaded-GGUF/resolve/main/PandafishHeatherReReloaded.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReReloaded-GGUF/resolve/main/PandafishHeatherReReloaded.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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.
<!-- end -->
|
mradermacher/PandafishHeatherReloaded-GGUF
|
mradermacher
| 2024-05-06T05:11:11Z | 89 | 0 |
transformers
|
[
"transformers",
"gguf",
"merge",
"mergekit",
"lazymergekit",
"ichigoberry/pandafish-dt-7b",
"MysticFoxMagic/HeatherSpell-7b",
"en",
"base_model:MysticFoxMagic/PandafishHeatherReloaded",
"base_model:quantized:MysticFoxMagic/PandafishHeatherReloaded",
"endpoints_compatible",
"region:us"
] | null | 2024-04-07T01:32:30Z |
---
base_model: MysticFoxMagic/PandafishHeatherReloaded
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- merge
- mergekit
- lazymergekit
- ichigoberry/pandafish-dt-7b
- MysticFoxMagic/HeatherSpell-7b
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/MysticFoxMagic/PandafishHeatherReloaded
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/PandafishHeatherReloaded-GGUF/resolve/main/PandafishHeatherReloaded.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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.
<!-- end -->
|
mradermacher/Tess-72B-v1.5b-GGUF
|
mradermacher
| 2024-05-06T05:11:02Z | 1 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:migtissera/Tess-72B-v1.5b",
"base_model:quantized:migtissera/Tess-72B-v1.5b",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-04-07T03:47:12Z |
---
base_model: migtissera/Tess-72B-v1.5b
language:
- en
library_name: transformers
license: other
license_link: https://huggingface.co/Qwen/Qwen-72B/blob/main/LICENSE
license_name: qwen-72b-licence
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/migtissera/Tess-72B-v1.5b
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Tess-72B-v1.5b-i1-GGUF
## 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/Tess-72B-v1.5b-GGUF/resolve/main/Tess-72B-v1.5b.Q2_K.gguf) | Q2_K | 27.2 | |
| [GGUF](https://huggingface.co/mradermacher/Tess-72B-v1.5b-GGUF/resolve/main/Tess-72B-v1.5b.IQ3_XS.gguf) | IQ3_XS | 30.0 | |
| [GGUF](https://huggingface.co/mradermacher/Tess-72B-v1.5b-GGUF/resolve/main/Tess-72B-v1.5b.IQ3_S.gguf) | IQ3_S | 31.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Tess-72B-v1.5b-GGUF/resolve/main/Tess-72B-v1.5b.Q3_K_S.gguf) | Q3_K_S | 31.7 | |
| [GGUF](https://huggingface.co/mradermacher/Tess-72B-v1.5b-GGUF/resolve/main/Tess-72B-v1.5b.IQ3_M.gguf) | IQ3_M | 33.4 | |
| [GGUF](https://huggingface.co/mradermacher/Tess-72B-v1.5b-GGUF/resolve/main/Tess-72B-v1.5b.Q3_K_M.gguf) | Q3_K_M | 35.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Tess-72B-v1.5b-GGUF/resolve/main/Tess-72B-v1.5b.Q3_K_L.gguf) | Q3_K_L | 38.6 | |
| [GGUF](https://huggingface.co/mradermacher/Tess-72B-v1.5b-GGUF/resolve/main/Tess-72B-v1.5b.IQ4_XS.gguf) | IQ4_XS | 39.2 | |
| [GGUF](https://huggingface.co/mradermacher/Tess-72B-v1.5b-GGUF/resolve/main/Tess-72B-v1.5b.Q4_K_S.gguf) | Q4_K_S | 41.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Tess-72B-v1.5b-GGUF/resolve/main/Tess-72B-v1.5b.Q4_K_M.gguf) | Q4_K_M | 43.9 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/Tess-72B-v1.5b-GGUF/resolve/main/Tess-72B-v1.5b.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Tess-72B-v1.5b-GGUF/resolve/main/Tess-72B-v1.5b.Q5_K_S.gguf.part2of2) | Q5_K_S | 50.0 | |
| [PART 1](https://huggingface.co/mradermacher/Tess-72B-v1.5b-GGUF/resolve/main/Tess-72B-v1.5b.Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Tess-72B-v1.5b-GGUF/resolve/main/Tess-72B-v1.5b.Q5_K_M.gguf.part2of2) | Q5_K_M | 51.4 | |
| [PART 1](https://huggingface.co/mradermacher/Tess-72B-v1.5b-GGUF/resolve/main/Tess-72B-v1.5b.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Tess-72B-v1.5b-GGUF/resolve/main/Tess-72B-v1.5b.Q6_K.gguf.part2of2) | Q6_K | 59.4 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Tess-72B-v1.5b-GGUF/resolve/main/Tess-72B-v1.5b.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Tess-72B-v1.5b-GGUF/resolve/main/Tess-72B-v1.5b.Q8_0.gguf.part2of2) | Q8_0 | 76.9 | 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
## 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.
<!-- end -->
|
mradermacher/Limitless-GGUF
|
mradermacher
| 2024-05-06T05:10:56Z | 116 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:alkahestry/Limitless",
"base_model:quantized:alkahestry/Limitless",
"endpoints_compatible",
"region:us"
] | null | 2024-04-07T04:22:23Z |
---
base_model: alkahestry/Limitless
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/alkahestry/Limitless
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/Limitless-GGUF/resolve/main/Limitless.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Limitless-GGUF/resolve/main/Limitless.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Limitless-GGUF/resolve/main/Limitless.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Limitless-GGUF/resolve/main/Limitless.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Limitless-GGUF/resolve/main/Limitless.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Limitless-GGUF/resolve/main/Limitless.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Limitless-GGUF/resolve/main/Limitless.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Limitless-GGUF/resolve/main/Limitless.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Limitless-GGUF/resolve/main/Limitless.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Limitless-GGUF/resolve/main/Limitless.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Limitless-GGUF/resolve/main/Limitless.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Limitless-GGUF/resolve/main/Limitless.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Limitless-GGUF/resolve/main/Limitless.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Limitless-GGUF/resolve/main/Limitless.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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.
<!-- end -->
|
mradermacher/Flammen-Bophades-7B-GGUF
|
mradermacher
| 2024-05-06T05:10:47Z | 1 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:nbeerbower/Flammen-Bophades-7B",
"base_model:quantized:nbeerbower/Flammen-Bophades-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-07T06:01:59Z |
---
base_model: nbeerbower/Flammen-Bophades-7B
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/nbeerbower/Flammen-Bophades-7B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/Flammen-Bophades-7B-GGUF/resolve/main/Flammen-Bophades-7B.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Flammen-Bophades-7B-GGUF/resolve/main/Flammen-Bophades-7B.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Flammen-Bophades-7B-GGUF/resolve/main/Flammen-Bophades-7B.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Flammen-Bophades-7B-GGUF/resolve/main/Flammen-Bophades-7B.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Flammen-Bophades-7B-GGUF/resolve/main/Flammen-Bophades-7B.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Flammen-Bophades-7B-GGUF/resolve/main/Flammen-Bophades-7B.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Flammen-Bophades-7B-GGUF/resolve/main/Flammen-Bophades-7B.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Flammen-Bophades-7B-GGUF/resolve/main/Flammen-Bophades-7B.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Flammen-Bophades-7B-GGUF/resolve/main/Flammen-Bophades-7B.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Flammen-Bophades-7B-GGUF/resolve/main/Flammen-Bophades-7B.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Flammen-Bophades-7B-GGUF/resolve/main/Flammen-Bophades-7B.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Flammen-Bophades-7B-GGUF/resolve/main/Flammen-Bophades-7B.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Flammen-Bophades-7B-GGUF/resolve/main/Flammen-Bophades-7B.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Flammen-Bophades-7B-GGUF/resolve/main/Flammen-Bophades-7B.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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.
<!-- end -->
|
mradermacher/WinterGoddess-1.4x-70b-32k-GGUF
|
mradermacher
| 2024-05-06T05:10:33Z | 41 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:ChuckMcSneed/WinterGoddess-1.4x-70b-32k",
"base_model:quantized:ChuckMcSneed/WinterGoddess-1.4x-70b-32k",
"license:llama2",
"endpoints_compatible",
"region:us"
] | null | 2024-04-07T08:19:24Z |
---
base_model: ChuckMcSneed/WinterGoddess-1.4x-70b-32k
language:
- en
library_name: transformers
license: llama2
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/ChuckMcSneed/WinterGoddess-1.4x-70b-32k
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-i1-GGUF
## 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/WinterGoddess-1.4x-70b-32k-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.Q2_K.gguf) | Q2_K | 25.6 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.IQ3_XS.gguf) | IQ3_XS | 28.4 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.IQ3_S.gguf) | IQ3_S | 30.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.Q3_K_S.gguf) | Q3_K_S | 30.0 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.IQ3_M.gguf) | IQ3_M | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.Q3_K_M.gguf) | Q3_K_M | 33.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.Q3_K_L.gguf) | Q3_K_L | 36.2 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.IQ4_XS.gguf) | IQ4_XS | 37.3 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.Q4_K_S.gguf) | Q4_K_S | 39.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.Q4_K_M.gguf) | Q4_K_M | 41.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.Q5_K_S.gguf) | Q5_K_S | 47.6 | |
| [GGUF](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.Q5_K_M.gguf) | Q5_K_M | 48.9 | |
| [PART 1](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.Q6_K.gguf.part2of2) | Q6_K | 56.7 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/WinterGoddess-1.4x-70b-32k-GGUF/resolve/main/WinterGoddess-1.4x-70b-32k.Q8_0.gguf.part2of2) | Q8_0 | 73.4 | 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
## 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.
<!-- end -->
|
mradermacher/Mestral-7B-GGUF
|
mradermacher
| 2024-05-06T05:10:26Z | 34 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:alkahestry/Mestral-7B",
"base_model:quantized:alkahestry/Mestral-7B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-07T09:56:57Z |
---
base_model: alkahestry/Mestral-7B
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/alkahestry/Mestral-7B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/Mestral-7B-GGUF/resolve/main/Mestral-7B.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/Mestral-7B-GGUF/resolve/main/Mestral-7B.IQ3_XS.gguf) | IQ3_XS | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mestral-7B-GGUF/resolve/main/Mestral-7B.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Mestral-7B-GGUF/resolve/main/Mestral-7B.IQ3_S.gguf) | IQ3_S | 3.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Mestral-7B-GGUF/resolve/main/Mestral-7B.IQ3_M.gguf) | IQ3_M | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Mestral-7B-GGUF/resolve/main/Mestral-7B.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Mestral-7B-GGUF/resolve/main/Mestral-7B.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Mestral-7B-GGUF/resolve/main/Mestral-7B.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Mestral-7B-GGUF/resolve/main/Mestral-7B.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mestral-7B-GGUF/resolve/main/Mestral-7B.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Mestral-7B-GGUF/resolve/main/Mestral-7B.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Mestral-7B-GGUF/resolve/main/Mestral-7B.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Mestral-7B-GGUF/resolve/main/Mestral-7B.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Mestral-7B-GGUF/resolve/main/Mestral-7B.Q8_0.gguf) | Q8_0 | 7.8 | 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
## 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.
<!-- end -->
|
mradermacher/ds-brew-13b-GGUF
|
mradermacher
| 2024-05-06T05:10:21Z | 21 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama",
"llama-2",
"en",
"base_model:Doctor-Shotgun/ds-brew-13b",
"base_model:quantized:Doctor-Shotgun/ds-brew-13b",
"license:agpl-3.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-07T11:18:47Z |
---
base_model: Doctor-Shotgun/ds-brew-13b
language:
- en
library_name: transformers
license: agpl-3.0
quantized_by: mradermacher
tags:
- llama
- llama-2
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Doctor-Shotgun/ds-brew-13b
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/ds-brew-13b-GGUF/resolve/main/ds-brew-13b.Q2_K.gguf) | Q2_K | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/ds-brew-13b-GGUF/resolve/main/ds-brew-13b.IQ3_XS.gguf) | IQ3_XS | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/ds-brew-13b-GGUF/resolve/main/ds-brew-13b.IQ3_S.gguf) | IQ3_S | 5.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/ds-brew-13b-GGUF/resolve/main/ds-brew-13b.Q3_K_S.gguf) | Q3_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/ds-brew-13b-GGUF/resolve/main/ds-brew-13b.IQ3_M.gguf) | IQ3_M | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/ds-brew-13b-GGUF/resolve/main/ds-brew-13b.Q3_K_M.gguf) | Q3_K_M | 6.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/ds-brew-13b-GGUF/resolve/main/ds-brew-13b.Q3_K_L.gguf) | Q3_K_L | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/ds-brew-13b-GGUF/resolve/main/ds-brew-13b.IQ4_XS.gguf) | IQ4_XS | 7.1 | |
| [GGUF](https://huggingface.co/mradermacher/ds-brew-13b-GGUF/resolve/main/ds-brew-13b.Q4_K_S.gguf) | Q4_K_S | 7.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ds-brew-13b-GGUF/resolve/main/ds-brew-13b.Q4_K_M.gguf) | Q4_K_M | 8.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ds-brew-13b-GGUF/resolve/main/ds-brew-13b.Q5_K_S.gguf) | Q5_K_S | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/ds-brew-13b-GGUF/resolve/main/ds-brew-13b.Q5_K_M.gguf) | Q5_K_M | 9.3 | |
| [GGUF](https://huggingface.co/mradermacher/ds-brew-13b-GGUF/resolve/main/ds-brew-13b.Q6_K.gguf) | Q6_K | 10.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/ds-brew-13b-GGUF/resolve/main/ds-brew-13b.Q8_0.gguf) | Q8_0 | 13.9 | 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
## 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.
<!-- end -->
|
mradermacher/lolicore-test-GGUF
|
mradermacher
| 2024-05-06T05:10:17Z | 35 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:Rorical/lolicore-test",
"base_model:quantized:Rorical/lolicore-test",
"endpoints_compatible",
"region:us"
] | null | 2024-04-07T11:49:16Z |
---
base_model: Rorical/lolicore-test
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Rorical/lolicore-test
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/lolicore-test-GGUF/resolve/main/lolicore-test.Q2_K.gguf) | Q2_K | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/lolicore-test-GGUF/resolve/main/lolicore-test.IQ3_XS.gguf) | IQ3_XS | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/lolicore-test-GGUF/resolve/main/lolicore-test.IQ3_S.gguf) | IQ3_S | 0.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/lolicore-test-GGUF/resolve/main/lolicore-test.Q3_K_S.gguf) | Q3_K_S | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/lolicore-test-GGUF/resolve/main/lolicore-test.IQ3_M.gguf) | IQ3_M | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/lolicore-test-GGUF/resolve/main/lolicore-test.Q3_K_M.gguf) | Q3_K_M | 0.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/lolicore-test-GGUF/resolve/main/lolicore-test.Q3_K_L.gguf) | Q3_K_L | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/lolicore-test-GGUF/resolve/main/lolicore-test.IQ4_XS.gguf) | IQ4_XS | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/lolicore-test-GGUF/resolve/main/lolicore-test.Q4_K_S.gguf) | Q4_K_S | 0.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/lolicore-test-GGUF/resolve/main/lolicore-test.Q4_K_M.gguf) | Q4_K_M | 0.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/lolicore-test-GGUF/resolve/main/lolicore-test.Q5_K_S.gguf) | Q5_K_S | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/lolicore-test-GGUF/resolve/main/lolicore-test.Q5_K_M.gguf) | Q5_K_M | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/lolicore-test-GGUF/resolve/main/lolicore-test.Q6_K.gguf) | Q6_K | 0.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/lolicore-test-GGUF/resolve/main/lolicore-test.Q8_0.gguf) | Q8_0 | 0.4 | 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
## 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.
<!-- end -->
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.
Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.